video data analysis education

Video Data Collection and Video Analyses in CSCL Research

• First Online: 09 October 2021

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• Carmen Zahn 8 ,
• Alessia Ruf 8 &
• Ricki Goldman 9  

Part of the book series: Computer-Supported Collaborative Learning Series ((CULS,volume 19))

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The purpose of this chapter is to examine significant advances in the collection and analysis of video data in computer-supported collaborative learning (CSCL) research. We demonstrate how video-based studies create robust and dynamic research processes. The chapter starts with an overview of how video analysis developed within CSCL by way of its pioneering roots. Linked throughout the chapter are the theoretical, methodological, and technological advances that keep advancing CSCL research. Specific empirical and experimental research examples will illustrate current and future advances in data collection, transformation, coding, and analysis. Research benefits and challenges that include the current state of understanding from observations of single, multiple, or 360° camera recordings will also be featured. In addition, eye-tracking and virtual reality environments for collecting and analyzing video data are discussed as they become new foci for future CSCL research.

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Further Readings

Goldman, R., Zahn, C., & Derry, S. (2014). Frontiers of digital video research in the learning sciences: Mapping the terrain. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd ed.). New York: Cambridge University Press.

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Zahn, C., Ruf, A., Goldman, R. (2021). Video Data Collection and Video Analyses in CSCL Research. In: Cress, U., Rosé, C., Wise, A.F., Oshima, J. (eds) International Handbook of Computer-Supported Collaborative Learning. Computer-Supported Collaborative Learning Series, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-030-65291-3_35

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Research as storytelling: the use of video for mixed methods research

• Erica B. Walker   ORCID: orcid.org/0000-0001-9258-3036 1 &
• D. Matthew Boyer 2  

Video Journal of Education and Pedagogy volume  3 , Article number:  8 ( 2018 ) Cite this article

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Mixed methods research commonly uses video as a tool for collecting data and capturing reflections from participants, but it is less common to use video as a means for disseminating results. However, video can be a powerful way to share research findings with a broad audience especially when combining the traditions of ethnography, documentary filmmaking, and storytelling.

Our literature review focused on aspects relating to video within mixed methods research that applied to the perspective presented within this paper: the history, affordances and constraints of using video in research, the application of video within mixed methods design, and the traditions of research as storytelling. We constructed a Mind Map of the current literature to reveal convergent and divergent themes and found that current research focuses on four main properties in regards to video: video as a tool for storytelling/research, properties of the camera/video itself, how video impacts the person/researcher, and methods by which the researcher/viewer consumes video. Through this process, we found that little has been written about how video could be used as a vehicle to present findings of a study.

From this contextual framework and through examples from our own research, we present current and potential roles of video storytelling in mixed methods research. With digital technologies, video can be used within the context of research not only as data and a tool for analysis, but also to present findings and results in an engaging way.

Conclusions

In conclusion, previous research has focused on using video as a tool for data collection and analysis, but there are emerging opportunities for video to play an increased role in mixed methods research as a tool for the presentation of findings. By leveraging storytelling techniques used in documentary film, while staying true to the analytical methods of the research design, researchers can use video to effectively communicate implications of their work to an audience beyond academics and use video storytelling to disseminate findings to the public.

Using motion pictures to support ethnographic research began in the late nineteenth century when both fields were early in their development (Henley, 2010 ; “Using Film in Ethnographic Field Research, - The University of Manchester,” n.d ). While technologies have changed dramatically since the 1890s, researchers are still employing visual media to support social science research. Photographic imagery and video footage can be integral aspects of data collection, analysis, and reporting research studies. As digital cameras have improved in quality, size, and affordability, digital video has become an increasingly useful tool for researchers to gather data, aid in analysis, and present results.

Storytelling, however, has been around much longer than either video or ethnographic research. Using narrative devices to convey a message visually was a staple in the theater of early civilizations and remains an effective tool for engaging an audience today. Within the medium of video, storytelling techniques are an essential part of a documentary filmmaker’s craft. Storytelling can also be a means for researchers to document and present their findings. In addition, multimedia outputs allow for interactions beyond traditional, static text (R. Goldman, 2007 ; Tobin & Hsueh, 2007 ). Digital video as a vehicle to share research findings builds on the affordances of film, ethnography, and storytelling to create new avenues for communicating research (Heath, Hindmarsh, & Luff, 2010 ).

In this study, we look at the current literature regarding the use of video in research and explore how digital video affordances can be applied in the collection and analysis of quantitative and qualitative human subject data. We also investigate how video storytelling can be used for presenting research results. This creates a frame for how data collection and analysis can be crafted to maximize the potential use of video data to create an audiovisual narrative as part of the final deliverables from a study. As researchers we ask the question: have we leveraged the use of video to communicate our work to its fullest potential? By understanding the role of video storytelling, we consider additional ways that video can be used to not only collect and analyze data, but also to present research findings to a broader audience through engaging video storytelling. The intent of this study is to develop a frame that improves our understanding of the theoretical foundations and practical applications of using video in data collection, analysis, and the presentation of research findings.

Literature review

The review of relevant literature includes important aspects for situating this exploration of video research methods: the history, affordances and constraints of using video in research, the use of video in mixed methods design, and the traditions of research as storytelling. Although this overview provides an extensive foundation for understanding video research methods, this is not intended to serve as a meta-analysis of all publications related to video and research methods. Examples of prior work provide a conceptual and operational context for the role of video in mixed methods research and present theoretical and practical insights for engaging in similar studies. Within this context, we examine ethical and logistical/procedural concerns that arise in the design and application of video research methods, as well as the affordances and constraints of integrating video. In the following sections, the frame provided by the literature is used to view practical examples of research using video.

The history of using video in research is founded first in photography and next in film followed more recently, by digital video. All three tools provide the ability to create instant artifacts of a moment or period of time. These artifacts become data that can be analyzed at a later date, perhaps in a different place and by a different audience, giving researchers the chance to intricately and repeatedly examine the archive of information contained within. These records “enable access to the fine details of conduct and interaction that are unavailable to more traditional social science methods” (Heath et al., 2010 , p. 2).

In social science research, video has been used for a range of purposes and accompanies research observation in many situations. For example, in classroom research, video is used to record a teacher in practice and then used as a guide and prompt to interview the teacher as they reflect upon their practice (e.g. Tobin & Hsueh, 2007 ). Video captures events from a situated perspective, providing a record that “resists, at least in the first instance, reduction to categories or codes, and thus preserves the original record for repeated scrutiny” (Heath et al., 2010 , p. 6). In analysis, these audio-visual recordings allow the social science researcher the chance to reflect on their subjectivities throughout analysis and use the video as a microscope that “allow(s) actions to be observed in a detail not even accessible to the actors themselves” (Knoblauch & Tuma, 2011 , p. 417).

Examining the affordances and constraints of video in research provides a researcher the opportunity to examine the value of including video within a study . An affordance of video, when used in research, is that it allows the researcher to see an event through the camera lens either actively or passively and later share what they have seen, or more specifically, the way they saw it (Chalfen, 2011 ). Cameras can be used to capture an event in three different modes: Responsive, Interactive, and Constructive. Responsive mode is reactive. In this mode, the researcher captures and shows the viewer what is going on in front of the lens but does not directly interfere with the participants or events. Interactive mode puts the filmmaker into the storyline as a participant and allows the viewer to observe the interactions between the researcher and participant. One example of video captured in Interactive mode is an interview. In Constructive mode, the researcher reprocesses the recorded events to create an explicitly interpretive final product through the process of editing the video (MacDougall, 2011 ). All of these modes, in some way, frame or constrain what is captured and consequently shared with the audience.

Due to the complexity of the classroom-research setting, everything that happens during a study cannot be captured using video, observation, or any other medium. Video footage, like observation, is necessarily selective and has been stripped of the full context of the events, but it does provide a more stable tool for reflection than the ever-changing memories of the researcher and participants (Roth, 2007 ). Decisions regarding inclusion and exclusion are made by the researcher throughout the entire research process from the initial framing of the footage to the final edit of the video. Members of the research team should acknowledge how personal bias impacts these decisions and make their choices clear in the research protocol to ensure inclusivity (Miller & Zhou, 2007 ).

One affordance of video research is that analysis of footage can actually disrupt the initial assumptions of a study. Analysis of video can be standardized or even mechanized by seeking out predetermined codes, but it can also disclose the subjective by revealing the meaning behind actions and not just the actions themselves (S. Goldman & McDermott, 2007 ; Knoblauch & Tuma, 2011 ). However, when using subjective analysis the researcher needs to keep in mind that the footage only reveals parts of an event. Ideally, a research team has a member who acts as both a researcher and a filmmaker. That team member can provide an important link between the full context of the event and the narrower viewpoint revealed through the captured footage during the analysis phase.

Although many participants are initially camera-shy, they often find enjoyment from participating in a study that includes video (Tobin & Hsueh, 2007 ). Video research provides an opportunity for participants to observe themselves and even share their experience with others through viewing and sharing the videos. With increased accessibility of video content online and the ease of sharing videos digitally, it is vital from an ethical and moral perspective that participants understand the study release forms and how their image and words might continue to be used and disseminated for years after the study is completed.

Including video in a research study creates both affordances and constraints regarding the dissemination of results. Finding a journal for a video-based study can be difficult. Traditional journals rely heavily on static text and graphics, but newly-created media journals include rich and engaging data such as video and interactive, web-based visualizations (Heath et al., 2010 ). In addition, videos can provide opportunities for research results to reach a broader audience outside of the traditional research audience through online channels such as YouTube and Vimeo.

Use of mixed methods with video data collection and analysis can complement the design-based, iterative nature of research that includes human participants. Design-based video research allows for both qualitative and quantitative collection and analysis of data throughout the project, as various events are encapsulated for specific examination as well as analyzed comparatively for changes over time. Design research, in general, provides the structure for implementing work in practice and iterative refinement of design towards achieving research goals (Collins, Joseph, & Bielaczyc, 2004 ). Using an integrated mixed method design that cycles through qualitative and quantitative analyses as the project progresses gives researchers the opportunity to observe trends and patterns in qualitative data and quantitative frequencies as each round of analysis informs additional insights (Gliner et al., 2009 ). This integrated use also provides a structure for evaluating project fidelity in an ongoing basis through a range of data points and findings from analyses that are consistent across the project. The ability to revise procedures for data collection, systematic analysis, and presenting work does not change the data being collected, but gives researchers the opportunity to optimize procedural aspects throughout the process.

Research as storytelling refers to the narrative traditions that underpin the use of video methods to analyze in a chronological context and present findings in a story-like timeline. These traditions are evident in ethnographic research methods that journal lived experiences through a period of time and in portraiture methods that use both aesthetic and scientific language to construct a portrait (Barone & Eisner, 2012 ; Heider, 2009 ; Lawrence-Lightfoot, 2005 ; Lenette, Cox & Brough, 2013 ).

In existing research, there is also attention given to the use of film and video documentaries as sources of data (e.g. Chattoo & Das, 2014 ; Warmington, van Gorp & Grosvenor, 2011 ), however, our discussion here focuses on using media to capture information and communicate resulting narratives for research purposes. In our work, we promote a perspective on emergent storytelling that develops from data collection and analysis, allowing the research to drive the narrative, and situating it in the context from where data was collected. We rely on theories and practices of research and storytelling that leverage the affordances of participant observation and interview for the construction of narratives (Bailey & Tilley, 2002 ; de Carteret, 2008 ; de Jager, Fogarty & Tewson, 2017 ; Gallagher, 2011 ; Hancox, 2017 ; LeBaron, Jarzabkowski, Pratt & Fetzer, 2017 ; Lewis, 2011 ; Meadows, 2003 ).

The type of storytelling used with research is distinctly different from methods used with documentaries, primarily with the distinction that, while documentary filmmakers can edit their film to a predetermined narrative, research storytelling requires that the data be analyzed and reported within a different set of ethical standards (Dahlstrom, 2014 ; Koehler, 2012 ; Nichols, 2010 ). Although documentary and research storytelling use a similar audiovisual medium, creating a story for research purposes is ethically-bounded by expectations in social science communities for being trustworthy in reporting and analyzing data, especially related to human subjects. Given that researchers using video may not know what footage will be useful for future storytelling, they may need to design their data collection methods to allow for an abundance of video data, which can impact analysis timelines as well. We believe it important to note these differences in the construction of related types of stories to make overt the essential need for research to consider not only analysis but also creation of the reporting narrative when designing and implementing data collection methods.

This study uses existing literature as a frame for understanding and implementing video research methods, then employs this frame as perspective on our own work, illuminating issues related to the use of video in research. In particular, we focus on using video research storytelling techniques to design, implement, and communicate the findings of a research study, providing examples from Dr. Erica Walker’s professional experience as a documentary filmmaker as well as evidence from current and former academic studies. The intent is to improve understanding of the theoretical foundations and practical applications for video research methods and better define how those apply to the construction of story-based video output of research findings.

The study began with a systematic analysis of theories and practices, using interpretive analytic methods, with thematic coding of evidence for conceptual and operational aspects of designing and implementing video research methods. From this information, a frame was constructed that includes foundational aspects of using digital video in research as well as the practical aspects of using video to create narratives with the intent of presenting research findings. We used this frame to interpret aspects of our own video research, identifying evidence that exemplifies aspects of the frame we used.

A primary goal for the analysis of existing literature was to focus on evidentiary data that could provide examples that illuminate the concepts that underpin the understanding of how, when, and why video research methods are useful for a range of publishing and dissemination of transferable knowledge from research. This emphasis on communicating results in both theoretical and practical ways highlighted areas within the analysis for potential contextual similarities between our work and other projects. A central reason for interpreting findings and connecting them with evidence was the need to provide examples that could serve as potentially transferable findings for others using video with their research. Given the need for a fertile environment (Zhao & Frank, 2003 ) and attention to contextual differences to avoid lethal mutations (Brown & Campione, 1996 ), understand that these examples may not work for every situation, but the intent is to provide clear evidence of how video research methods can leverage storytelling to report research findings in a way that is consumable by a broader audience.

In the following section, we present findings from the review of research and practice, along with evidence from our work with video research, connecting the conceptual and operational frame to examples and teasing out aspects from existing literature.

Results and findings

When looking at the current literature regarding the use of video in research, we developed a Mind Map to categorize convergent and divergent themes in the current literature, see Fig.  1 . Although this is far from a complete meta-analysis on video research (notably absent is a comprehensive discussion of ethical concerns regarding video research), the Mind Map focuses on four main properties in regards to video: video as a tool for storytelling/research, properties of the camera/video itself, how video impacts the person/researcher, and methods by which the researcher/viewer consumes video.

Mind Map of current literature regarding the use of video in mixed methods research. Link to the fully interactive Mind Map- http://clemsongc.com/ebwalker/mindmap/

Video, when used as a tool for research, can document and share ethnographic, epistemic, and storytelling data to participants and to the research team (R. Goldman, 2007 ; Heath et al., 2010 ; Miller & Zhou, 2007 ; Tobin & Hsueh, 2007 ). Much of the research in this area focuses on the properties (both positive and negative) inherent in the camera itself such as how video footage can increase the ability to see and experience the world, but can also act as a selective lens that separates an event from its natural context (S. Goldman & McDermott, 2007 ; Jewitt, n.d .; Knoblauch & Tuma, 2011 ; MacDougall, 2011 ; Miller & Zhou, 2007 ; Roth, 2007 ; Sossi, 2013 ).

Some research speaks to the role of the video-researcher within the context of the study, likening a video researcher to a participant-observer in ethnographic research (Derry, 2007 ; Roth, 2007 ; Sossi, 2013 ). The final category of research within the Mind Map focuses on the process of converting the video from an observation to records to artifact to dataset to pattern (Barron, 2007 ; R. Goldman, 2007 ; Knoblauch & Tuma, 2011 ; Newbury, 2011 ). Through this process of conversion, the video footage itself becomes an integral part of both the data and findings.

The focus throughout current literature was on video as data and the role it plays in collection and analysis during a study, but little has been written about how video could be used as a vehicle to present findings of a study. Current literature also did not address whether video-data could be used as a tool to communicate the findings of the research to a broader audience.

