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Brief research report article, the influence of attitudes and beliefs on the problem-solving performance.

• 1 Department of Mathematics and Computer Science, University of Education of Ludwigsburg, Ludwigsburg, Germany
• 2 Hamburg Center for University Teaching and Learning, University of Hamburg, Hamburg, Germany

The problem-solving performance of primary school students depend on their attitudes and beliefs. As it is not easy to change attitudes, we aimed to change the relationship between problem-solving performance and attitudes with a training program. The training was based on the assumption that self-generated external representations support the problem-solving process. Furthermore, we assumed that students who are encouraged to generate representations will be successful, especially when they analyze and reflect on their products. A paper-pencil test of attitudes and beliefs was used to measure the constructs of willingness, perseverance, and self-confidence. We predicted that participation in the training program would attenuate the relationship between attitudes and problem-solving performance and that non-participation would not affect the relationship. The results indicate that students’ attitudes had a positive effect on their problem-solving performance only for students who did not participate in the training.

Introduction

Mathematical problem solving is considered to be one of the most difficult tasks primary students have to deal with ( Verschaffel et al., 1999 ) since it requires them to apply multiple skills ( De Corte et al., 2000 ). It is decisive in this respect that “difficulty should be an intellectual impasse rather than a computational one” ( Schoenfeld, 1985 , p. 74). When solving problems, it is not enough to retrieve procedural knowledge and reproduce a known solution approach. Rather, problem-solving tasks require students to come up with new ways of thinking ( Bransford and Stein, 1993 ). Problem-solvers must activate their existing knowledge network and adapt it to the respective problem situation ( van Dijk and Kintsch, 1983 ). They have to succeed in generating an adequate representation of the problem situation (e.g., Mayer and Hegarty, 1996 ). This requires conceptual knowledge, which novice problem-solvers have to acquire ( Bransford et al., 2000 ). As problem solving is the foundation for learning mathematics, an important goal of primary school mathematics teaching is to strengthen students’ problem-solving performance. One central problem is that problem-solving performance is highly influenced by students’ attitudes towards problem solving ( Reiss et al., 2002 ; Schoenfeld, 1985 ; Verschaffel et al., 2000 ).

Attitudes and beliefs are considered quite stable once they are developed ( Hannula, 2002 ; Goldin, 2003 ). However, students who are novices in a particular content area are still in the process of development, as are their attitudes and beliefs. It can therefore be assumed that their attitudes change over time ( Hannula, 2002 ). However, such a change does not take place quickly ( Higgins, 1997 ; Mason and Scrivani, 2004 ). Nevertheless, in a shorter period of time, it might be possible to reduce the influence of attitudes on problem-solving performance ( Hannula et al., 2019 ). In this paper, we present a training program for primary school students, which aims to do exactly that.

Problem-Solving Performance

Successful problem solving can be observed on two levels: problem-solving success and problem-solving skills. Many studies measure the problem-solving performance of students on the basis of correctly or incorrectly solved problem-solving tasks, that is, the product (e.g., Boonen et al., 2013 ; de Corte et al., 1992 ; Hegarty et al., 1992 ; Verschaffel et al., 1999 ). In this case, only problem-solving success, that is, specifically whether the numerically obtained result is correct or incorrect, is evaluated. This is a strict assessment measure, since the problem-solving process is not taken into account. As a result, the problem-solving performance is only considered from a single, product-oriented perspective. For instance students’ performance is assessed as unsuccessful when they apply an essentially correct procedure or strategy but achieve the wrong result, or it is considered successful when students achieve the right result even though they have misunderstood the problem ( Lester and Kroll, 1990 ). An advantage of this operationalization, however, is that student performance tends to be underestimated rather than overestimated.

A more differentiated view of successful problem solving includes the solver’s problem-solving process ( Lester and Kroll, 1990 ; cf. Adibnia and Putt, 1998 ). In this way, sub-skills such as understanding the problem, adequately representing the situation, applying strategies, or achieving partial solutions are taken into account. These are then incorporated into the evaluation of performance and, thus, of problem-solving skills ( Charles et al., 1987 ; cf. Sturm, 2019 ). The advantage of this operationalization option is that it also takes into account smaller advances by the solver, although they may not yet lead to the correct result. It is therefore less likely to underestimate students’ performance. In order to assess and evaluate the problem-solving skills of students in the best way and, thus, avoid over- and under-estimating their skills, direct observation and questioning should be implemented (e.g., Lester and Kroll, 1990 ). An analysis of written work should not be the only means of assessment ( Lester and Kroll, 1990 ).