In a recent two-year study, the research team led by Dr. Erica Walker collected several types of video footage with the embedded intent to use video as both data and for telling the story of the study and findings once concluded (Walker, 2016 ). The study focused on a multidisciplinary team that converted a higher education Engineering course from lecture-based to game-based learning using the Cognitive Apprenticeship educational framework. The research questions examined the impact that the intervention had on student learning of domain content and twenty-first Century Skills. Utilizing video as both a data source and a delivery method was built into the methodology from the beginning. Therefore, interviews were conducted with the researchers and instructors before, during, and after the study to document consistency and changes in thoughts and observations as the study progressed. At the conclusion of the study, student participants reflected on their experience directly through individual video interviews. In addition, every class was documented using two static cameras, placed at different angles and framing, and a mobile camera unit to capture closeup shots of student-instructor, student-student, and student-content interactions. This resulted in more than six-hundred minutes of interview footage and over five-thousand minutes of classroom footage collected for the study.

Video data can be analyzed through quantitative methods (frequencies and word maps) as well as qualitative methods (emergent coding and commonalities versus outliers). Ideally, both methods are used in tandem so that preliminary results can continue to inform the overall analysis as it progresses. In order to capitalize on both methods, each interview was transcribed. The researchers leveraged digital and analog methods of coding such as digital word-search alongside hand coding the printed transcripts. Transcriptions contained timecode notations throughout, so coded segments could quickly be located in the footage and added to a timeline creating preliminary edits.

There are many software workflows that allow researchers to code, notate timecode for analysis, and pre-edit footage. In the study, Opportunities for Innovation: Game-based Learning in an Engineering Senior Design Course, NVivo qualitative analysis software was used together with paper-based analog coding. In a current study, also based on a higher education curriculum intervention, we are digitally coding and pre-trimming the footage in Adobe Prelude in addition to analog coding on the printed transcripts. Both workflows offer advantages. NVivo has built-in tools to create frequency maps and export graphs and charts relevant to qualitative analysis whereas Adobe Prelude adds coding notes directly into the footage metadata and connects directly with Adobe Premiere video editing software, which streamlines the editing process.

From our experience with both workflows, Prelude works better for a research team that has multiple team members with more video experience because it aligns with video industry workflows, implements tools that filmmakers already use, and Adobe Team Projects allows for co-editing and coding from multiple off-site locations. On the other hand, NVivo works better for research teams where members have more separate roles. NVivo is a common qualitative-analysis software so team members more familiar with traditional qualitative research can focus on coding and those more familiar with video editing can edit based on those codes allowing each team member to work within more familiar software workflows.

In both of these studies, assessments regarding storytelling occurred in conjunction with data processing and analysis. As findings were revealed, appropriate clips were grouped into timelines and edited to produce a library of short, topic-driven videos posted online , see Fig.  2 . A collection of story-based, topic-driven videos can provide other practitioners and researchers a first-hand account of how a study was designed and conducted, what worked well, recommendations of what to do differently, participant perspectives, study findings, and suggestions for further research. In fact, the videos cover many of the same topics traditionally found in publications, but in a collection of short videos accessible to a broad audience online.

The YouTube channel created for Opportunities for Innovation: Game-based Learning in an Engineering Senior Design Course containing twenty-four short topical videos. Direct link- https://goo.gl/p8CBGG

By sharing the results of the study publicly online, conversations between practitioners and researchers can develop on a public stage. Research videos are easy to share across social media channels which can broaden the academic audience and potentially open doors for future research collaborations. As more journals move to accept multi-media studies, publicly posted videos provide additional ways to expose both academics and the general public to important study results and create easy access to related resources.

Video research as storytelling: The intersection and divergence of documentary filmmaking and video research

“Film and writing are such different modes of communication, filmmaking is not just a way of communicating the same kinds of knowledge that can be conveyed by an anthropological text. It is a way of creating different knowledge” (MacDougall, 2011 ).

When presenting research, choosing either mode of communication comes with affordances and constraints for the researcher, the participants, and the potential audience.

Many elements of documentary filmmaking, but not all, are relevant and appropriate when applied to gathering data and presenting results in video research. Documentary filmmakers have a specific angle on a story that they want to share with a broad audience. In many cases, they hope to incite action in viewers as a response to the story that unfolds on screen. In order to further their message, documentarians carefully consider the camera shots and interview clips that will convey the story clearly in a similar way to filmmakers in narrative genres. Decisions regarding what to capture and how to use the footage happen throughout the entire filmmaking process: prior to shooting footage (pre-production), while capturing footage (production), and during the editing phase (post-production).

Video researchers can employ many of the same technical skills from documentary filmmaking including interview techniques such as pre-written questions; camera skills such as framing, exposure, and lighting; and editing techniques that help draw a viewer through the storyline (Erickson, 2007 ; Tobin & Hsueh, 2007 ). In both documentary filmmaking and in video research, informed decisions are made about what footage to capture and how to employ editing techniques to produce a compelling final video.

Where video research diverges from documentary filmmaking is in how the researcher thinks about, captures, and processes the footage. Video researchers collect video as data in a more exploratory way whereas documentary filmmakers often look to capture preconceived video that will enable them to tell a specific story. For a documentary filmmaker, certain shots and interview responses are immediately discarded as they do not fit the intended narrative. For video researchers, all the video that is captured throughout a study is data and potentially part of the final research narrative. It is during the editing process (post-production) where the distinction between data and narrative becomes clear.

During post-production, video researchers are looking for clips that clearly reflect the emergent storylines seen in the collective data pool rather than the footage necessary to tell a predetermined story. Emergent storylines can be identified in several ways. Researchers look for divergent statements (where an interview subject makes unique observation different from other interviewees), convergent statements (where many different interviewees respond similarly), and unexpected statements (where something different from what was expected is revealed) (Knoblauch & Tuma, 2011 ).

When used thoughtfully, video research provides many sources of rich data. Examples include reflections of the experience, in the direct words of participants, that contain insights provided by body language and tone, an immersive glimpse into the research world as it unfolds, and the potential to capture footage throughout the entire research process rather than just during prescribed times. Video research becomes especially powerful when combined with qualitative and quantitative data from other sources because it can help reveal the context surrounding insights discovered during analysis.

We are not suggesting that video researchers should become documentary filmmakers, but researchers can learn from the stylistic approaches employed in documentary filmmaking. Video researchers implementing these tools can leverage the strengths of short-format video as a storytelling device to share findings with a more diverse audience, increase audience understanding and consumption of findings, and encourage a broader conversation around the research findings.

Implications for future work

As the development of digital media technologies continues to progress, we can expect new functionalities far exceeding current tools. These advancements will continue to expand opportunities for creating and sharing stories through video. By considering the role of video from the first stages of designing a study, researchers can employ methods that capitalize on these emerging technologies. Although they are still rapidly advancing, researchers can look for ways that augmented reality and virtual reality could change data analysis and reporting of research findings. Another emergent area is the use of machine learning and artificial intelligence to rapidly process video footage based on automated thematic coding. Continued advancements in this area could enable researchers to quickly quantify data points in large quantities of footage.

In addition to exploring new functionalities, researchers can still use current tools more effectively for capturing data, supporting analysis, and reporting findings. Mobile devices provide ready access to collect periodic video reflections from study participants and even create research vlogs (video blogs) to document and share ongoing studies as they progress. In addition, participant-created videos are rich artifacts for evaluating technical and conceptual knowledge as well as affective responses. Most importantly, as a community, researchers, designers, and documentarians can continue to take strengths from each field to further the reach of important research findings into the public sphere.

In conclusion, current research is focused on using video as a tool for data collection and analysis, but there are new, emerging opportunities for video to play an increased and diversified role in mixed methods research, especially as a tool for the presentation and consumption of findings. By leveraging the storytelling techniques used in documentary filmmaking, while staying true to the analytical methods of research design, researchers can use video to effectively communicate implications of their work to an audience beyond academia and leverage video storytelling to disseminate findings to the public.

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Walker, E.B., Boyer, D.M. Research as storytelling: the use of video for mixed methods research. Video J. of Educ. and Pedagogy 3 , 8 (2018). https://doi.org/10.1186/s40990-018-0020-4

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• Mixed methods
• Storytelling
• Video research

THE VDA FRAMEWORK

We increasingly record social life – be it on mobile phones, CCTV, drone cameras, or body-worn cameras. As the availability of video data continues to proliferate in societies across the world, the relevance of visual data for researchers increases in kind. Video Data Analysis (VDA) facilitates the use of 21st century video in social science research.

What is VDA?

VDA is a curated multi-disciplinary collection of tools, techniques, and quality criteria intended for analyzing the content of visuals to study driving dynamics of social behavior and events ‘in the wild.’ It often uses visual data in combination with other data types.

At its core, Video Data Analysis (VDA) focuses on the content of recordings to study situational dynamics in social processes and outcomes. How do teams col­laborate successfully in organizations? How do families interact with their toddlers and how does this shape learning? How do victory celebrations in sports change over time? What does racial discrimination in the housing market look like on the level of interactions? In more fundamental terms, VDA aims to facilitate studying how patterns of behaviors, actions, and interactions shape the fabric of social life, and how they impact, and are impacted by, structural factors. VDA uses videos, other (audio-)visual data, and complementary non-video data to analyze the driving dynamics of such processes and outcomes at the micro level.

The main source of data in Video Data Analysis (VDA) studies are ready-made or custom-made videos that capture events or situations in real life (i.e., outside laboratory settings). Ready-made data are produced as by-products of non-academic activities. Custom-made data are collected by researchers for the purpose of a given study. Both have unique strengths and drawbacks for VDA, but both have a number of unique affordances: They comprise direct and detailed observations of driving dynamics at the micro-level during real-life social processes and events, are easy to re-watch and share with colleagues and readers, and often enable highly efficient data collection.

How 21st century video data reshapes social science research

VDA was developed in response to recent social and technological shifts. In 2011, anti-government protests and uprisings erupted in Northern Africa and the Middle East in what is often called the ‘Arab Spring’. For the first time, smartphone recordings and social media posts played a crucial role in the spread of protests and their global visibility. Thousands of citizens filmed the protests with their phones, often capturing the same instance from multiple camera angles. Especially Egypt’s self-proclaimed ‘Facebook generation’ uploaded large amounts of these videos on social media platforms. As a result, everyone could see first-hand footage of the protests, taken from the perspec­tive of those involved in the marches. Major media outlets began to incorporate such videos in their investigation and coverage to find out and convey what happened on the ground. As one journalist describes: ‘In most cases citizens capture the breaking news moments first. The Arab spring was really the tipping point when it all came together’ (Batty 2011). 

Since then, video footage from smartphones, surveillance cam­eras, and other non-newsmedia sources form a natural part of reporting on and our perception of uprisings and civil war, from the 2021 Storm of the US Capitol to the war in Ukraine and many other large-scale events. Ubiquitous video cameras also capture rare events such as mass panics and natural disasters, including the panic at a 2010 music festival in Germany that left 21 people dead and 652 injured. Thousands of videos on social media sites show behavior and interactions during floods and earthquakes. On May 25, 2020, the brutal murder of George Floyd was filmed by bystanders‘ mobile phones, CCTV cameras, and officers’ body-worn cameras. 

But not only the extraordinary, dramatic, or terrible is being captured. People use smartphones to document more common events, such as weddings and funerals, or to capture mundane events, such as playing video games or spending time with friends. CCTV cameras capture everyday behavior in pub­lic places, on school yards, and even in workplaces. Livestreams capture press conferences, business meetings, trials, and a wide array of other events and occasions. And with the Covid-19 pandemic, we saw large parts of social life move to video chat platforms; job talks, break­ups, marriage proposals, kindergarten kids drawing and chatting; it was all filmed as we lived our lives from a distance. We saw the creative ways in which teachers engaged their students through online platforms, and the hot mess that was the Handforth Parish Council meeting, a UK regional government body meeting that was conducted via Zoom and went viral for its utterly cha­otic opening sequence.  

Parallel to this increase in third-hand video data, advances in camera and data storage technology also enabled new ways of collecting first-hand videos for research, by researchers. In short, we find ourselves in a new era of how social life is captured.

For social scientists, who are inherently interested in how social life ‘works,’ these videos can generate completely new insights. Over the past decade, using 21st-century video data has allowed researchers across sociology, crimi­nology communication studies, anthropology, psychology, education research, political science, and other disciplines to make groundbreaking discoveries. Researchers can now use video data to find out how we talk to each other, how we express emotions, how we fight, or how we learn. We can use video to study what successful workplace meetings look like, how family interactions shape pre-school learning, or how people  do  racial discrimination in educa­tion or in the housing market. Thanks to video data, we can look at situations step-by-step and frame-by-frame to understand sequences, interaction rou­tines, communication patterns, social hierarchies, or other aspects of culture and social life. Moreover, through video data, we have access to permanent first-hand recordings of situations and events that we did not necessarily observe ourselves. We can look at the same situation over and over again, as a team or alone.

Using these new sources of video data and building on established theoretical perspectives and methodological approaches, new lines of inquiry have emerged across the social sciences, in which researchers use video data to study situational driving dynamics of social processes and events. Situational dynamics refer to things that happen when people interact with one another, while being in temporal proximity and physical or mediated co-presence. Recent Studies use video data to seek to understand the situational dynamics of real-life events and processes by studying human behavior, interactions, and emotions in situations, and how they impact social outcomes (for a list of recent publications in the field, click  here ). From these developments, Video Data Analysis has emerged as a methodological framework in an effort to systematize methodological development, formulate open questions and solutions, and increase inter-disciplinary dialogue. 

VDA use cases

Studies of what happens “on the ground” during events or situations can take three perspectives (for details, see Nassauer and Legewie 2022): seeing such micro-level processes as consequences or manifestations of “structural” or “macro” phenomena (e.g., the education system); seeing micro-level processes as phenomena in their own right (e.g., to understand how we maintain order in everyday life); or perceiving micro-level processes as driving forces behind macro phenomena (e.g., micro-level processes may produce differences in the frequency of officer-involved shootings in the United States).

One of the most prominent among recent applications in sociology is Collins’ (2008) analysis of pictures and videos to study emotional dynamics in a variety of violent and near-violent situations. Collins focuses on the minutes and seconds before and during violent behaviors and identifies emotions in actors’ facial muscles and body postures. This approach allows Collins to challenge core assumptions of conventional theories of violence, by showing that situational emotions, instead of actors’ prior strategies or motivations, trigger violent behaviors. Visual data are instrumental in enabling Collins to develop his argument and corroborate his findings. His study suggests that “there is crucial causality at the micro-level” (Collins 2016), which can be uncovered using visual data. Further examples from studies on deviant behavior include analyses of massacres (Klusemann 2009) and protest violence (Nassauer 2019). Criminologists increasingly use visual data to study crime as it unfolds (Lindegaard and Bernasco 2018; Stickle et al. 2020). In the field of policing (McCluskey et al. 2019; Sytsma, Chillar, and Piza 2021), videos can capture how real-life police-citizen encounters unfold and thereby contribute to our understanding of policework as part of social order, the state monopoly of force, procedural justice, and police use of force. In law studies, scholars have used video data to, among other things, better understand the jury deliberation process (Diamond et al. 2003) and successful interrogation techniques (Alison et al. 2013). In business and organization studies, scholars have researched behavior and team work in emergency call centers (Fele 2008; Mondada 2008) or nuclear power plant control rooms (Waller, Gupta, and Giambatista 2004). In primary care research, videos have been used to study topics such as staff-patient interactions in nursing centers (Caldwell and Atwal 2005), training and simulation exercises of health care workers (Hunziker et al. 2011), and cooperation in anesthesia teams (Burtscher et al. 2010; for an overview, see Heath, Hindmarsh, and Luff 2010:8ff; Asan and Montague 2014). Research in education, too, has a tradition of studying visuals to examine non-verbal and verbal aspects of social interaction, and videos are now a prominent tool in the field (Derry et al. 2010:4). Examples include classroom interactions (Andersson and Sørvik 2013) and peacemaking among children (Verbeek 2008). In political science, videos have been used to study the impact of candidate debates on audiences’ voting behavior (Brierley, Kramon, and Ofosu 2020) and cooperation and outgroup perception (Chang and Peisakhin 2019; Choi, Poertner, and Sambanis 2019). You find links to these and many other VDA studies in the  research articles section .

As the examples illustrate, in Video Data Analysis researchers use video data to find out how we talk to each other, how we express emotions, how we fight, or how we learn. We can use video to study what successful workplace meetings look like, how family interactions shape pre-school learning, or how people do racial discrimination in education or in the housing market. Thanks to video data, we can look at situations step-by-step and frame-by-frame to understand sequences, interaction routines, communication patterns, social hierarchies, or other aspects of culture and social life. Moreover, through video data, we have access to permanent first-hand recordings of situations and events that we did not necessarily observe ourselves. We can look at the same situation over and over again, as a team or alone.

Social life is now ubiquitously captured on camera. For researchers interested in social life, these recordings offer a new world of insights. Paraphrasing Carl Sagan, we are still at the shore of an ocean of possibilities that video data offer. Over the next decades these possibilities will benefit scientific research and our understanding of human behavior.

How to use VDA: ANALYTICal DIMENSIONS AND PROCEDURES

Analytical dimensions refer to the content of visual data that are of interest when analyzing situations: facial expressions and body posture, interactions, and context. Facial expressions and body postures are any nonverbal information that a person’s face and body convey. Interactions refer to anything people do or say that is geared toward or affects their environment or people within. Context means information on the physical and social setting of a situation. These dimensions should be understood as lenses that help deriving information from visual recordings and that might help to understand situational dynamics, provided they draw on a thorough theoretical reflection and employ clear, detailed coding schemes.

VDA can be used in indicative and deductive approaches, qualitative in-depth and quantitative large-N, or even computational analyses.

Although these approaches differ in many ways, VDA approaches are united by a number of analytical procedures. First, coding of video data plays a central role in analysis. Coding means to tag a section of data with labels that synthesize content as relevant to a given research project. Some researchers conduct coding in their analysis with­out using the term itself, and studies differ in whether they develop a coding scheme first, then code data (a deductive approach), or whether they use an iterative approach of data collection, coding, and analysis (an inductive or abductive approach). Still, all types of qualitative and quantitative analysis include some type of data coding in order to make sense of it and identify patterns.

Second, the above figure shows six analytic lenses (for more information, see Ch. 6 of our  book ), which move researchers from labeling the data to identifying and interpreting patterns or driving dynamics: counts and quantifications, timing and sequence, rhythm and turn-taking, actors, networks and relations, and spacing. These procedures can help in analyzing video data, regardless of whether the aim is to describe patterns at the micro level, or to study causal links within situations or events. The six procedures all build on coding of the data (indicated by the black bi-directional arrows), and they are all interconnected (indicated by the grey lines). For instance, one could pro­duce counts and quantifications based on video data that help studying social relations and networks. In other words, the six procedures should not be understood as discrete analytical steps or mutually exclusive ways to analyze video data. Rather, they are a non-exhaustive toolbox from which researchers can pick any combination of tools that work well for what they try to accomplish in their VDA. 

As the above figure suggests, researchers can also use additional analytical tools together with VDA to produce interesting findings. These can be anything from regression analyses to sequence analysis and other approaches concerned with sequential patterns, to simulations, to compara­tive configurational methods, or any other approach that allows gleaning additional insights from the analyzed video data. Depending on how one sets up the research in terms of theoretical perspective, object of study, and analytical foci, it may or may not make sense to use one of these tools together with analy­sis of your video data.

QUALITY CRITERIA

Criteria for validity include neutral or balanced data sources, optimal capture, and natural behavior. Neutral or balanced data sources should not reflect an adherence to particular interests that could lead to the concordant publication or provision of access to biased data; if sources that demonstrate a propensity for specific interests are used, researchers should seek to triangulate various sources representing divergent interests. Optimal capture means visual data should cover the duration of a situation or event, its space, and all actors involved. Natural behavior refers to an actor’s unaltered behavior in a given situation, that is, the researcher should consider the degree to which actors recorded in visual data behave the same way that they would have otherwise behaved, were a camera not present.

RESEARCH ETHICS

Research ethics concern the application of ethical principles to the research process as a reflection of moral rules and values, with core goals being protecting participants from harm, avoiding conflict of interest and misrepresentation, respecting common laws, and adhering to standards such as professional competence and nondiscrimination. These provide the overall frame of reference for reflections on online video research. In the context of VDA, five ethical areas are specifically relevant: (1) informed consent; (2) privacy; (3) unique opportunities; (4) potential harm; and (5) transparency.

Informed consent means that people should know that they are being researched, receive relevant information on the planned research in a comprehensible format, and should then voluntarily agree to participate, or decline to do so. When assessing issues of informed consent, we suggest asking three questions of the data: Does the space filmed require informed consent? If not: Was the focus on the space or on people? And if the space filmed requires consent: did people give consent?

Privacy means respect people’s private information and anonymity, both of which are complex concepts that need to be re-evaluated for online contexts. To assess issues of privacy in livestreams and online video research, we suggest reflecting on the online and situational context in which the footage was found and taken, and the content of the video.

Unique opportunities focuses on the potential benefits of a study. We suggest asking: does a study offer unique potential for scientific insights and/or real-life benefits, and could other data could replace the video fully or in parts of the analysis?

Potential harm refers to possible ways in which a study may hurt the study subjects, researchers, or third parties. Since one of the main goals in research ethics is to minimize harm, a rigorous assessment of potential harm is essential for any study. We suggest three for assessing potential harm specific to video research: What kind of behavior is depicted? Could data harm people or groups depicted? How publicly available is the video prior to research?

Finally, transparency refers to making goals, procedures, and data as accessible to the public as possible, thereby improving traceability and openness of scientific processes and findings. We suggest four questions to assess whether a given study can actualize the immense potential for transparency inherent in video research: can permanent access to the video be assured? Can the researcher share the data with reviewers? Can the researcher share the data with the broader research community? And can the researcher share the data during talks at conferences or workshop?

These ethical areas and principles always have to be evaluated in relation to each other and weighed against each other in the context of a specific study. Of course, an outcome of such an assessment may very well be that a study is too unethical to be implemented, or needs substantial revisions.

When not to USE VDA

VDA is not suited for all types of research questions and theoretical approaches and, like all methodological approaches, it entails limitations and challenges. First, the type of data used by VDA implies limited access to video recordings from private events, such as funerals in Western societies. Second, VDA does not offer the tacit knowledge and immersion in a social context that comes with continuous direct participant observation, and it does not offer the same potential as ethnography for studying the cultural knowledge or narratives of a specific community or group of people. Third, interpretation of certain elements, such as gestures, may be context dependent, making VDA less suitable to study social contexts that a researcher is unfamiliar with. Fourth, a number of research ethics questions remain unclear with the new types of video data VDA often employs; e.g., what types of video from which platforms are admissible to use as research data.

Header image copyright: IFacility CCTV Cameras , by RickySpanish, licensed under CC BY-SA 4.0

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Video Research in the Learning Sciences

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Video Research in the Learning Sciences is a comprehensive exploration of key theoretical, methodological, and technological advances concerning uses of digital video-as-data in the learning sciences as a way of knowing about learning, teaching, and educational processes. The aim of the contributors, a community of scholars using video in their own work, is to help usher in video scholarship and supportive technologies, and to mentor video scholars, so that video research will meet its maximum potential to contribute to the growing knowledge base about teaching and learning. This volume contributes deeply to both to the science of learning through in-depth video studies of human interaction in learning environments—whether classrooms or other contexts—and to the uses of video for creating descriptive, explanatory, or expository accounts of learning and teaching. It is designed around four themes—each with a cornerstone chapter that introduces and synthesizes the cluster of chapters related to it: Theoretical frameworks for video research; Video research on peer, family, and informal learning; Video research on classroom and teacher learning; and Video collaboratories and technological futures. Video Research in the Learning Sciences is intended for researchers, university faculty, teacher educators, and graduate students in education, and for anyone interested in how knowledge is expanded using video-based technologies for inquiries about learning and teaching. Visit the Web site affiliated with this book: www.videoresearch.org

Table of Contents

Roy Pea, Ricki Goldman, Brigid Barron, Sharon J. Derry

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"...this comprehensive book is a testimony to video popularity in the USA and should be a recommended text in educational technology courses—it hasmuch to offer in terms of ideas and innovation." -- Sanjaya Mishra, British Journal of Technology , Vol 39 No 5 2008

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Qualitative Analysis of Video Data: Standards and Heuristics

Video research is an increasingly important method in the learning sciences. Video provides unique analytical affordances to researchers but also presents unique tensions, many of which have not yet been adequately addressed in the literature. The authors of this symposium draw on their diverse experiences, analyzing a variety of video corpuses, to provide theoretical and methodological standards and heuristics for the process of video analysis. We focus on three themes central to the process of video analysis that would benefit from increased theoretical and methodological attention: transcription tensions, defining the unit of analysis, and representing context. We discuss how our approaches to video analysis are framed by theory and how we have applied them to specific datasets, to answer a variety of research questions. In doing so, we make explicit some crosscutting methodological norms and invite continued discussion about these norms from multiple analytic traditions.

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• Published: 26 September 2021

360-degree video in education: An overview and a comparative social media data analysis of the last decade

• Georgios Lampropoulos   ORCID: orcid.org/0000-0002-5719-2125 1 , 2 ,
• Vassilis Barkoukis   ORCID: orcid.org/0000-0002-7335-6062 3 ,
• Kevin Burden   ORCID: orcid.org/0000-0003-0488-3202 4 &
• Theofylaktos Anastasiadis   ORCID: orcid.org/0000-0001-9064-2637 3  

Smart Learning Environments volume  8 , Article number:  20 ( 2021 ) Cite this article

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Due to its immersive and interactive nature, 360-degree video is becoming ever more popular. However, little is known about the public’s viewpoints and attitudes regarding the use of this emerging technology in educational contexts. This study reviews the research evidence for 360° video, virtual learning environments and social media and presents related studies. Moreover, the methodology, the tools and the analysis process used to comparatively analyze social media data are presented and the results that arose are showcased. The use of 360° video in education is discussed and directions for future research are given. Based on the data from the last 10 years, the main findings of the study show that 360° video and its use in educational settings are recognized positively by the public who mostly express anticipation, trust and joy when referring to it. Nonetheless, teachers are unfamiliar with 360° video and do not have the necessary technical skills to develop educational material using it or incorporating it into teaching activities. Finally, 360° video is proven to be an effective educational tool which satisfies the emerging educational needs, enriches the teaching and learning process and promotes students’ motivation, active participation and engagement, rendering, thus, learning more effective.

Introduction

As it was aptly described over a decade ago, today’s students are no longer the people our educational system was designed to teach (Prensky, 2001 ). Therefore, education methods should be adapted in such a way as to accommodate these evolving and varying learning styles and emerging educational needs and requirements (DiLullo et al., 2011 ). In order to improve the quality of education to meet these new requirements and adjust to the new conditions, Information and Communication Technologies (ICT) have been integrated into education and technology-enhanced learning methods have been implemented (Alexander et al., 2019 ; Henard and Roseveare, 2012 ). Moreover, technology-enhanced learning has become a fundamental part of 21st century education as it allows easy and rapid access to information and services and renders the creation of immersive virtual learning environments feasible. Based on a study conducted by European University Association, 91% of European Higher Education institutes have already delivered blended and distance learning courses (Gaebel et al., 2014 ).

Furthermore, the recent advances in ICT and Extended Reality (XR) technologies as well as the emphasis on pedagogy in student-centered learning have paved the way for using immersive technologies in educational activities. XR technologies involve immersive technologies such as augmented reality and virtual reality. Virtual reality refers to environments or realities created by computers designed to simulate a person’s physical presence in specific real or artificial environments which perceptually surround users and are presented in such a way that they can be regarded to be true. It is differentiated from augmented reality which refers to technological applications of computer units that enrich and enhance users’ physical environment with additional information and virtual objects in real time (Lampropoulos et al., 2020 ). In order to create highly interactive and immersive educational environments and experiences, the use of 360° video, augmented reality and virtual reality are becoming more and more popular as they enable users to directly experience and interact with virtual contents and environments. This immersive nature blends well with existing educational pedagogies such as those rooted in constructivist ideals since they facilitate communication, interaction, collaboration and enhance engagement (Huang et al., 2010 ). Dede ( 2009 , p. 66) defined immersiveness as “the subjective impression that one is participating in a comprehensive, realistic experience” and in a subsequent study he highlighted the potentials of immersive environments as a means to improve learning outcomes by promoting situated experiences, various perspectives as well as communication and knowledge acquisition and transfer (Dede, 2009 ). Additionally, the engagement, motivation and active participation of students are regarded as key contributors to the learning and teaching process as well as towards learning and academic success (Appleton et al., 2006 ; Fredricks et al., 2004 ; Hew, 2016 ; Roorda et al., 2011 ; Wang and Fredricks, 2014 ) and they can all be reinforced through the use of immersive technologies which offer richer virtual environments and more vivid experiences irrespective of users’ location (McKenzie et al., 2019 ; Rupp et al., 2016 ). The sense of presence within a virtual environment is a topic which has attracted a lot of interest. Although the terms presence and immersion seem to be alike, there is a significant difference between them as presence refers to subjective psychological response of a user experiencing a virtual environment while immersion to the objective level of sensory fidelity (Slater, 2003 ). Particularly, presence can be defined as a sense of being within a virtual world or as a perceptual illusion of being in a place that you are aware of not being there (Slater et al., 1994 ; Slater, 2018 ).

The remainder of this paper presents the main aims, motivations and incentives of this study, goes over recent related work and showcases the concept of 360-degree video and its application in educational settings. Furthermore, it analyzes the methodology followed and the results drawn based on a comparative social media data analysis of the last decade. Finally, it summarizes and discusses the main findings and drawn conclusions, goes over the challenges and limitations and provides directions for future research.

Aims, motivations and incentives

Our motivations and incentives derive from the concept of crowd wisdom in which the aggregation of information of public groups yields more efficient results while leveraging the collective knowledge and decisions of a group of individuals instead of relying on a handful of experts’ opinions (Surowiecki, 2004 ). That is the reason why we opted to base our work on the public’s viewpoints and attitudes. This type of data retrieval and analysis can be regarded as a form of crowdsourcing. Crowdsourcing is the act of outsourcing a task or a job conventionally carried out by a definite agent to an undefined public (Howe, 2006 ) and is based on the fact that a higher value is created when people work collectively in virtual and physical networks and not independently. Nowadays, both concepts of crowd wisdom and crowdsourcing are ubiquitous due to the technological advancements and the development of user-centered applications. Moreover, the rise of social media and their continuously increasing number of users have led to the creation of an immense volume of data, known as Big Data, which is exponentially increasing daily (Wu et al., 2013 ). Social media enable users to publicly share their opinions and viewpoints in real time. Additionally, these platforms provide useful tools such as Application Programming Interfaces (API) to make the available open data easily accessible. It goes without saying that social media along with the tools they offer can be used to extract invaluable knowledge regarding the public’s opinion on certain matters.

For the purposes of this study, the Twitter’s API was used to access and collect open raw data of text (tweets) that users chose to share publicly. Analyzing the text contents of the tweets in order to generate new knowledge and results was its main focus. This specific platform was selected since it is regarded as the go to platform for people to share their opinion on matters as soon as they occur or on breaking news and it urges them to be concise and precise in their posts due to the limited amount of characters per tweet (Bruns et al., 2012 ; Java et al., 2007 ; Phuvipadawat and Murata, 2010 ).

Furthermore, this study expanded over the related Twitter data of the last decade so as to have a better understanding and provide a more complete overview of the changes, evolution and advancements of this innovative technology both in general as well as in the educational sector in particular. Specifically, this study involved the identification and the collection of Twitter data from January 2010 to December 2020 regarding (1) the general use of 360° video and (2) the use of 360° video in education as well as the processing, analysis and visualization of the retrieved data in order to better comprehend the related public’s viewpoints and attitudes and answer our two main research questions which are:

RQ1: What public attitudes and perspectives about and towards the use of 360° video in general are evident in social media (Twitter) posts?

RQ2: What public attitudes and perspectives about and towards the use of 360° video in education are evident in social media (Twitter) posts?