Attitudes and Beliefs

Attitudes are dispositions to like or dislike objects, persons, institutions, or events ( Ajzen, 2005 ). They influence behavior (Ajzen, 1991). Therefore, it is not surprising that attitudes–which are sometimes also synonymously referred to as beliefs–are a central construct in psychology ( Ajzen, 2005 ).

Individual attitudes to word problems influence, albeit rather unconsciously, approaches to such problems and willingness to learn mathematics and solve problems ( Grigutsch et al., 1998 ; Awofala, 2014 ). Research on attitudes of primary students to word problems is scarce. Most research focuses on students with well-established attitudes. However, the importance of the attitudes of younger children is undisputed ( Di Martino, 2019 ). Di Martino (2019) conducted a study on kindergarten children as well as on first-, third-, and fifth-graders and found that, with increasing age, students’ perceived competence in problem solving decreases, and negative emotions towards mathematical problems increase. Whether a solver can overcome problem barriers when dealing with word problems depends not only on his or her previous knowledge, abilities, and skills, but also on his or her attitudes and beliefs ( Schoenfeld, 1985 ; Verschaffel et al., 2000 ; Reiss et al., 2002 ). It has been shown many times that attitudes towards problem solving are influencing factors on performance and learning success which should not be underestimated ( Charles et al., 1987 ; Lester et al., 1989 ; Lester & Kroll, 1990 ; De Corte et al., 2002 ; Goldin et al., 2009 ; Awofala, 2014 ). Learners associate a specific feeling with an object, in this case with a word problem, triggering a specific emotional state ( Grigutsch et al., 1998 ). The feelings and states generated are subjective and can therefore vary between individuals ( Goldin et al., 2009 ).

Attitudes towards problem solving can be divided into willingness, perseverance, and self-confidence ( Charles et al., 1987 ; Lester et al., 1989 ). This distinction comes from the Mathematical Problem-Solving Project, in which Webb, Moses, and Kerr (1977) found that willingness to solve problems, perseverance in attempting to find a solution, and self-confidence in the ability to solve problems are the most important influences on problem-solving performance. When students are willing to work on a variety of mathematics tasks and persevere with tasks until they find a solution, they are more task oriented and easier to motivate ( Reyes, 1984 ). Perseverance is defined as the willing pursuit of a goal-oriented behavior even if this involves overcoming obstacles, difficulties, and disappointments ( Peterson and Seligman, 2004 ). Confidence is an individual’s belief in his or her ability to succeed in solving even challenging problems as well as an individual’s belief in his or her own competence with respect to his or her peers ( Lester et al., 1989 ). Students’ lack of confidence in themselves as problem-solvers or their beliefs about mathematics can considerably undermine their ability to solve or even approach problems in a productive way ( Shaughnessy, 1985 ). The division of attitudes into these three sub-categories can also be found in current studies ( Zakaria and Yusoff, 2009 ; Zakaria and Ngah, 2011 ).

Reducing the Influence of Attitudes and Beliefs

As it seems impossible to change attitudes within a short time frame, we developed a training program to reduce the influence of attitudes on problem solving, on the one hand, and to foster the problem-solving performance of primary-school students, on the other hand.

The training program was an integral part of regular math classes and focused on teaching students to generate and use external representations ( Sturm, 2019 ; Sturm et al., 2016 ; Sturm and Rasch, 2015 ; see also Supplementary Appendix A ). Such a program that concentrates on the strengths and weaknesses of novices and on their individually generated external representations can be a benefit for primary-school students in two ways. The class discusses how the structure described in the problem can be adequately represented so that the solution can be found, working out multiple approaches based on different student representations. The students are thus exposed to ideas about how a problem can be solved in different ways. Such a training program fulfils, albeit rather implicitly, another essential component. By respectfully considering their individual thoughts and difficulties, the students are made aware of their strengths and their creativity and of the fact that there is not a single correct approach or solution that everyone has to find ( Lester and Cai, 2016 ; Di Martino, 2019 ). This can counteract fears of failure and lack of self-confidence, and generate positive attitudes ( Lester and Cai, 2016 ; Di Martino, 2019 ). The teacher pays attention to the solution process rather than to the numerical result in order to reduce the influence of attitudes on performance ( Di Martino, 2019 ). In the same way, experiencing success and perceiving increasing flexibility and agility can reduce the influence of attitudes. As a result, we expected attitudes and beliefs to have a smaller effect on problem-solving performance.