• 360-degree video

The exponentially increase in online content, the more affordable and cost-effective technologies as well as the drastic improvements afforded to mobile technologies, have brought about an interest in the use of 360° video and immersive technologies in education. In contrast to conventional videos which offer a very restrictive perspective, 360° video provides a spherical view with multiple viewing angles and perspectives. Moreover, the contents of 360° video are in an omnidirectional form and can either be computer generated or captured from the real world and as a result, users are able to view everything within the camera range, thus more interactive, immersive, engaging and realistic experiences can be created. Furthermore, 360° video aligns well with existing pedagogical approaches and due to their immersive qualities, students perceive being physically present in a virtual environment and thus, they become more absorbed and engaged in learning activities (Rupp et al., 2016 ). Additionally, 360° video can affect students’ emotional response to the learning climate in a positive way (Ulrich et al., 2021 ).

Existing evidence suggests that 360° video has the potential to offer interactive learning experiences so it is highly likely that the positive learning aspects will be increased while the negative ones will be reduced through an interactive design and development process (Preece et al., 2015 ). Furthermore, along with the advancement of lower cost technologies, the increased online video content and the progressive integration of 360° video in social media platforms has led to the creation of an exponentially growing collection of online 360° videos (Snelson and Hsu, 2020 ). Additionally, as 360° video provides users with spherical views and renders their interaction and communication with the virtual world around them feasible, it enhances the feeling of immersion (Ranieri et al., 2020 ) and through the illusion that the experience perceptually surrounds them, they feel a sense of presence within it (Hodgson et al., 2019 ). 360° video affects the emotional response to the learning climate positively, it is cost-effective and it can increase the effectiveness of e-learning (Ulrich et al., 2021 ). Due to the visual and auditory immersion which is provided by 360° video, distraction that may impede the effectiveness of learning and teaching may be overcome and practical skills training can be enhanced.

Ranieri et al. ( 2020 ) noted that research into the use of 360° video in educational setting is at its very early stages. Therefore, given the limited empirical evidence for 360° video as an effective educational tool, the present study aims at comprehending the general public’s viewpoints and attitudes regarding the use of 360° video as a whole as well as their utilization in educational settings through the lens of social media. In that view, the main aims of this study are to analyze the public’s opinion regarding the use of 360° video as well as their specific use in education through the analysis of the publicly available and user-generated content on social media. Consequently, 360° video, either on their own or in combination with other immersive technologies, help students explore new interactive environments and perspectives as well as experience emotions and situations which would be impossible otherwise. When used in conjunction with XR technologies, contemporary virtual learning environments, which enable students to experience and interact with virtual content, can be created. These environments provide students with engaging, realistic and authentic scenarios and experiences in which they can explore, interact and communicate with virtual contents while simultaneously offering them a sense of embodiment and a feeling of belonging (Kilteni et al., 2012 ). Therefore, 360° video can be regarded as an innovative pedagogical practice which, when applied in educational settings, can yield a number of merits and increase students’ overall engagement.

Related work

Rupp et al. ( 2016 ) carried out a study in order to assess the immersiveness of educational 360° video as well as their future VR expectations on subjective experiences. Their experiment involved sixty-three (63) participants (aged 18–30) who viewed a 360-degree educational video from various devices having as a main goal to gain as much knowledge as possible. They highlighted that immersive experiences might enhance the learning experience by increasing interest in the learning content. Their findings showcased that the feelings of presence were greater when the experiences were more immersive. However, they quoted that the novelty of such experiences might overwhelm learners and lead to less information retention. Roche and Gal-Petitfaux ( 2017 ), investigated the use of 360° video in physical education teacher education so as to describe preservice teacher activity in a workshop. Particularly, they tried to pinpoint what kind of activity the students would deploy while viewing 360° video and based on the chosen activity which design would be the most useful for a video-enhanced teacher learning environment for learning to teach physical education. Their study followed the course-of-action theory (Theureau, 2003 ) and was based on the hypothesis of situated action Suchman 1987 and situated cognition Hutchins 1995 . Based on their results, 360° video constitutes a novel way to utilize video in teacher training as it allows preservice teacher to live an immersive experience. Finally, they quoted that designing 360° video enhanced environments can lead to the development of immersive situations that work well for learning to teach physical education but in order to comprehend the situation in depth, 360° video should utilize wide angles or points of view.

Aiming at reaping the benefits of 360° video in language learning, Berns et al. ( 2018 ) developed an application which offers students an immersive and fully interactive learning environment. More specifically, in order to foster learners’ language competencies, they looked into the potentials of chatbots and 360° video applications in creating immersive experiences. A Technology Acceptance Model based questionnaire (Liu et al., 2017 ) was used to assess their application effectiveness and gather information regarding students’ learning experience and attitudes. Based on their results, students, in their assessment, neither denied nor questioned the potentials of their application and had a positive attitude towards it due to its attractiveness and impressiveness and not due to the effect of novelty. With a view to evaluating the effect that 360 operative videos have on information retention and attentiveness in surgical education, Harrington et al. ( 2018 ) conducted a cross-over study in which they appraised 360° video in comparison to two dimensional (2D) ones based on students’ perspectives. In total forty (40) preclinical undergraduate students (mean age 23.2) took part in their experiments. Their results showcased that compared to 2D video 360° video offered significantly higher engagement and attentiveness benefits but there were no significant variances in information retention. It is worth noting that the participants of this experiments expressed that when it came to 360° video, the overall experience was more immersive and characterized it as both entertaining and beneficial to learning. Moreover, Hodgson et al. ( 2019 ), examined the use of 360° video in immersive VR applications in higher education. Their experiment involved students from two undergraduate courses that had adopted VR as a classroom learning method. According to the results of their questionnaire, students assessed 360° video and VR in a positive manner and expressed greater learning satisfaction while also experiencing more opportunities for professional skill rehearsal. Finally, they pinpointed that interaction through these means interest and curiosity can be promoted and professional competence reinforced. Aiming at comprehending the impact that the incorporation of 360° video into class content and educational settings have on students and teachers, McKenzie et al. ( 2019 ) presented an exploratory case study regarding the use of 360° video in educational settings. More specifically, they conducted experiments in the classroom using 360° video to determine if they have an impact on students’ feelings of presence with class content as well as how the inclusion of 360° video affects the teaching experience. In their study, they used both quantitative and qualitative aspects. Their results showcased that 360° video can constitute an alternative immersive content presentation means for students as well as help them improve their sense of presence. Additionally, they stated that these interactive and immersive experiences might distract students’ attention from the teaching staff and the image quality and file size that the given learning environment requires should be taken into account. Finally, they stated that the small number of participants that viewed the 360° video was a limitation to their study.

Violante et al. ( 2019 ) investigated the use of 360° video and interactive virtual technologies in engineering education. Their main aim was to assist in identifying a methodology for designing and using 360° video as interactive learning contents that encourage greater student engagement. Moreover, they utilized a quantitative survey, in which thirty (30) students (aged 20–24) participated, to rate students’ perceived levels of engagement in regard to behavioral, cognitive and emotional engagement. According to their results, the students evaluated the use of 360° video as highly immersive and engaging and as a means that increased their involvement, initiation and action. In addition, students also reported feeling more creative and less worried and being less distracted as they focused and concentrated more on the contents of the 360° video. Finally, when using 360° video, students expressed higher levels of interest, enjoyment and concentration which according to Shernoff et al. ( 2014 ) foster optimal learning. In their research, Ulrich et al. ( 2021 ) presented reports regarding the use of 360° video in healthcare education which involved a controlled experiment in which eighty-one (81) physiotherapy students participated. Their approach used a 3 × 3 factorial design with three treatment groups and measured their academic performance, the perceived user satisfaction and their perception of learning climate. In order to assess academic performance, the students were tested before and after each treatment while for the assessment of user satisfaction and perception of learning climate the instrument presented in Chou and Liu ( 2005 ) was adapted. According to their findings, there were no differences regarding the academic results among 360° video, regular video and traditional teaching. However, in regard to learning satisfaction as well as internal and external perspectives of the students’ learning satisfaction, traditional teaching was equal or better than both 360° video and regular video. Finally, they highlighted that even though 360° video offered more presence compared to regular video, neither of them contained any interactivity which resulted in them being either just as effective or outperformed by traditional teaching.

When it comes to 360° video, the way audiences process information may be affected since it contains new audiovisual grammar (Dooley, 2017 ). With the aim of coping with this issue, (Barreda-Ángeles et al., 2020 ) went over the significance of the immersion in the audiences’ cognitive access to information which according to Dunaway and Soroka ( 2021 ) is closely related to learning as it facilitates information acquisition and process. In order to analyze how immersion affects information processing as well as the mediating effects of arousal and presence, they conducted an experiment which included psychophysiological and self-report methods and which involved thirty-seven (37) volunteers (aged 19–43). Their results showcased that immersive presentation of narrated events through 360° video brought about feelings of presence and emotional arousal. Finally, they highlighted that in order to create immersive narratives that not only inform audiences but also engage them, a careful design of the overall experience would be of great significance. Furthermore, Snelson and Hsu ( 2020 ) conducted a scoping review through a systematic process to identify trends in peer-reviewed research journal articles about the emerging research of educational360-degree video in VR. With the aim of displaying the extent and nature of research regarding 360° video as well as the generated educational benefits and drawbacks, they identified and analyzed twelve (12) articles that met their research criteria and which included nine (9) different content areas that promoted immersive learning. Based on their results, the most important aspect of 360° video in education was immersiveness as every one of the involved studies included some type of immersive VR experience. In addition, the results of a word frequency query revealed that the word immersive or some variant was one of the most used words within these articles. Thus, they indicated that the concept of immersiveness and the role of VR are closely related to the use of 360° video in educational settings. In addition, the results in relation to the effect of 360° VR video on learning were mixed but nonetheless, the learners showcased enjoyment throughout the whole experience. Finally, they highlighted that the use of 360 VR video could be more beneficial and appropriate for certain types of learning. Ranieri et al. ( 2020 ) conducted a systematic literature review with the aim of identifying how 360° video is currently being used in different educational settings as well as the various issues and opportunities that occur. Based on their results, even though 360-degree video is still at its infancy and there is a need for more studies to be conducted, it can be used as a supplementary educational means which can increase students’ satisfaction and involvement.

As the above-mentioned studies show, even though traditional teaching cannot be fully replaced by technology, 360° video is a contemporary technology that can be used to enhance the overall teaching and learning process. Due to its immersive nature and the interactive experiences that 360° video offers, it can facilitate and enrich traditional teaching and can be used in blended learning approaches and methodologies (e.g. flipped classroom) to further improve students’ immersiveness, engagement and overall learning experience. Finally, when used in a student-centered way, 360° video can constitute a vital educational tool that can meet the new educational needs and requirements.

The present study

Even though prior research looked into the usefulness of 360° video in various educational domains, there is a limited number of studies that compare 360° video with traditional teaching methods. Furthermore, despite the fact that most studies support the positive effects that the use of 360° video brings in various sectors, still little is known about the viewpoints and attitudes of the general public regarding its use in education. With a view to bridging this gap, the present study employed a social media data analysis on public’s viewpoints and attitudes concerning 360° video as well as its use in educational settings. Before going on to present and analyze the used methodology and the drawn results from the two (2) data sets of this study and in order for it to be better comprehended, it is vital to briefly go over the concept of social media.

• Social media

As this study focuses on retrieving data from social media, it is essential to define social media and their selection as our data source. Social media nowadays constitute an integral part of modern society. Social media enable users to create a network of personal connections and are used to communicate, collaborate and interact within a common information space as they offer features that increase awareness and support informal communication and allow users to be involved in various social and interactive activities (Boyd and Ellison, 2007 ; Pallis et al., 2011 ). Having transparency, bottom-up communication, collaboration, personalization and knowledge as well as information and knowledge sharing as their foundations (Makkonen et al., 2019 ), social media platforms are based on the ideological and technological foundations of Web 2.0 and promote horizontal knowledge sharing (Kaplan and Haenlein, 2009 ; Zolkepli and Kamarulzaman, 2015 ). Additionally, social media are ruled by hybrid media logic (Gil de Zúñiga et al., 2017 ) and they can be considered as virtual communities (Dwyer et al., 2007 ) that foster the fundamental motivation factors of a sense of belonging and socialization (Özgüven and Mucan, 2013 ).

As social media enable users to create and publish content in various formats and make it instantly available to the public, they have drastically changed how people share and co-create information and knowledge, communicate and collaborate. Therefore, social media have become a significant means for web content discovery, information dissemination, debate as well as opinion and knowledge sharing. Finally, due to the sheer amount of user generated and opinionated publicly available content within social media networks, social media can be utilized to conduct data analyses in order to comprehend the wisdom of the crowd regarding specific matters. For all these reasons, we opted to use social media and Twitter in particular as a fundamental tool of this study in order to comprehend the public’s viewpoints regarding the use of 360° video as a whole and their utilization in educational settings.

Methodology

In order to meet the requirements, answer the main research questions and fulfill the aims of this study successfully, the methodology described below, which is also displayed on Fig.  1 , was followed:

Data requirements: As data quality affects the analysis drastically and consequently the conclusions drawn, the high quality of data is significant. In order to ensure data validity, accuracy, completeness, consistency and uniformity, specific aims were set regarding the data retrieval, collection, analysis and visualization processes such as the selection of time periods, variables, sources etc.

Data collection: In order to get the public’s viewpoints and attitudes regarding 360° video as well as its use in educational settings, the Twitter social networking service was used. After testing various keywords both separately and in combination, we resulted in using the keywords 360video, 360 degree video, 360-degree video, 360° video to acquire public’s opinion on 360° video and the same keywords were used in combination with the keywords education, university, higher education, professional development, school to obtain their viewpoint on the specific use of 360° video in education. For the study use case, data from Twitter over the period of January 2010 to December 2020 was collected. In total, 691,714 tweets regarding the 360° video were collected out of which 4721 tweets corresponded to the public’s opinion on the use of 360° video in educational settings. It is worth noting that only tweets that contained either one of the keywords for the first data set or a combination of them for the second one got retrieved. In this way, we could be certain that the tweets referred to the technology of 360° video. Moreover, all the publicly available information was stored for each tweet. That is, id, permalink, date, author_id, username, location, text, hashtags, mentions, urls, retweets and replies. It is evident that users’ private information or information that they chose not to share publicly cannot be retrieved.

Data processing, cleaning and storage: In order to create clean data sets, the collected data was processed. More specifically, the text was converted into lowercase and the punctuations, single characters, URLs and stop-words were omitted where necessary and the abbreviated words were expanded. Both data sets which included the retrieved tweets were stored in Comma-separated Values (CSV) files as well as in JavaScript Object Notation (JSON) format files to enhance the overall usability, flexibility and extensibility.

Data analysis and visualization: After creating the two data sets, data analysis was conducted (a) on an annual basis and (b) throughout the period of January 2010–December 2020 as a whole. For the data visualization process, the results of the data analysis were showcased through graphs which were specifically generated for each of the following cases: total number of tweets, most frequently used words, most frequently used hashtags, most frequently used mentions, most active users, tweets per given interval and tweets per country.

• Sentiment analysis

Sentiment analysis is widely applied in order to analyze and better identify and comprehend crowd sentiment regarding specific matters. Particularly, it is contextual text mining which identifies and extracts subjective information and affective states in source material through the use of Natural Language Processing (NLP), computational linguistics, biometrics and text analysis. Sentiment analysis can be regarded as the interpretation and classification of emotions within text data. Two different sentiment analysis methods, namely polarity and emotional detection, were used for the purposes of this case study. More specifically, in order to identify the crowd’s basic viewpoints (e.g. positive, negative or neutral) regarding the main research questions, basic polarity detection, that is binary text classification, was conducted. The open-source library TextBlob was used as a basis for the lexicon-based sentiment analysis (Loria et al., 2014 ) and its results were further validated through the use of Valence aware dictionary for sentiment reasoning (VADER) which is a rule-based tool for social media text sentiment analysis (Hutto and Gilbert, 2014 ). Moreover, with a view to detecting the sentiment of the public in line with the main research questions, emotion detection was also performed. In particular, the emotion analysis was based on Plutchik’s wheel of emotions which presents eight (8) basic emotions which come in pairs and the opposite emotions cannot be felt simultaneously (Plutchik and Kellerman, 1980 ; Plutchik, 1984 ). The emotions along with their pairings are: joy and sadness, acceptance and disgust, fear and anger as well as surprise and anticipation. Furthermore, the latest National Research Council Canada (NRC) Word-Emotion Association Lexicon (EmoLex) (Mohammad and Turney, 2010 ; Mohammad and Turney, 2013 ; Mohammad, 2020 ) was used as a basis for the lexicon-based sentiment analysis for emotion detection. The NRC emotion lexicon constitutes a list of English words along with their associations with the eight (8) basic emotions and two (2) sentiments.

The whole code and all scripts developed for the purposes of fulfilling this task were based on the Python programming language (version 3.8) which is an interpreted, object-oriented, high-level programming language with dynamic semantics. PyCharm was selected as the open-source integrated development environment (IDE). Additionally, in order to retrieve the related data, in our case tweets, the Twitter API was used. With a view to increasing the overall usability, flexibility and extensibility, the retrieved data was stored in both CSV and JSON files. For the purposes of data visualization, the open-source python library Matplotlib was used (Hunter, 2007 ). Finally, both TextBlob and EmoLex, which were presented earlier, were used to conduct sentiment analysis.

Developing a new machine learning model to conduct sentiment analysis was outside the scope of this specific study. For that reason and as above mentioned regarding the sentiment analysis, TextBlob and VADER were selected for the polarity detection while EmoLex was used in order to conduct emotion detection. It is worth noting that all the tools and libraries used for this study are open-source.

As TextBlob is rule-based, a pre-defined set of categorized words is required. For the purposes of this study, the widely used, verified and accepted Natural Language Toolkit (NLTK) database was used which contains a dictionary of words and their respective sentiment scores. Additionally, for each polarity task TextBlob provides a float within the range of [− 1.0, 1.0] with − 1.0 meaning a negative polarity, 1.0 a positive one while 0 refers to a neutral state. Several other studies such as (Ahuja and Dubey, 2017 ; Saha et al., 2017 ; Manguri et al., 2020 ) have also used TextBlob to successfully carry out sentiment analysis for Twitter data. To further evaluate and verify the results, VADER which is both a lexicon and a rule-based analysis tool was used. VADER, which is particularly useful for carrying out sentiment analysis on social media data, contains labeled lexical features based on their semantic orientation and utilizes them in order to calculate the text sentiment. Based on these, it calculates the probability of a given sentence, in our case tweet, to be either positive, negative or neutral. Several studies such as (Ramteke et al., 2016 ; Park and Seo, 2018 ; Elbagir and Yang, 2019 ) utilized this open-source tool to conduct similar analyses. Furthermore, as the emotion analysis is based on the eight basic emotions presented in Plutchik’s wheel of emotions, EmoLex was selected. In particular, the EmoLex lexicon constitutes a list of words along with their associations with the eight (8) basic emotions and two (2) sentiments. Due to its vast usability and popularity, it has been translated into more than one hundred languages. Through the studies that have utilized it, it has been showcased that a majority of affective norms remain stable across languages. Various studies such as van den Broek-Altenburg and Atherly ( 2019 ), Corazza et al. ( 2020 ) and Mathur et al. ( 2020 ) have used EmoLex as a tool to conduct sentiment analysis using tweets. In all cases, the sentiments and emotions are calculated based on the frequency of words in each sentence as well as the semantic relations. Consequently, more precise and accurate results are generated.

With a view to answering our main research questions and understanding the crowd wisdom on these specific matters, the above-mentioned methodology was used and two separate data sets containing Twitter data which was retrieved from January 2010 to December 2020 were created. The first data set contained a total of 691,714 data entries (tweets) regarding the public’s viewpoints and attitudes towards 360° video while the second one consisted of 4721 data entries concerning the public’s viewpoints on the use of 360° video in education. Our analysis results include tables, figures, graphs, word clouds, plots and diagrams. The information of both data sets was examined (1) as a whole and (2) on an annual basis. Particularly, the most frequently used words (with and without keywords), hashtags and mentions in the relevant tweets were analyzed. Additionally, the number of tweets made per each year and month as well as the countries that tweeted the most about the relevant topics were analyzed. Finally, polarity and emotion detection sentiment analyses were carried out.

General use of 360° video

The results regarding the public’s opinion on 360° video from January 2010 to December 2020 are presented below:

Word frequency analysis with keywords: The top-40 most commonly used words including keywords are displayed on Table  1 from which the top-5 words were: video, 360, virtual reality (vr), 360-degree and 360-video.

Word frequency analysis without keywords: The top-5 most commonly used words excluding keywords were: virtualreality (vr), xbox, youtube, game and new. The results of the top-40 most commonly used words excluding keywords are displayed on Table 2 .

Hashtag frequency analysis: The top-40 most commonly used hashtags are presented in Table 3 from which the top-5 were: #360video, #VR, #360Video, #VirtualReality and #360.

Mention frequency analysis: The top-4 most commonly used magazine, manufacturer and social media mentions for each year are displayed on Table 4 .

Frequency of tweets over the period of 2010–2020: the frequency of the monthly and annual tweets over the years 2010–2020 are presented in Table 5 .

Country frequency analysis: As users are free to set the details about the country private, which is also the default option in Twitter, it is not possible to retrieve this specific information from all users. More details about this issue will be provided in the challenges and limitations subsection further below. For that reason and although it might not be completely representative of the whole data set, the top-5 countries that tweeted the most based on the country data that we managed to collect were: the United States, India, Japan, Canada, and Australia.

Sentiment analysis–polarity detection: According to the polarity detection, the majority of the tweets was neutral (46.5%) followed by positive (39.4%) while only 14.1% of the tweets was negative when using TextBlob while 53.72% neutral, 37.2% positive and 9.08% negative when utilizing VADER. The related results for each year are presented in Table 6 .

Sentiment analysis–emotion detection: According to the analysis, the emotion frequency based on the most intense emotion of each tweet was: 53.39% Neutral (369,304 tweets), Anticipation 18.58% (128,505 tweets), Trust 7.55% (52,240 tweets), Anger 6.75% (46,703 tweets), Joy 5.59% (38,686 tweets), Fear 4.17% (28,818 tweets), Sadness 2.12% (14,641 tweets), Surprise 1.09% (7520 tweets) and the related results for each year are depicted on Table 7 .

Use of 360° video in education

Based on the keywords used, a total of 4721 tweets were collected. As we wanted to eliminate the chance that the content of a tweet related only to a specific 360° video, meaning that the users’ sentiment will be based on the content of the video and not on the technology itself, we manually applied more filters. As a result, a total of 3,241 tweets were used for the data analysis. The results regarding the public’s opinion on 360° video in education from January 2010 to December 2020 are:

Word frequency analysis with keywords: The top-5 most commonly used words including keywords were: video, 360, education, virtualreality (vr) and school. The results of the top-40 most commonly used words are displayed on Table 8 .

Word frequency analysis without keywords: The top-40 most commonly used words excluding keywords are presented in Table 9 from which the top-5 were: virtualreality (vr), youtube, reality, weekly and technology.

Hashtag frequency analysis: The top-40 most commonly used hashtags are displayed on Table 10 from which the top-5 were: #360video, #education, #VR, #technology and #immersive.

Frequency of tweets over the period of 2010–2020: The frequency of the monthly and annual tweets over the years 2010–2020 are presented in Table 11 .

Country frequency analysis: As mentioned above, due to the limitation that the country field is a private one, the results of this category might not fully represent the entire data set. Nevertheless, based on the collected data, the top-5 countries that tweeted the most about the educational use of 360° video were: Spain, the United States, Canada, Australia and India.

Sentiment Analysis–Polarity Detection: Based on the results of the polarity detection, the majority of the tweets was neutral (54.3%) followed by positive (39.68%) while only 6.02% of the tweets was negative when using TextBlob while 47.24% positive, 46.65% neutral and 6.11% negative when utilizing VADER. The related annual results are depicted on Table 12 .

Sentiment analysis–emotion detection: According to the analysis, the emotion frequency based on the most intense emotion of each tweet was: Neutral 30.39% (985 tweets), Trust 29.96% (971 tweets), Anticipation 25.52% (827 tweets), Joy 4.6% (149 tweets), Surprise 3.3% (107 tweets), Anger 2.68% (87 tweets), Fear 2.41% (78 tweets), Sadness 0.62% (20 tweets) and Disgust 0.52% (17 tweets) and the related results for each year are presented in Table 13 .

The aim of the present study was to comprehend the general public’s viewpoints and attitudes towards 360° video overall while also putting emphasis on the application of 360° video in education. With a view to providing a more complete overview and a clearer depiction of the public’s opinions regarding 360° video, the study examined the related data of the last decade. This time period was selected as several advancements have been accomplished and the specific technology has been more accessible and more widely used.

Based on the results, an increasing interest in 360° video was noticed in September 2015 and afterwards and more specifically on 23/9/2015 when Facebook introduced 360° video on their platform. The highest number of tweets was observed during December 2016 and particularly on 28/12/2016 when 360° video was also introduced on Twitter. Moreover, it was observed that 360° video is highly associated with social media, immersive experiences and contemporary technologies such as virtual reality and 4k videos. YouTube was by far the most mentioned account within the tweets. Additionally, in both cases the analysis also revealed that there were tweets about 360° video from all five continents, suggesting a worldwide spread of its use.

Regarding the general use of 360° video, the results of the social media data analysis suggested that 360° video is used worldwide and positive emotions, such as anticipation and trust, are associated with it. The majority of the activities related to 360° video were recreational (e.g., gaming, scuba diving) and YouTube was the most popular social media platform used to present 360° video. Moreover, based on the polarity analysis, the majority of people were either positive or neutral about the use of 360° video with only a very small number being negative. Therefore, it can be said that the majority of people found 360° video as a useful daily tool.

The results concerning the use of 360° in education showed that the majority of people positively viewed 360° video as an educational tool. Based on the emotion detection, 360° video was considered to be intriguing and useful for educational purposes as people expressed joy, anticipation and trust when referring to it. Once again, YouTube was the most mentioned social media platform. This fact demonstrates its popularity for educational purposes. Furthermore, words and hashtags relevant to immersion were widely associated with the use of 360° video in education which reinforces the aspect that 360° video can be used in conjunction with immersive technologies such as augmented reality and virtual reality to create more interactive, fun and engaging experiences.

At this stage, it should be said that although there is a small difference between the results of the two tools used to conduct sentiment analysis caused by their using different sentiment scores, the majority of the sentiments followed the exact same order in both cases. This fact further validates the method, tools and results of this study.

Overall, this study in line with previous research evidence demonstrated that the public has a positive view and attitude towards 360° video in everyday life and in education. Furthermore, the results of previous studies suggest that students rated high in satisfaction and immersiveness lessons that had incorporated 360° video and reported positive attitudes (Harrington et al., 2018 ; Hodgson et al., 2019 ; Liu et al., 2010 ). Therefore, it can be said that the results of the sentiment analysis seem to also reflect students’ learning experiences when 360° video was used as an educational tool.

Similar to traditional teaching, 360° video can assist in improving students’ comprehensibility, promote their active participation, creativity and engagement (Coller and Shernoff, 2009 ) and increase their concentration and enjoyment, fostering thus optimal learning conditions (Shernoff et al., 2014 ). However, 360° video provides a higher level of interactivity and immersiveness which is essential for the educational process to be effective (Zhang et al., 2006 ). This type of interactivity in combination with higher levels of satisfaction experienced in lessons that incorporate 360° video is expected to increase students’ engagement in the class. Engagement is a prime contributor to learning, teaching and academic success (Appleton et al., 2006 ; Fredricks et al., 2004 ; Wang and Holcombe, 2010 ). Therefore, creating immersive and interactive experiences through 360° video that promotes students’ active participation and engagement is of great significance. Consequently, interactive design approaches which increase the positive aspects of the user experience and decrease the negative ones should be adopted (Preece et al., 2015 ) as they will increase students’ development and academic performance in the long run. Thus, the effective use of 360° video can constitute an alternative method of learning and teaching as it offers an immersive experience and seems to be more attractive to and favorably received by students (Huang et al., 2010 ; Olmos et al., 2018 ).

Concerning education, which is the focus of the present study, students are able to comprehend abstract concepts more easily within interactive three-dimensional (3D) virtual environments (Fernandez, 2017 ). As mentioned above and as our results showcased, there is a close relationship between virtual reality and 360° video. When these technologies are combined, immersive experiences that could further enhance the positive learning outcomes could be developed. Particularly, virtual reality enables users to be immersed in virtual environments and as a result, it reinforces students’ feeling of immersion and engagement by allowing them to interact and communicate with the virtual world and its contents (Berns et al., 2018 ) while also inducing their emotions (Riva et al., 2007 ). Virtual reality is regarded as an appropriate for pedagogical purposes technology (Mikropoulos and Natsis, 2011 ). Therefore, 360° video can capitalize on the numerous educational benefits that virtual reality offers (Merchant et al., 2014 ), such as real-time feedback (Sherman and Craig, 2018 ) and opportunities to practice professional skills (Hodgson et al., 2019 ).

Finally, it is worth noting that a large number of social media tweets were neutral. Thus, it can be inferred that the general public are either unaware of the benefits of using 360° video in the educational process. As we cannot be certain about the occupation of the people who tweeted, we could speculate that in the case of educators a lack of technical skills or of the appropriate equipment and experience to develop educational material based on 360° video could be another reason. All in all, the usefulness and effectiveness of 360° video as educational tools should be further promoted by educational authorities and policy makers in order to integrate it into existing curricula and secure resources for the development of educational 360° video as it can constitute an invaluable educational tool that can be applied in all levels of education.

Challenges and limitations

Despite the specific methodology being widely used in analyzing social media data as it has been showcased in earlier sections, there still remain some challenges and limitations. The main challenges in our study were (a) defining the most suitable time period during which the text retrieval should be carried out and data should be analyzed, (b) selecting the most effective social media platform which would generate the most accurate data and (c) overcoming the various limitations that the official Twitter API sets. Additionally, with a view to providing more cohesive results, some limitations were set such as limiting the text mining to tweets that were written in English.

Nonetheless, there are some inherent limitations. Due to privacy and security manners, several data fields are regarded as private personal data in Twitter and cannot be retrieved unless made publicly available by the user. This is the case for the field of occupation. For that reason and although it would be particularly constructive, retrieving, identifying and distinguishing the occupation of each person who tweeted was not feasible. As mentioned above, the same applies for the country field. Even though some of the tweets contained this information and despite the fact that the data was normalized later, we do not believe it reflects the actual numbers and therefore, only the top-5 most common countries for each data set were mentioned.

Finally, although the specific methodology and tools used allowed for the retrieval of social media posts that solely contained text regarding 360° video, there are still some limitations regarding their sentiment analysis. In particular, as this analysis method examines the text of each post, it could be possible that in some cases the concluded sentiment and emotion were a result of the specific content of a video and not of the technology in itself. Even though this is a common phenomenon in similar studies, we tried to limit its impact by manually filtering the retrieved data to include only the data directly related to the technology of 360° video in education in the case of the educational data set.

Conclusions

The technology of 360-degree video is becoming more and more popular. As previous studies showcased, 360° video increases immersiveness and can be applied in various domains and use cases. Moreover, emphasis is being put on the education field, as 360° video can help satisfy the new and upcoming educational needs. However, so far, we have had no evidence on the informal evaluation of 360° video from people using it in their everyday life and in education. This study analyzed the results of the last decade regarding the public’s viewpoints and attitudes towards 360° video and its use in educational settings and its results are in line with those of previous studies. Particularly, this study highlighted that the public perceive 360° video in a positive manner while mostly expressing anticipation, trust and joy when referring to it. Nonetheless, the majority of teachers of all educational levels might not yet be familiar with 360° video or have the necessary technical skills or equipment to develop educational material using it or incorporating it into their teaching activities. Therefore, efforts should be made to inform the educational community about the benefits of using this learning approach and how to integrate it into the existing teaching curricula. According to existing evidence and public viewpoints and attitudes identified in the present study, 360° video can constitute an invaluable educational tool which can boost academic performance.

To sum up, due to the auditory and visual immersion 360° video offers, it may help satisfy students’ emerging needs and meet the new educational requirements successfully and thus resulting in more effective learning. Particularly, 360° video can be regarded as a useful means that creates richer virtual experiences and as a complementary educational tool that can enrich the teaching and learning process as well as promote and enhance students’ motivation, engagement and enjoyment. Due to its interconnected nature, 360° video can be used in conjunction with several other technologies such as virtual reality and augmented reality as well as with innovative learning approaches and methods e.g. gamification, flipped classroom, digital story telling etc. to provide several educational benefits and improve the overall learning and teaching process. Therefore, it may play a vital role in education in the near future. Finally, to better assess the effectiveness of 360° video in education, future studies should highlight the role of educators, teachers and students and involve them in the development process in an active way so as to create more personalized and student-centered experiences and environments and to achieve primal results.