Based on previous research, our goal was to reduce the influence of attitudes on the problem-solving performance of students (see Figure 1 ). To this end, the hypothesis was derived that participation in the training program would minimize the effect of attitudes and beliefs on problem-solving success, so that students would succeed at the end of the training despite initial negative attitudes and beliefs.

FIGURE 1 . The moderation model with the single moderator variable training influencing the effect of attitudes and beliefs on problem-solving success.

Participants

In total 335 students from 20 Grade 3 classes from eight different primary schools in the German state of Rhineland-Palatinate took part in the intervention study (172 boys and 163 girls). Nineteen students dropped out because of illness during the intervention. The age of the participants ranged between seven and ten years ( M = 8.10, SD = 0.47).

This investigation was part of a large interdisciplinary project 1 . A central focus of the project was to investigate whether representation training has a demonstrable effect on the performance of third-graders (cf. Sturm, 2019 ). For this reason, we implemented a pretest-posttest control group design. The intervention took place between Measurement Points 1 and 2. We measured the problem-solving performance of the students with a word-problem-solving test (WPST) at Measurement Points 1 and 2. All other variables were measured at Measurement Point 1 only (factors to establish comparable experimental conditions: intelligence, text comprehension, and mathematical abilities; co-variates for the mediation model: metacognitive skills, mathematical abilities).

In the intervention, third-grade students worked on challenging word problems for one regular mathematics lesson a week. The intervention was based on six task types with different structures ( Sturm and Rasch, 2015 ): 1) comparison tasks, 2) motion tasks, 3) tasks involving comparisons and balancing items or money, 4) tasks involving combinatorics, 5) tasks in which structure reflects the proportion of spaces and limitations, and 6) tasks with complex information. Two word problems were included for each task type and were presented to all classes in the same random sequence. Each task had to be completed in a maximum of one lesson.

The training was implemented for half of the classes and was conducted by the first author; the other half worked on the tasks with their regular mathematics teacher. They were not informed on the purpose of the intervention and not given any instructions on how to process the tasks. In the lessons for students doing the training, the students were explicitly cognitively stimulated to generate external representations and to use them to develop solutions. They were repeatedly encouraged to persevere and not to give up. The diverse external representations generated by the students were analyzed, discussed, and compared by the class during the training. They jointly identified the characteristics of representations that enabled them to specifically solve the tasks and identified different approaches (for more details about the study, see Sturm and Rasch, 2015 ). With the goal of reducing the influence of attitudes on performance, the class worked directly on the students’ own representations instead of on prefabricated representations. The aim was that students realized that it was worthwhile investing effort into creating representations and that they were able to solve problem tasks independently.

Thus, the study was composed of two experimental conditions: training program ( n = 176; 47% boys) (hereinafter abbreviated to T+) and no training program ( n = 159; 58% boys) (hereinafter abbreviated to T-). In order to control potential interindividual differences, the 20 classes were assigned to the experimental conditions by applying parallelization at class level ( Breaugh and Arnold, 2007 ; Myers and Hansen, 2012 ). The classes were grouped into homogeneous blocks using the R package blockTools Version 0.6-3 and then randomly assigned to the experimental conditions ( Greevy et al., 2004 ; Moore, 2012 ; see also Supplementary Appendix B for more information).

Word-Problem-Solving Test

Before the intervention and immediately after it, the students worked on a WPST, which we created. It consisted in each case of three challenging word problems with an open answer format. Each of the three tasks represented a different type of problem. The word problems from the WPST at Measurement Point 1 and the word problems from the WPST at Measurement Point 2 had the same structure. We implemented two parallel versions; only the context was changed by exchanging single words (see Supplementary Appendix C ). An example of an item from the test is a task with complex information ( Sturm, 2018 ): Classes 3a and 3b go to the computer room. Some students have to work at a computer in pairs. In total there are 25 computers, but 40 students. How many students work alone at a computer? How many students work at a computer in pairs? Direct observation and questioning could not be conducted due to the large number of participants in the project; only the students’ written work was available for analysis. The problem-solving process of the students could therefore only be assessed indirectly. For this reason, the performance of students in the two tests was evaluated based on problem-solving success, ruling out overestimation of performance.