Availability of data and materials

The data sets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Information and Communication Technology

Extended Reality

Two-dimensional

Application Programming Interfaces

Comma-Separated Values

JavaScript Object Notation

Natural Language Processing

Natural Language Toolkit

Valence aware dictionary for sentiment reasoning

National Research Council Canada

Word-Emotion Association Lexicon

Integrated development environment

Three-dimensional

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Acknowledgements

The authors would like to express their gratitude to the SEPA360 project consortium for their valuable feedback during the design of the study.

The present study was supported by European Union through an Erasmus+ Strategic Partnership, Key Action 2 grant.

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Lampropoulos, G., Barkoukis, V., Burden, K. et al. 360-degree video in education: An overview and a comparative social media data analysis of the last decade. Smart Learn. Environ. 8 , 20 (2021). https://doi.org/10.1186/s40561-021-00165-8

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A Complete Guide to Video Analytics

In this guide we cover the origins of video analytics, video content analysis, the use of video intelligence in multiple industry sectors, stand-out analytics advances and future AI applications.

This guide covers the origins of video analytics, video content analysis, the use of video intelligence in multiple industry sectors, stand-out analytics advances and future AI applications.

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Video analysis is the processing of video footage in real-time and the transformation of captured information into intelligent data. The analytics process automatically generates descriptions of video activity (or ‘metadata’) to detect and track objects such as people and vehicles.

Video analysis software is generally installed to generate objective data intended to inform decision making and ‘best next actions.’ Today, ‘next actions’ cover a vast range of possibilities, from security interventions to procedural updates and lifestyle management.

Video analytics software is fast and accurate – often supported by artificial intelligence – and have largely replaced manual video footage review, which was and is inefficient, costly, and prone to human error.

Video analysis is most frequently deployed to overcome human limitations when it comes to coping accurately with large volumes of data.

Video analytics solutions are also used for video data mining, which involves the analysis of historical data to discover insights such as trends and patterns.

Video content analysis

Video content analysis enables organisations to maximise the value of CCTV video footage. Video content analysis helps organisations to analyse content for performance improvement, customer behaviour and preferences, to understand potential risks, and to develop valuable training materials.

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Many businesses record and store large volumes of video footage but, without content analysis, the video footage they hold represents an untapped resource.

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Track customer visiting times

Analyse customer behaviour

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Develop ‘real-world’ training tools

Assess health and safety risks

Trend analysis and performance analysis are important to any organisation., and are particularly valuable in sectors such as:

Law enforcement

Public space and facilities management

Consider the value to a retailer of insights into what makes a particular product or area of a store popular or unpopular. Consider the value to clinicians of redacted video of live healthcare procedures for staff training. Consider the value of video analysis in redacted video of events when training police officers, to illustrate the most effective ways to deal with a variety of situations.

The evolution of video analytics: surveillance roots stimulate video analytics debate

Since the widespread introduction of CCTV in the 1970s, surveillance of the public has been met with a mix of sceptical and positive reviews. Opinion is split between those who see video analytics as an intrusion on privacy and those who value its effectiveness for preventing and punishing criminal activity.

However, over the years since video analytics solutions were introduced, the use of what is known as smart video technology has spread to inform the analysis of every type of activity, in business, sports and people’s lifestyles, and has extended far beyond security operations.

How do video analytics work?

Video content analysis refers to the automated processing and analysis of video streams in real-time to extract meaningful information.

Video content analysis can also be used to analyse past events and, when redacted to comply with privacy regulations, to develop training materials.

Analytics technology leverages techniques from computer vision, machine learning and artificial intelligence to understand and interpret video data. Depending on the particular use case, the architecture of a solution may vary.

When conducted in real time, video content analysis isconfigured in the system to trigger alerts for specific events and incidents that occur in the moment, or in post processing, by performing advanced searches to facilitate extensive analysis.

Key techniques in video content analysis

Object detection and tracking

Object detection : Identifying and locating objects within a frame. Common methods include using convolutional neural networks (CNNs) like YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector).

Object tracking : Following the detected objects across multiple frames. Algorithms like SORT (Simple Online and Realtime Tracking) or Deep SORT are popular.

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Detecting and understanding specific actions or behaviours of objects. For example, recognising if someone is loitering, falling or fighting.

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Identifying or verifying a person’s identity using their facial features. Techniques involve deep learning models like those based on FaceNet architecture.

Motion detection

Identifying sections in video footage where motion is occurring, which is often used in surveillance and security applications.

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Identifying changes in a scene, such as lighting changes and camera movements.

Anomaly detection

Detecting unusual patterns or behaviours that deviate from the norm, which is useful in security for spotting suspicious activities.

Optical character recognition (OCR)

Extracting text from video frames is useful for reading license plates, signage and other textual information.

Video summaries

Creating concise summaries of video content by identifying and compiling key frames or segments.

Video content analysis and privacy regulations

Some video analytics tools, for example, Facit’s queue management CCTV plug-in solution , track objects as opposed to capturing people’s personal data such as faces. Object counting does not pose a risk of breaching privacy regulations (e.g., GDPR).

However, CCTV footage of staff, customers and the general public that shows people’s faces, licence plates and any other personally identifying information must be treated responsibly so that it complies with privacy regulations.

Before video can be shared with or viewed by third parties, all personal data must be redacted (masked or removed). For example, video that is submitted as evidence in court must be redacted to remove all but the subject(s) of interest.

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Facit’s Identity Cloak redacts personal data in video in real-time and post-event to ensure privacy compliance.

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Surveillance and security Automated monitoring of public and private spaces for threats, suspicious activities and incidents. Examples include intrusion detection, perimeter protection and crowd monitoring. Retail analytics Analysing customer behaviour in stores to optimise layout, improve customer service and increase sales. Applications include heat mapping, queue management and demographic analysis. Traffic and transportation Monitoring and managing traffic flow, detecting accidents and enforcing traffic rules. Use cases include vehicle counting, speed detection and congestion analysis. Monitoring behaviour and safety on trains and buses. Healthcare Monitoring patients in hospitals for fall detection, patient movement, and to ensure compliance with medical protocols. Telemedicine applications for remote patient monitoring. Smart cities Integrating video analytics for urban management, such as monitoring public spaces, improving public safety and managing utilities. Applications include waste management, energy use monitoring and public transport management. Industrial automation Monitoring production lines for quality control, detecting defects and ensuring safety compliance. Use cases include counting products, identifying defects and monitoring worker safety.

Video content analysis considerations

Data privacy and security Handling and storing video data securely to ensure compliance with data protection regulations.

Accuracy and reliability Ensuring that algorithms can perform accurately in diverse conditions, for example varying lighting and complex backgrounds.

Scalability Developing systems that can scale to handle large volumes of video data from multiple sources.

Video content analysis is a rapidly evolving field with vast potential across multiple industries. Its success hinges on advancements in AI and machine learning, as well as on addressing the ethical and technical challenges associated with video data.

Feeding video data to the system

The data to be analysed can be ingested from various video sources. The most common are CCTV cameras, traffic cameras and online video feeds. The majority of video sources can be integrated into the analytics solution.

The breadth of the video coverage and the amount of data collected determine the breadth and, to some extent, the accuracy of the analytics.

How do people use video content analysis to improve performance?

Video content analysis (VCA) has become an essential tool for improving performance in various fields, including sports, education, business and law enforcement.

VCA in sports:

Technical skills

Motion analysis : Coaches and athletes use video to break down movements frame by frame to identify areas for improvement in technique, such as the swing of a golf club.

Biomechanics : Analysing the mechanics of movements to ensure efficiency and prevent injury.

Tactical Analysis

Game review : Teams review footage of games to study opponents' strategies and develop counter-strategies.

Positional play : Players can observe their positioning and movement in relation to their teammates and opponents to improve decision-making.

Performance metrics

Statistical analysis : Combining video with statistical data to evaluate performance metrics such as speed, agility and accuracy.

Progress tracking : Comparing current performance with past videos to track improvement over time.

VCA in education:

Teaching and learning

Self-observation: Teachers can record their lessons to review and refine their teaching methods in order to improve engagement and clarity.

Student feedback : Students can record presentations or practice sessions to receive feedback from teachers and peers.

Skill development

Repetitive practice : Students can watch instructional videos repeatedly to master complex concepts or skills.

VCA in business:

The latest video content analysis functions with AI and machine learning that enable automated video content analysis to identify patterns and provide business insights.

By employing video content analysis, individuals and organisations can gain valuable insights into performance in order to make operational improvements.

Would you like to learn more about how people use video content analysis to improve performance?

How is video content analysis used in retail.

Video analysis in retail is used variously to enhance operational efficiency, customer experience and security. Key applications include:

Customer behaviour analysis

Foot traffic analysis : Retailers use video analytics to monitor and analyse the flow of customers in and out of a store and in different store locations. Analysis helps in understanding peak hours, popular products and optimal staff resourcing.

Dwell time : By measuring how long customers spend in different areas, retailers can identify which displays or products attract the most attention and optimise store layout accordingly.

Queue management

Video analytics can monitor queue lengths and waiting times at checkout counters. When queues get too long, the system can alert staff to open additional counters in order to reduce customer wait times, improve customer satisfaction and prevent purchase abandonment.

Loss prevention and security

Video content analysis helps in identifying suspicious behaviours such as loitering, shoplifting and employee theft. Advanced systems can alert security personnel in real-time to improve loss prevention efforts.

Customer demographics

Advanced video analytics can estimate customer demographics such as age, gender and even mood. Demographic data helps in tailoring marketing strategies and personalised customer experiences.

Heat mapping

Video content analysis can create heat maps of customer movement and engagement within a store. Heat maps help retailers to understand high-traffic and low-traffic areas and optimise product placements and store layouts.

In-store advertising and promotions

By analysing customer behaviour and demographics, retailers can tailor in-store advertising to influence specific audiences effectively by improving the relevance and impact of promotions.

Compliance and safety

Video analytics can ensure that safety protocols are followed, such as monitoring occupancy or, during a pandemic, social distancing or mask-wearing. VCA can also help stores to comply with regulatory requirements.

Staff performance

Video analysis can be used to monitor staff interactions with customers to help in assessing and improving customer service quality. Video content analysis helps to identify training needs and footage can be redacted to become training resources.

Omnichannel integration

Integrating video content analysis with other data sources such as mobile apps, loyalty schemes and online behaviour can provide a comprehensive view of customer preferences and behaviours, which enables retailers to enhance omnichannel retail strategies.

By adopting video content analysis, retailers can improve operational efficiency, enhance customer experience, reduce losses and stimulate sales growth.

Retailers benefit most from video analytics

Retail is the industry sector that has benefited most from the use of video analytics services. Smart video analysis used in smart zones and retail is so advanced that retailers have the ability to gain a deep understanding of consumer behaviour.

Bricks-and-mortar retailers can make the physical space within which they operate work better for them by capturing unprecedented levels of insight and business intelligence. Such data was historically very difficult to acquire. Today, retailers have at their fingertips actionable data on traffic flow, workforce management, store design and merchandising, supply, security and compliance.

Video intelligence helps retail businesses to align their targets for personnel, finances, overheads and margins, with new evidence-based data.

How retail video analytics may become commonplace in the future is indicated by emerging outlets such as Amazon Go . Video analytics eliminate check-outs to simplify the customer’s shopping experience and enable customers to walk out of the store having been charged automatically for the items they have selected.

Central processing vs edge processing

Video analysis software can be run centrally on servers located in a monitoring station, in other words central processing. Alternatively, analysis can be embedded in the cameras themselves, which is known as edge processing .

The selection of central and edge processing is often determined by bandwidth and storage considerations. Legacy systems catered only for central processing, which lends itself to extensive post processing, while edge processing can be pre-configured only to send data related to specific event types.

Defining situations and training models

After installation, it is possible to introduce models and train the system to become increasingly accurate at identifying events. For example, recognising vehicle types such as moving and stationary trucks, cars and motorbikes. In this instance, the data can be used to detect a possible crash.

Classifying objects such as people and vehicles, animals and man-made structures, in combination with local geographical data, enables the system to become smarter over time.

Image datasets are available that simplify the training of new models, while pre-trained models are available to fine-tune models for a specific use case.

Growing public understanding of the value of video analytics

Video analytic software is now capable of producing insights and reports that facilitate increased operational efficiency across all business types, by measuring and optimising what we do and how we act.

Perhaps video analytics entered the public’s consciousness most recently and most comprehensively through its development in sports.

Football fans cannot escape the controversy and ongoing human errors that surround the use of the otherwise accurate VAR (video assisted referee). Then again, technological change has always been viewed with scepticism, until it proves its value.

Used correctly, video analysis of athletic performance provides objective insights into anything observable, repeatable and improvable with a player’s movement and technique. Technology helps trainers to plan sports development and enables managers to make tactical decisions based on live data, such as distance covered, pitch heatmaps, passing accuracy, and more.

The application of video analytics in the wider community

Almost every industry around the world has reaped the rewards of wider video analysis. Those benefitting include operators making use of video intelligence in retail, commercial offices, warehouses and workspaces, as well as in the travel, transport, leisure, education and medical sectors.

The benefits of healthcare video analytics

Healthcare staff from surgeons to nurses benefit in many ways from the use of smart video analytics services. Surgeons use video analytics as a way of detecting minute mistakes, which improves clinical procedures over time in a way that was not previously possible.

The training of healthcare professionals is improved by the advent of remote procedure observation. Hospitals are generally able to improve the way they operate owing to regular insights into staff processes, and staff interaction, for example with people and equipment.

Video analytics in healthcare support incremental improvements that contribute to the health of patients and senior management’s awareness of best practice.

Away from hospitals, video analytics solutions are deployed to manage the health and safety of potentially vulnerable people, where cameras can detect in real time if a person has fallen, such as an elderly person living alone or a remote worker operating in a hazardous environment.

How is video content analysis used in healthcare?

Video analysis in healthcare leverages advanced technologies to enhance patient care, improve medical training and streamline operations. Key applications include:

Patient monitoring and diagnosis

Remote patient monitoring : Video analysis helps in monitoring patients remotely, especially those with chronic conditions. Cameras can track vital signs, detect falls and alert caregivers to potential emergencies.

Behavioural analysis : For patients with neurological conditions or mental health issues, video analysis can aid in observation and analyse behaviour patterns to help diagnose conditions like autism, dementia and depression.

Postoperative monitoring : Continuous video monitoring can track a patient's recovery process and detect complications early to ensure timely intervention.

Surgical assistance and training

Surgical navigation : During surgeries, real-time video analysis assists surgeons by providing augmented reality overlays that enhance precision in complex procedures.

Training and simulation : Video recordings of surgeries are used for educational purposes to enable medical students and professionals to learn from real cases and improve their skills through detailed review and analysis.

Error reduction : Video analysis can identify and highlight deviations from standard procedures, which helps to minimise human error and improve patient safety.

Rehabilitation and therapy

Physical therapy : Video analysis can track a patient’s movements during rehabilitation exercises and provide real-time feedback to maximise recovery and prevent injury.

Virtual reality therapy : Combining video analysis with VR, patients can undergo immersive therapy sessions that are monitored and adjusted based on real-time feedback from the system.

Operational efficiency

Workflow optimisation : Hospitals and clinics use video analysis to monitor the flow of patients, staff and equipment, which helps to identify bottlenecks and optimise processes.

Security and safety : Ensuring the safety of patients and staff is critical. Video surveillance systems enhanced with video content analysis can detect unauthorized access, monitor hazardous areas and ensure compliance with safety protocols.

Research and development

Clinical trials : Video analysis is used to monitor participants in clinical trials to ensure accurate data collection on the efficacy and safety of new treatments.

Biomedical research : High-resolution video microscopy and imaging techniques are used to observe cellular and molecular processes, which helps clinicians to understand diseases and development new therapies.

Telemedicine

Virtual consultations : Video analysis can enhance telemedicine by providing tools for remote examination, such as analysing wounds or monitoring respiratory conditions.

Patient engagement : Tools such as emotion detection help in understanding patient concerns and improving doctor-patient communication during virtual visits.

Elderly care

Activity monitoring : For elderly patients, especially those living alone, video analysis can monitor daily activities to ensure that they are following routines and to detect any unusual behaviour that might indicate health issues.

Fall detection : Advanced video systems can detect falls in real-time , which facilitates an immediate assistance and reduces the risk of serious injury.

Infection control

Hygiene compliance : Video content analysis can be used to monitor and ensure compliance with hand hygiene practices and cleaning protocols in healthcare facilities, which is critical for preventing hospital-acquired infections.

Video analysis in healthcare is a multi-faceted tool that enhances patient care, improves training and operations, and supports research and development, all of which contribute to a more efficient and effective healthcare system.

Video analysis in education supports continual innovation

The education sector relies on continual innovation and adaptation. Individuals must find new ways of learning about themselves and improve in a cycle of continuous professional development.

Teachers are asked to become more self-aware and improve the way they present their lessons in order to enhance the student experience and learning environment.

Video analytics services enable experts to provide constructive feedback to teachers at every stage of their development in order to help them become and remain effective educators.

Transport: video content analytics keep us on the move

American transportation entrepreneur, Robin Chase, co-founder of Zipcar, said:

“Transportation is the centre of the world! It is the glue of our daily lives. When it goes well, we don't see it. When it goes wrong, it negatively colours our day, makes us feel angry and impotent, and curtails our possibilities.”

Transport is a necessity for the vast majority of people. When transport operators have issues, they have a measurable impact and are widely publicised. As a result, it is natural that video analysis is integrated into the control and monitoring of most transport types.

Rail and air travel, motoring, and interactions at transport hubs have all been enhanced by the use of video analysis. For example, video analysis combined with improved artificial intelligence improves traffic flow management and reduces congestion. Video analysis is also used to detect suspicious behaviour and anticipate potential dangers before they develop.

Analytics at work: how Transport for London (TfL) makes widespread use of video analytics

TfL’s explanation of its use of video analytics is informative and encouraging for privacy sceptics. The bulk of TfL’s CCTV operations support TfL’s statutory functions, in particular, to deliver “integrated, efficient and economic transport facilities and services to, from and within Greater London.” The main purposes of TfL’s CCTV operations and video analytics include:

Protecting the health and safety of employees, customers and members of the public

Protecting property and other infrastructure

The management and investigation of major incidents

Preventing and detecting crime and antisocial behaviour

Realtime traffic monitoring

Supporting the efficient management and operation of our road and rail networks

Video analytics are also used to create a visual summary of the operational changes TfL makes from time to time. Examples include: improvements to the layout of road junctions, or cycle lanes, or where traffic signal timings ae amended. Videos are used to demonstrate how changes contribute to the Mayor's Transport Strategy and other initiatives such as the Healthy Streets Approach .

TfL emphasises that the focus of any photographs or videos is the road network itself and not individual pedestrians, vehicles or cyclists. When personal data is captured inadvertently, redaction (blurring) is used to minimise the extent that people and cars are recognisable.

Video analytics are key in in the development of smart cities when an increase in traffic can result in an increase in accidents and traffic jams if adequate traffic management measures are not taken.

Security video analytics are in a class of their own

As we suggested at the outset, video surveillance was the original home of video analytics. It is therefore not surprising that major advances in security applications have involved advanced analytics.

Facial recognition and license plate recognition are used to identify people and vehicles in real-time in order to make informed decisions. The reasons for using video intelligence may range from searching for a suspect to detecting missing cars and persons, in both ‘live’ and stored video footage.

Crowd management is another key function of security systems. Video analysis tools can help to manage busy shopping malls, hospitals, stadiums and transport hubs. Video intelligence is used to trigger alerts when an occupancy threshold is reached, or when prohibited movement is detected.

One of the latest developments in video analytics is camera analytics software, or ‘edge’ processing. ‘Edge’ processing eliminates the need for central servers as the video processing is embedded in the cameras themselves.

How is video content analysis used in law enforcement?

Video content analysis in law enforcement is a powerful tool that is used to enhance public safety, solve crimes and increase the efficiency of police work.

1. Crime prevention

Surveillance systems: Continuous monitoring through surveillance cameras in public areas helps to deter criminal activity. Advanced video content analysis can identify suspicious behaviours in real time to facilitate immediate intervention.

2. Crime investigation

Evidence collection: Video footage from security cameras, body-worn cameras, dashcams and public/private CCTV systems provide crucial evidence in criminal investigations. Automated facial recognition systems can identify suspects, missing persons or individuals of interest from video footage.

Object tracking tools can track the movement of vehicles, persons or objects across multiple cameras to reconstruct events associated with a crime.

License plate recognition can be used to identify vehicle license plates to track vehicles involved in criminal activities.

3. Real-time monitoring

Live feeds: Law enforcement agencies monitor live feeds to respond swiftly to incidents, manage crowds and ensure public safety during events. Automated alerts: Systems trigger alerts for predefined events, such as a person entering a restricted area or an unattended bag in a public space.

4. Post-event analysis

Behavioural analysis: Reviewing footage to analyse the behaviour of individuals in the context of a crime helps in identifying patterns that help to predict future offences. Incident reconstruction: Reconstructing the sequence of events associated with a crime to understand how it occurred and identify all parties involved. Forensic video content analysis: Enhancing and clarifying video footage to ensure that minute details can be observed, which might be crucial for court evidence.

5. Training and performance evaluation

Officer training: Analysing footage from training exercises to improve the skills and techniques of law enforcement officers. Performance review: Reviewing footage from body-worn cameras to evaluate officers' interactions with the public and adherence to protocols.

Training materials: Use of redacted event recordings during officer orientation to specific event types, such as crowd control, riots and large-scale accidents.

6. Technological Integration

AI and machine learning: Implementing AI algorithms to detect and categorise activities automatically in order to reduce the workload on human operators. Data integration: Combining video data with other data sources (e.g., emergency phone calls) to provide a comprehensive view of incidents and trends.

Video analytics concerns and considerations Privacy concerns: Ensuring the right balance between surveillance and privacy rights Legal and ethical issues: Navigating the legalities around the use of video evidence and ensuring that it is admissible in court.

Video content analysis is a crucial component of modern law enforcement that aids in everything from real-time crime prevention to detailed post-incident investigations.

Video content analysis improves AI functionality

As well as the use of video content analysis to train people, VCA is also invaluable in improving the capabilities and accuracy of artificial intelligence technologies.

Algorithms can be refined to interpret nuanced scenes and activities through the use of video content analysis.

Facit: analytics, operations, compliance

Facit enables customers to maximise the value of their video footage, from queue management and heat mapping, to loss prevention and fall detection.

Our content analysis solutions are simple to install and operate, and integrate with customers’ business intelligence suites to generate a wide range of actionable insights.

If you are interested to learn more about advances in video content analysis, or are considering maximising intelligence available to you via CCTV data, we would be delighted to discuss video analytics software , operations management and compliance automation. We can help you lead the AI revolution.

Related Insights

Leveraging real-world video training for compliance and safety, data democratisation, artificial intelligence and data security: 3 smart analytics trends in 2023, people counting on the edge for accurate real-time results on axis cameras.

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Effective Educational Videos: Principles and Guidelines for Maximizing Student Learning from Video Content

• Cynthia J. Brame

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Educational videos have become an important part of higher education, providing an important content-delivery tool in many flipped, blended, and online classes. Effective use of video as an educational tool is enhanced when instructors consider three elements: how to manage cognitive load of the video; how to maximize student engagement with the video; and how to promote active learning from the video. This essay reviews literature relevant to each of these principles and suggests practical ways instructors can use these principles when using video as an educational tool.

Video has become an important part of higher education. It is integrated as part of traditional courses, serves as a cornerstone of many blended courses, and is often the main information-delivery mechanism in online courses. Several meta-analyses have shown that technology can enhance learning (e.g., Means et al ., 2010 ; Schmid et al ., 2014 ), and multiple studies have shown that video, specifically, can be a highly effective educational tool (e.g., Allen and Smith, 2012 ; Kay, 2012 ; Lloyd and Robertson, 2012 ; Rackaway, 2012 ; Hsin and Cigas, 2013 ; Stockwell et al ., 2015 ). Video may have particular value for student preparation in biology classes, in part because students may find it more engaging ( Stockwell et al ., 2015 ) and because it can be well suited to illuminating the abstract or hard-to-visualize phenomena that are the focus of so many biology classes (e.g., Dash et al ., 2016 ; see Video Views and Reviews features in CBE—Life Sciences Education for other examples). The medium is not inherently effective, however; Guo et al . (2014) have shown that students often disregard large segments of educational videos, while MacHardy and Pardos (2015) demonstrate that some videos contribute little to student performance. What, then, are the principles that allow instructors to choose or develop videos that are effective in moving students toward the desired learning outcomes? Consideration of three elements for video design and implementation can help instructors maximize video’s utility in the biology classroom:

Cognitive load

Student engagement

Active learning

Together, these elements provide a solid base for the development and use of video as an effective educational tool.

COGNITIVE LOAD

One of the primary considerations when constructing educational materials, including video, is cognitive load. Cognitive load theory, initially articulated by Sweller (1988 , 1989 , 1994 ), suggests that memory has several components. Sensory memory is transient, collecting information from the environment. Information from sensory memory may be selected for temporary storage and processing in working memory, which has very limited capacity. This processing is a prerequisite for encoding into long-term memory, which has virtually unlimited capacity. Because working memory is very limited, the learner must be selective about what information from sensory memory to pay attention to during the learning process, an observation that has important implications for creating educational materials.

Based on this model of memory, cognitive load theory suggests that any learning experience has three components. The first of these is intrinsic load, which is inherent to the subject under study and is determined in part by the degrees of connectivity within the subject. The common example given to illustrate a subject with low intrinsic load is a word pair (e.g., blue = azul ); grammar, on the other hand, is a subject with a high intrinsic load due to its many levels of connectivity and conditional relationships. In an example from biology, learning the names of the stages of mitosis would have lower intrinsic load than understanding the process of cell cycle control. The second component of any learning experience is germane load, which is the level of cognitive activity necessary to reach the desired learning outcome—for example, to make the comparisons, do the analysis, and elucidate the steps necessary to master the lesson. The ultimate goal of these activities is for the learner to incorporate the subject under study into a schema of richly connected ideas. The third component of a learning experience is extraneous load, which is cognitive effort that does not help the learner toward the desired learning outcome. It is often characterized as load that arises from a poorly designed lesson (e.g., confusing instructions, extra information) but may also be load that arises due to stereotype threat or imposter syndrome. These concepts are more fully articulated and to some extent critiqued in an excellent review by deJong (2010) .

These definitions have implications for design of educational materials and experiences. Specifically, instructors should seek to minimize extraneous cognitive load and should consider the intrinsic cognitive load of the subject when constructing learning experiences, carefully structuring them when the material has high intrinsic load. Because working memory has a limited capacity, and information must be processed by working memory to be encoded in long-term memory, it is important to prompt working memory to accept, process, and send to long-term memory only the most crucial information ( Ibrahim et al ., 2012 ).

The cognitive theory of multimedia learning builds on the cognitive load theory, noting that working memory has two channels for information acquisition and processing: a visual/pictorial channel and an auditory/verbal-processing channel ( Mayer, 2001 ; Mayer and Moreno, 2003 ). Although each channel has limited capacity, the use of the two channels can facilitate the integration of new information into existing cognitive structures. Using both channels maximizes working memory’s capacity—but either channel can be overwhelmed by high cognitive load. Thus, design strategies that manage the cognitive load for both channels in multimedia learning materials promise to enhance learning.

These theories give rise to several recommendations about educational videos (see Table 1 ). Based on the premise that effective learning experiences minimize extraneous cognitive load, optimize germane cognitive load, and manage intrinsic cognitive lead, four effective practices emerge.

Signaling , which is also known as cueing ( deKoning et al ., 2009 ), is the use of on-screen text or symbols to highlight important information. For example, signaling may be provided by the appearance of two or three key words ( Mayer and Johnson, 2008 ; Ibrahim et al ., 2012 ), a change in color or contrast ( deKoning et al ., 2009 ), or a symbol that draws attention to a region of a screen (e.g., an arrow; deKoning et al ., 2009 ). By highlighting the key information, signaling helps direct learner attention, thus targeting particular elements of the video for processing in the working memory. This can reduce extraneous load by helping novice learners with the task of determining which elements within a complex tool are important, and it can also increase germane load by emphasizing the organization of and connections within the information. Mayer and Moreno (2003) and deKoning et al . (2009) have shown that this approach improves students’ ability to retain and transfer new knowledge from animations, and Ibrahim et al . (2012) have shown that these effects extend to video.

The benefits of signaling are complemented by segmenting , or the chunking of information in a video lesson. Segmenting allows learners to engage with small pieces of new information and gives them control over the flow of new information. As such, it manages intrinsic load and can also increase germane load by emphasizing the structure of the information. Segmenting can be accomplished both by making shorter videos and by including “click forward” pauses within a video, such as using YouTube Annotate or HapYak to provide students with a question and prompting them to click forward after completion. Both types of segmenting have been shown to be important for student engagement with videos ( Zhang et al ., 2006 ; Guo et al ., 2014 ) and learning from video ( Zhang et al ., 2006 ; Ibrahim et al ., 2012 ).

Weeding , or the elimination of interesting but extraneous information that does not contribute to the learning goal, can provide further benefits. For example, music, complex backgrounds, or extra features within an animation require the learner to judge whether he or she should be paying attention to them, which increases extraneous load and can reduce learning. Importantly, information that increases extraneous load changes as the learner moves from novice toward expert status. That is, information that may be extraneous for a novice learner may actually be helpful for a more expert-like learner, while information that is essential for a novice may serve as an already known distraction for an expert. Thus, it is important that the instructor consider his or her learners when weeding educational videos, including information that is necessary for their processing but eliminating information that they do not need to reach the learning goal and that may overload their working memory. Ibrahim et al . (2012) has shown that this treatment can improve retention and transfer of new information from video.

Finally, the utility of video lessons can be maximized by matching modality to content. By using both the audio/verbal channel and the visual/pictorial channel to convey new information, and by fitting the particular type of information to the most appropriate channel, instructors can enhance the germane cognitive load of a learning experience. For example, showing an animation of a process on screen while narrating it uses both channels to elucidate the process, thus giving the learner dual and complementary streams of information to highlight features that should be processed in working memory. In contrast, showing the animation while also showing printed text uses only the visual channel and thus overloads this channel and impedes learning ( Mayer and Moreno, 2003 ). In another example, using a “talking head” video to explain a complex process makes productive use only of the verbal channel (because watching the speaker does not convey additional information), whereas a Khan Academy–style tutorial that provides symbolic sketches to illustrate the verbal explanation uses both channels to give complementary information. Using both channels to convey appropriate and complementary information has been shown to increase students’ retention and ability to transfer information ( Mayer and Moreno, 2003 ) and to increase student engagement with videos ( Guo et al ., 2014 ; Thomson et al ., 2014 ).

STUDENT ENGAGEMENT

Another lens through which to consider educational video is student engagement. The idea is simple: if students do not watch videos, they cannot learn from them. Lessons on promoting student engagement derive from earlier research on multimedia instruction and more recent work on videos used within MOOCs (massive open online courses; see Table 1 ).

The first and most important guideline for maximizing student attention to educational video is to keep it short . Guo and colleagues examined the length of time students watched streaming videos within four edX MOOCs, analyzing results from 6.9 million video-watching sessions ( Guo et al ., 2014 ). They observed that the median engagement time for videos less than 6 minutes long was close to 100%–that is, students tended to watch the whole video (although there are significant outliers; see the paper for more complete information). As videos lengthened, however, student engagement dropped, such that the median engagement time with 9- to 12-minute videos was ∼50%, and the median engagement time with 12- to 40-minute videos was ∼20%. In fact, the maximum median engagement time for a video of any length was 6 minutes. Making videos longer than 6–9 minutes is therefore likely to be wasted effort. In complementary work, Risko et al . (2012) showed 1-hour videos to students in a lab setting, probing student self-reports of mind wandering four times in each lecture and testing student retention of lecture material after the lecture. They found that student report of mind wandering increased and retention of material decreased across the video lecture ( Risko et al ., 2012) .