Problem-Solving Success

The success of the solution was measured dichotomously in two forms: 1) correct solution and (0) incorrect solution. Only the correctness of the result achieved was evaluated. This dependent variable acted as a strict criterion that could be quantified with high observer agreement (κ = 0.97; κ min = 0.93, κ max = 1.00). A confirmatory factor analysis using the R package lavaan version 0.6-7 confirmed that the WPST measured the one-dimensional construct problem-solving success. The one-dimensional model exhibited a good model fit ( Nussbeck et al., 2006 ; Hair et al., 2009 ): χ 2 (27) = 36.613, p = 0.103; χ 2 /df = 1.356, CFI = 0.985, TLI = 0.981, SRMR = 0.032, RMSEA = 0.033 ( p = 0.854). The reliability coefficients at Measurement Point 1 were classified as low (Cronbach’s α = 0.39) because the test consisted of only three items ( Eid et al., 2011 ) and a homogeneous sample was required at this measurement point ( Lienert and Raatz, 1998 ). The Cronbach’s alpha for the second measurement point (α = 0.60) was considered to be sufficient ( Hair et al., 2009 ). The test score represented the mean value of all three task scores.

Attitudes and Beliefs About Problem Solving

The attitudes and beliefs of the learners were recorded with the Attitudes Inventory Items ( Webb et al., 1977 ; Charles et al., 1987 ). The original questionnaire comprises 20 items, which are measured dichotomously (“I agree” and “I disagree”). The Attitudes Inventory measures the three categories of attitudes and beliefs related to problem solving: a) willingness (six items), b) perseverance (six items), and c) self-confidence (eight items). An example of an item for willingness is: “I will try to solve almost any problem.” An example of an item for perseverance is: “When I do not get the right answer right away, I give up.” An example of an item for self-confidence is: “I am sure I can solve most problems.”

Because the reported reliabilities were only satisfactory to some extent (α = 0.79, mean = 0.64) ( Webb et al., 1977 ), the Attitudes Inventory was initially tested on a smaller sample ( n = 74; M = 8.6 years old; 59% girls). A satisfactory Cronbach’s α = 0.86 was achieved (mean α = 0.73). The number of items was reduced to 13 (four items for willingness, four items for perseverance, five items for self-confidence), which had only a minor influence on reliability (α = 0.83). For economic reasons, the shortened questionnaire was used in the study. The three-factor structure of the questionnaire was confirmed with a confirmatory factor analysis using the R package lavaan version 0.6–7. As the fit indices show, the three-factor model had a good model fit: χ 2 (62) = 134.856, p < 0.001; χ 2 / df = 2.175, CFI = 0.948, TLI = 0.935, RMSEA = 0.062 ( p = 0.086) ( Hair et al., 2009 ; Brown, 2015 ). The three-factor model had a better fit than the single-factor model ( p = 0.0014): χ 2 (65) = 152.121, p < 0.001; χ 2 / df = 2.340, CFI = 0.938, TLI = 0.926, SRMR = 0.061, RMSEA = 0.066 ( p = 0.028). The students were grouped into three groups ( M –1 SD ; M ; M +1 SD ). The responses were coded in such a way that high scores ( M +1 SD ) indicated positive attitudes and beliefs, and low scores ( M –1 SD ) indicated negative attitudes and beliefs.

Additional Influencing Factors

In order to ensure the internal validity of the investigation, we collected student-related factors that influence the solution of word problems from a theoretical and empirical point of view. It has been shown that the mathematical abilities and metacognitive skills of students significantly influence their performance ( Sturm et al., 2015 ).

Mathematical Abilities

The basic mathematical abilities were determined using a standardized German-language test as a group test (Heidelberger Rechentest HRT 1–4, Haffner et al., 2005 ). The test consists of eleven subtests, from which three scale values were determined: calculation operations, numerical-logical and spatial-visual skills as well as the overall performance for all eleven subtests. The reliability was only satisfactory (Cronbach’s α = 0.74). Total performance was included in the study.

Metacognitive Skills

The metacognitive skills of the students were measured using a paper-pencil version of EPA2000, a test to measure metacognitive skills before and/or after the solving of tasks ( Clercq et al., 2000 ). The prediction skills and evaluation skills of the students were collected for all three word problems of the WPST using a 4-point rating scale: 1) “absolutely sure, it’s wrong,” 2) “sure, it’s wrong,” 3) “sure, it’s right,” and 4) “absolutely sure, it’s right” ( Clercq et al., 2000 ). If the students’ assessments of “absolutely sure” matched their solution, they were awarded 2 points. If they agreed with “sure,” they received 1 point. No match was scored with 0 points ( Desoete et al., 2003 ). The reliabilities were only satisfactory (Cronbach’s α total =0.74, α prediction =0.56, α evaluation = 0.73). A confirmatory factor analysis revealed that prediction skills and evaluation skills represent a single factor (χ 2 (9) = 16.652, p < 0.001; χ 2 / df = 1.850, CFI = 0.952, TLI = 0.919, RMSEA = 0.053 ( p = 0.396)). The aggregated factor was used as a control variable in the moderator analysis.

In addition to the variables considered in this paper, text comprehension and intelligence were also surveyed in the project. However, they are not the focus of this paper; additional information can be found in Sturm et al. (2015) .

Descriptive Statistics and Correlations Between the Measures

The descriptive statistics and correlations of all scales are presented in Table 1 (see Supplementary Appendix D for a separate overview for each of the experimental conditions). The signs for all correlations were as expected. The variable training program is not listed because it is the dichotomous moderator variable (T+ and T−).

TABLE 1 . Descriptive statistics and correlations of all variables for both experimental conditions.

Moderated Regression Analyses

The hypothesis was tested with a moderated regression analysis using product terms from mean-centered predictor variables ( Hayes, 2018 ). This model imposed the constraint that any effect of attitudes and beliefs was independent of all other variables in the model. This was achieved by controlling for mathematical abilities, metacognitive skills, and problem-solving performance at Measurement Point 1. The estimated main effects and interaction terms are presented in Table 2 .

TABLE 2 . Results from the regression analysis examining the moderation of the effect of attitudes and beliefs on problem-solving success (t 2 ) by participation in the training program, controlling for mathematical abilities, metacognitive skills, and problem-solving success from the pretest.

When testing the hypothesis, we found a significant main effect of attitudes and beliefs, a significant main effect of the training program, and a significant moderator effect of the training on attitudes and beliefs as a predictor of problem-solving success. The main effect of the training program indicated that students who participated in the training performed better in the second WPST. The main effect of attitudes and beliefs showed that students with more positive attitudes and beliefs were more successful than students with negative attitudes and beliefs.

To further explore the interaction between attitudes and beliefs and the training program, we analyzed simple slopes at values of 1 SD above and 1SD below the means of attitudes and beliefs ( Hayes, 2018 ). As can be seen from the conditional expectations in Figure 2 , attitudes and beliefs did not affect the problem-solving success of students who participated in the training program. Attitudes and beliefs only had a positive effect on the problem-solving success of students who did not participate in the training.

FIGURE 2 . Moderator effect of the training program on problem-solving success at Measurement Point 2.

Our results confirm previous findings that the attitudes and beliefs of students correlate with their problem-solving performance. They indicate that this correlation can be moderated by student participation in a training program. Negative attitudes and beliefs did not affect the performance of students who participated in a problem-solving training program over several weeks. Whether the training program also causes a change in the attitudes and beliefs of the students over time has to be investigated in a follow-up study, which is planned with a longer intervention period with at least two measurements of attitudes and beliefs. A longer intervention period would have the advantage that attitudes develop depending on the individual experiences of a person ( Hannula, 2002 ; Lim and Chapman, 2015 ), for instance, when new experience is gathered or new knowledge is acquired (e.g., Ajzen, 2005 ).

Some limitations need to be considered when interpreting the results of the study. For example, the mitigating processes need to be investigated further. It is also unclear as to which components of the training are ultimately responsible for counteracting the effect of attitudes and beliefs. Although the study did not provide results in this regard, we assume that the following factors might have an effect: generating external representations, reflecting on the representations together as a group, and fostering an appreciative and constructive approach to mistakes. Further studies are needed to show whether and to what extent these factors actually attenuate the effect of attitudes and beliefs.