Another method to keep students engaged is to use a conversational style . Called the personalization principle by Mayer, the use of conversational rather than formal language during multimedia instruction has been shown to have a large effect on students’ learning, perhaps because a conversational style encourages students to develop a sense of social partnership with the narrator that leads to greater engagement and effort ( Mayer, 2008 ). In addition, some research suggests that it can be important for video narrators to speak relatively quickly and with enthusiasm . In their study examining student engagement with MOOC videos, Guo and colleagues observed that student engagement was dependent on the narrator’s speaking rate, with student engagement increasing as speaking rate increased ( Guo et al ., 2014 ). It can be tempting for video narrators to speak slowly to help ensure that students grasp important ideas, but including in-video questions, “chapters,” and speed control can give students control over this feature—and increasing narrator speed appears to promote student interest.

Instructors can also promote student engagement with educational videos by creating or packaging them in a way that conveys that the material is for these students in this class . One of the benefits for instructors in using educational videos can be the ability to reuse them for other classes and other semesters. When creating or choosing videos, however, it is important to consider whether they were created for the type of environment in which they will be used. For example, a face-to-face classroom session that is videotaped and presented within an online class may feel less engaging than a video that is created with an online environment as the initial target ( Guo et al ., 2014 ). A video’s adaptability can be enhanced, however: when reusing videos, instructors can package them for a particular class using text outside the video to contextualize the relevance for that particular class and lesson.

ACTIVE LEARNING

As biology educators, we have abundant evidence that active learning in the classroom provides clear advantages over passive encounters with course material through lecture (e.g., Knight and Wood, 2005 ; Haak et al ., 2011 ; Freeman et al ., 2014 ). Similarly, elements that promote cognitive activity during video viewing can enhance student learning from this medium (see Table 1 ).

Schacter and Szpunar (2015) propose a conceptual framework for enhancing learning from educational videos that identifies online learning as a type of self-regulated learning. Self-regulation of learning requires students to monitor their own learning, to identify learning difficulties, and to respond to these judgments; in other words, it requires students to actively build and interrogate mental models, practicing metacognition about the learning process. Novices within a field, however, have difficulty accurately judging their understanding, often overestimating their learning ( Bjork et al ., 2013 ). This problem may be enhanced when new information is delivered via video, which students report as easier to learn and more memorable than text ( Salomon, 1994 ; Choi and Johnson, 2005 ). Incorporating prompts for students to engage in the type of cognitive activity necessary to process information—to engage in active learning—can help them build and test mental models, explicitly converting video watching from a passive to an active-learning event. The means to do this can vary, but the following strategies have demonstrated success in some contexts.

Package Video with Interactive Questions

Szpunar et al . compared the test performance of students who answered questions interpolated between ∼5 min video lectures and students who did unrelated arithmetic problems between the videos, finding that the students in the interpolated question group performed significantly better on subsequent tests of the material and reported less mind wandering ( Szpunar et al ., 2013 ). Students who received the interpolated questions also exhibited increased note taking, reported the learning event as less “mentally taxing,” and reported less anxiety about the final test. These results suggest that interpolated questions may improve student learning from video through several mechanisms. First, they may help to optimize cognitive load by decreasing extraneous load (i.e., anxiety about an upcoming test) and increasing germane load (i.e., note taking, reduced mind wandering). Further, interpolated questions may produce some of their benefit by tapping into the “testing effect,” in which recall of important information strengthens students’ memory of and ability to use the recalled information ( Roediger and Karpicke, 2006 ; Brame and Biel, 2015 ). Finally, interpolated questions may help students engage in more accurate self-assessment ( Szpunar et al ., 2014 ), an important benefit for a medium that students may perceive as “easier” than text. Tools like HapYak and Zaption can also allow instructors to embed questions directly into video and to give specific feedback based on student response. This approach has similar benefits to interpolated questions in increasing student performance on subsequent assessments ( Vural, 2013 ) and has the additional benefit of making the video interactive (see following section).

Use Interactive Features That Give Students Control

Zhang and colleagues compared the impact of interactive and noninteractive video on students’ learning in a computer science course ( Zhang et al ., 2006 ). Students who were able to control movement through the video, selecting important sections to review and moving backward when desired, demonstrated better achievement of learning outcomes and greater satisfaction. One simple way to achieve this level of interactivity is by using YouTube Annotate, HapYak, or another tool to introduce labeled “chapters” into a video. This not only has the benefit of giving students control but also can demonstrate the organization, increasing the germane load of the lesson.

Use Guiding Questions

Lawson and colleagues examined the impact of guiding questions on students’ learning from a video about social psychology in an introductory psychology class ( Lawson et al ., 2006 ). Building on work from Kreiner (1997) , they had students in some sections of the course watch the video with no special instructions, while students in other sections of the course were provided with eight guiding questions to consider while watching. The students who answered the guiding questions while watching the video scored significantly higher on a later test. Guiding questions may serve as an implicit means to share learning objectives with students, thus increasing the germane load of the learning task and reducing the extraneous load by focusing student attention on important elements. This strategy is often used to increase student learning from reading assignments (e.g., Tanner, 2012 ; Round and Campbell, 2013 ), and it can translate effectively to helping students learn from video.

Make Video Part of a Larger Homework Assignment

MacHardy and Pardos (2015) have developed a model relating educational video characteristics to students’ performance on subsequent assessments. One observation from their analysis of Khan Academy videos was that videos that offered the greatest benefits to students were highly relevant to associated exercises. This result is supported by results observed in a “teaching-as-research” project at Vanderbilt University (for background on teaching as research, see www.cirtl.net ). Specifically, Faizan Zubair participated in the BOLD Fellows program, in which graduate students develop online learning materials for incorporation into a faculty mentor’s course and then investigate their impact in teaching-as-research projects. Zubair developed videos on that were embedded in a larger homework assignment in Paul Laibinis’s chemical engineering class and found that students valued the videos and that the videos improved students’ understanding of difficult concepts when compared with a semester when the videos were not used in conjunction with the homework ( Zubair and Laibinis, 2015 ; see also Summary ).

The important thing to keep in mind is that watching a video can be a passive experience, much as reading can be. To make the most of our educational videos, we need to help students do the processing and self-evaluation that will lead to the learning we want to see.

Video may provide a significant means to improve student learning and enhance student engagement in biology courses ( Allen and Smith, 2012 ; Kay, 2012 ; Lloyd and Robertson, 2012 ; Rackaway, 2012 ; Hsin and Cigas, 2013 ; Stockwell et al ., 2015 ). To maximize the benefit from educational videos, however, it is important to keep in mind the three key components of cognitive load, elements that impact engagement, and elements that promote active learning. Luckily, consideration of these elements converges on a few recommendations:

Keep videos brief and targeted on learning goals.

Use audio and visual elements to convey appropriate parts of an explanation; consider how to make these elements complementary rather than redundant.

Use signaling to highlight important ideas or concepts.

Use a conversational, enthusiastic style to enhance engagement.

Embed videos in a context of active learning by using guiding questions, interactive elements, or associated homework assignments.

How to use Video Analysis in Education

The established video analysis processes that are used in professional sport are not necessarily sport-specific. They can also be applied to medical, instructional, and industrial settings. In this blog, I will share some examples and insight into how processes are being applied to enhance video analysis in education . The main aim of video analysis is to create resources that objectively display key information and facts about an activity that can be used to support and enhance the feedback process. When I deliver training sessions on our video analysis tools to a group that is either from sport or outside the world of sport, I always base it around three main steps: Get video in Break it down Get video out STEP 1 – GET VIDEO IN Whatever activity you are doing, as long as you have a video you can start a video analysis process. There are so many different ways to gather video now: affordable handycams, smartphones, tablets, professional cameras and also IP Cameras . This increased accessibility means that it is much easier to get started with using video analysis in education than you may think. Whatever your budget is, there will be an entry-level filming solution you can use. STEP 2 – BREAK IT DOWN

This is the part where we can really help you out. Before you start to break anything down, you have to establish what it is you would like to find within. Or perhaps, what you would not like to find within the video! Through establishing the things you would like to know more about (Key Performance Indicators), you can start to identify what actions need to be recorded and measured to get a picture of what is going on. In this process, we can help you to start building up the basis of a template of different actions to record. We then bring in tools like the Codimg video analysis software and Codimg View to put these templates together, so that you can use them to analyse your videos.

If you take a look at this previous blog , which is the first in a series of three, you can see more details about this template development process and what it looks like. The blogs detail our work with Exeter House School and Cardiff Metropolitan University , developing methods to assist with analysing Intensive Interaction sessions in special schools. Intensive Interaction is a method used with children who have Complex Learning Difficulties and Disabilities (CLDD). Since this first project, we have also established methods for assessing Engagement. This was done through a series of workshops with a group of schools that were exploring the use of technology in special schools. We are pleased to say that Exeter House School , Fountaindale School, and Heronsbridge School are now all using the Codimg video analysis software full time to enhance their teacher and pupil developments. So, back to the process. Once your initial template is established (it is likely to keep developing and changing as you use it more!), it’s now just a matter of watching through the video and pressing buttons as you see actions happen. The buttons can be set up to register a preset amount of time in the video when pressed, or you can have them running until you manually stop them registering. This results in you having a timeline for your video which is broken down into rows of different Categories. On each row, the moments you registered will show as a clip in the timeline. Extra information can also be associated with these clips, leading to you being able to use more analysis tools like a Matrix to get an overview of the whole video and what was occurring.

This is the process of breaking down the video and it’s something that can either be done live during an event or post-event. This is what we call real-time analysis or post-event analysis. It’s the important part where you can use video analysis in education settings to pick out those moments of gold dust to share with others. STEP 3 – GET VIDEO OUT By breaking down the video, you now have a record of that session which can be permanently stored and reviewed as many times as you want. By reviewing the clips in the timeline, you can start to get a full understanding of what happened. The next big step is to determine how this can impact performance, decisions or feedback to others. Within Codimg, there is a great presentation tool, which lets you grab clips from the timeline and assemble them into different lists. With your selected clips assembled, you can add notations, voiceovers, and drawings onto them to really highlight what you are seeing. The presentation you have put together can then be shown to others within the software, or you can export it as a video to share with others through online platforms for example. Showing a video of key moments to others is a great way to create discussions, reinforce your message, and enhance understanding and performance. Another resource you can utilise from the Codimg software is all the data you have created. You can create interactive dashboards that instantly give you an overview of the whole analysis, utilising charts, and numbers to display the information revealed during your BREAK IT DOWN phase. These can be saved out as a picture, but within the software, they give a unique method for reviewing the video. As you click on part of a chart or some numbers shown, it will show you the video clips which relate to that data.

HERE TO HELP Hopefully, the information above gives you an overview of video analysis in education processes and how they can be applied to any activity. We are here to help you achieve your goals and can assist in creating or developing existing video analysis workflows to apply to your environment. So please don’t hesitate to get in touch to find out more about video analysis, how it could help you, and how we could create bespoke solutions and workflows to suit your needs and requirements! (July 2020 Edit) – For a more recent case study looking at using video analysis within education, read this blog about the video coaching processes at Torquay Academy and find out more with the full online video webinar linked below, featuring Vice-Principal for Teaching and Learning, Harrison Littler, along with two teachers who share their feedback about receiving video coaching. Finally, we would like to say a big thank you to St John Fisher Catholic School who have chosen to work with us to enhance their teacher development processes. The school use a combination of Codimg licences and the Codimg View app when “developing their own”, and thanks to Headteacher Catherine Burnett, you can hear more about their workflows in this video.

• Case Studies
• Video Analysis

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Has Superintendent Turnover Gotten Any Better? What New Data Show

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One in five of the nation’s 500 largest school districts replaced their superintendent during the 2023-24 academic year, new data show.

The finding comes as school districts face significant challenges with financial stability, enrollment declines, staffing, and academic recovery. Turnover at the helm of school systems can slow those efforts as new leaders often introduce new strategies and require time to acclimate to the role, education leadership experts say.

The new data also show gender and racial disparities in district leadership. It comes from an analysis of superintendents in large districts released Sep. 10 by the ILO Group, a consulting firm that advocates for and provides support for women in educational leadership. (Julia Rafal-Baer, the co-founder and CEO of the ILO group, serves on the board of trustees for Editorial Projects in Education, Education Week’s publisher. Education Week retains full editorial control of its content.)

Here are three key findings from the new data:

1. Superintendent turnover remains higher than it was before the pandemic

One hundred of the nation’s 500 largest districts experienced superintendent turnover between July 1, 2023, and July 1, 2024, the analysis found. Three of those districts had two or more leadership changes in that time span.

That 20 percent turnover rate is slightly below last year’s rate, 21.4 percent. It remains above the 14 to 16 percent turnover rate estimated by organizations like AASA, the School Superintendents Association, in years past, the ILO report said.

State legislatures and leadership organizations have sought to improve turnover rates and make it easier to fill open positions in recent years.

In April, Wisconsin Gov. Tony Evers, a Democrat, vetoed a bill that would have waived state licensure requirements for superintendents. Republican supporters of the bill said it would help districts more easily locate candidates for open leadership roles but Evers, a former educator, said the requirements are necessary to ensure superintendents have needed skills for the complex roles.

State superintendents in Washington and Alaska have created peer mentoring programs, providing professional development on issues like budgeting and pairing new superintendents with veterans to provide practical advice and help them navigate the rocky adjustment period.

“The superintendency is an incredibly isolated position, and the demands are monumental,” Sean Dusek, who leads the mentorship program for the Alaska Council of School Administrators, told Education Week in 2023 . “With fewer and fewer people getting into the profession to begin with, we have to strengthen our current superintendents as much as we can and provide them with support so that they have job satisfaction and want to stay.”

2. While most teachers are women, most superintendents are men

Women led 152 of the 500 largest districts by the end of the 2023-24 school year, a number that did not change from the previous year, the data show. That’s particularly striking because 77 percent of teachers are women, according to the latest federal data.

“That zero growth-rate mirrors the steady state of women in top leadership positions in the private sector, including Fortune 500 CEOs,” the report said.

That means districts did not use turnover at the top as an opportunity to recruit new women leaders. Thirty-five of the districts with turnover selected female leaders, while 68 selected males, the data show.

Male superintendents were also less likely to hold a doctoral degree than their female counterparts, the data show.

Women who filled open leadership roles were more likely to be hired as internal candidates. And a majority of women superintendents in the analysis—138 of 152—were initially or currently employed as interim superintendents, suggesting districts’ recruitment and hiring practices could contribute to gender disparities.

In a March survey by Women Leading Ed, a network of superintendents that Rafal-Baer also leads, a majority of women in leadership roles reported that they believed that they had been passed over for leadership opportunities that were later given to male colleagues, and that their gender was a factor in salary negotiations.

3. White men are most likely to lead districts

The data also found racial disparities in district leadership, with women of color least likely to fill those roles.

Among the 500 largest districts:

• 220 are led by white men
• 128 are led by men of color
• 80 are led by white women
• 72 are led by women of color

The report recommends that school boards should be more transparent about the gender and race of candidates they consider for superintendent positions, set goals to promote diversity in leadership, and mentor current employees to take on leadership roles.

“Highlighting these issues is just the first step; we must take decisive action to dismantle these barriers,” Rafal-Baer said in a statement. “We need comprehensive policy changes to transform equality from a mere ideal into a tangible and integral part of our educational systems.”

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Education at a Glance 2024: Key Takeaways

Education at a Glance is the definitive guide to the state of education around the world. It gives policymakers the evidence to improve education systems by sharing comprehensive data on attainment, enrolment, finance, labour market outcomes and much more. This year’s edition focused on equity in education. In our post-launch webinar we’re delving deeper into EAG 2024 with Andreas Schleicher, OECD Director of Education and Skills, who will present some of the global results and analysis. Following his presentation, we hear from distinguished speakers, Natalia Miteva, Deputy Minister of Education and Science of Bulgaria, and João Costa, former Minister of Education of Portugal and current Chairperson of the OECD Education Policy Committee, who offer their unique perspectives. The session concludes with a live Q&A where we’ll also hear from Lucie Cerna, Senior Analyst at the Equity, Inclusion & Transitions project and get her thoughts on the importance of this year’s EAG focus, equity.

Additional tags

• Centre for Educational Research and Innovation (CERI)
• Education equity
• Education financing
• Teachers and educators
• Directorate for Education and Skills
• Gender equality in education
• Education organisation and governance
• Education access, participation and progression

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