Furthermore, the measurement instruments for the control variables mathematical abilities and metacognitive skills were rather limited. If researchers are interested in understanding further effects of metacognitive skills, more aspects should be included. Furthermore, according to Lester et al. (1987), investigating attitudes and beliefs using a questionnaire is associated with disadvantages. How accurately students answer the questions depends on how objectively and accurately they can reflect on and assess their own attitudes. Misinterpretations and errors cannot be ruled out. The most serious disadvantage, however, is that data collection using an inventory can easily be assumed to have unjustified validity and reliability. For a deeper insight into the attitudes and beliefs of primary school students, qualitative interviews have to be implemented.

However, for the purpose of this study, it seems sufficient to consider the two control variables mathematical abilities and metacognitive abilities. We were able to ensure that the correlation between attitudes and beliefs and the mathematical performance of students was not influenced by these factors.

Regardless of the limitations, our study has some practical implications. Participation in the training program, independently of the mathematical abilities and text comprehension of students, reduced the influence of attitudes and beliefs on their performance. Thus, for teaching practice, it can be concluded that it is important not only to implement regular problem-solving activities in mathematics lessons, but also to encourage students to externalize and find their own solutions. The aim is to establish a teaching culture that promotes a variety of approaches and procedures, allows mistakes to be made, and makes mistakes a subject for learning. Reflecting on different possible solutions and also on mistakes helps students to progress. Thus, students develop a repertoire of external representations from which they can profit in the long term when solving problems.

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by the Ethics Committee of the Department of Psychology, University of Koblenz and Landau, Germany. Written informed consent to participate in this study was provided by the participants' legal guardian. This study was also carried out in accordance with the guidelines for scientific studies in schools in the German state Rhineland-Palatinate (Wissenschaftliche Untersuchungen an Schulen in Rheinland-Pfalz), Aufsichts- und Dienstleistungsdirektion Trier. The protocol was approved by the Aufsichts- und Dienstleistungsdirektion Trier.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

The project was funded by grants from the Deutsche Forschungsgemeinschaft (DFG, grant number GK1561/1).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2021.525923/full#supplementary-material

1 This project was part of the first author’s PhD thesis

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Keywords: attitudes and beliefs, word problem, training program design, problem-solving, problem-solving success, primary school, moderation effect analysis

Citation: Sturm N and Bohndick C (2021) The Influence of Attitudes and Beliefs on the Problem-Solving Performance. Front. Educ. 6:525923. doi: 10.3389/feduc.2021.525923

Received: 21 May 2020; Accepted: 18 January 2021; Published: 17 February 2021.

Reviewed by:

Copyright © 2021 Sturm and Bohndick. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Nina Sturm, [email protected]

This article is part of the Research Topic

Psychology and Mathematics Education

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Relationship Among Students’ Problem-Solving Attitude, Perceived Value, Behavioral Attitude, and Intention to Participate in a Science and Technology Contest

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The strong humanistic and ethics-oriented philosophy of Confucianism tends to lead people influenced by these principles to undervalue the importance of hands-on practice and creativity in education. GreenMech, a science and technology contest, was implemented to encourage real-world, hands-on problem solving in an attempt to mitigate this effect. The self-reported attitudes, values, and intentions of 684 GreenMech participants from elementary, junior high, and senior high schools in Taiwan were subjected to confirmatory analysis with structural equation modeling to test the hypothesized model. The research findings revealed that the students’ problem-solving attitude is positively correlated to their perception of their own knowledge enrichment and thinking-skill enhancement as a result of participating in GreenMech. The findings also suggest that these perceived advantages positively influenced the intention to participate in future contests. This indicates that a highly competitive contest can be used to promote awareness of opportunities, which may enhance thinking skills and enrich knowledge.

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Acknowledgments

This research is partially supported by the “Aim for the Top University Project” sponsored by the Ministry of Education and the Ministry of Science and Technology, Taiwan, ROC, under Grant no. MOST 104-2911-I-003-301 and NSC 102-2511-S-003-030-MY2. The authors would like to express their appreciation to Dr. Todd Milford, University of Victoria, and mentors Professor Larry D. Yore and Shari Yore for their assistance in preparing the report of this research study.

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Huang, NT.N., Chiu, LJ. & Hong, JC. Relationship Among Students’ Problem-Solving Attitude, Perceived Value, Behavioral Attitude, and Intention to Participate in a Science and Technology Contest. Int J of Sci and Math Educ 14 , 1419–1435 (2016). https://doi.org/10.1007/s10763-015-9665-y

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Relationship between students’ attitude towards, and performance in mathematics word problems

Robert Wakhata

1 University of Rwanda, College of Education, Rwamagana, Rwanda: African Centre of Excellence for Innovative Teaching and Learning Mathematics and Science (ACEITLMS), Rwamagana, Rwanda

Sudi Balimuttajjo

2 Department of Educational Foundations and Psychology, Mbarara University of Science and Technology, Mbarara, Uganda

Védaste Mutarutinya

3 University of Rwanda, College of Education, Rwamagana, Rwanda

Associated Data

The data is freely available and accessible to explore and reuse. Repository name: Mendeley. Data identification number: doi: 10.17632/xc2xj3rwp9.1 . Direct URL to data: https://data.mendeley.com/datasets/xc2xj3rwp9/1 . Supplementary material associated with this article can be found, in the online version, at https://doi.org/10.1016/j.dib.2023.109055 .

The study explored the relationship between students’ attitude towards, and performance in mathematics word problems (MWTs), mediated by the active learning heuristic problem solving (ALHPS) approach. Specifically, this study investigated the correlation between students’ performance and their attitude towards linear programming word tasks (ATLPWTs). Tools for data collection were: the adapted Attitude towards Mathematics Inventory-Short Form (ATMI-SF), (α = .75) as a multidimensional measurement tool, and linear programming achievement tests (pre-test and post-test). A quantitative approach with a quasi-experimental pre-test, post-test non-equivalent control group study design was adopted. A sample of 608 eleventh-grade Ugandan students (291 male and 317 female) from eight secondary schools (both public and private) participated. Data were analyzed using PROCESS macro (v.4) for SPSS version 26. The results revealed a direct significant positive relationship between students’ performance and their ATLPWTs. Thus, students’ attitude positively and directly impacted their performance in solving linear programming word problems. The present study contributes to the literature on performance and attitude towards learning mathematics. Overall, the findings carry useful practical implications that can support theoretical and conceptual framework for enhancing students’ performance and attitude towards mathematics word problems.

1.0 Introduction

1.1 the concept of students’ attitude towards mathematics.

The term attitude is the most indispensable concept in contemporary social psychology and science. It is related to emotional and mental entities that drive an individual towards performing a particular task [ 1 ]. According to [ 2 ], attitude is “a learned disposition or tendency on the part of an individual to respond positively or negatively to some object, situation, concept or another person” (p. 551). [ 3 ], define attitude towards mathematics as positive, negative, or neutral feelings and dispositions. Attitude can be bi-dimensional, (a person’s emotions and beliefs) or multidimensional (affect, behavior, and cognition). Over the last decades, an extensive body of research from different settings and contexts has investigated variables that influence students’ attitude towards Science, Technology, Engineering and, Mathematics (STEM) (e.g., [ 2 , 4 , 5 ]). This means that attitude determines and may be used as a predictor for academic achievement. In this study, we are particularly concerned with an exploration of the effect of the heuristic problem-solving approach on students’ attitude towards learning mathematics, and the topic of LP in particular. This is due to significant roles LP play in constructing elementary and advanced models for understanding science, technology and engineering (STE).

Numerous studies on students’ attitude towards mathematics which is always translated as liking and disliking of the subject have been published (e.g., [ 6 – 9 ]). To some secondary school students, mathematics appears abstract, difficult to comprehend, boring and viewed with limited relationship or relevance to everyday life experiences. Students start learning the subject well but gradually start disliking some topics or the entire subject. They feel uncomfortable and nervous during learning and examinations. This is partly attributed to students’ lack of self-confidence, and motivation during problem-solving. To some students, persevering and studying advanced mathematics has become a nightmare. Indeed, some students do not seem to know the significance of learning mathematics beyond the compulsory level. Students may (may not) relate mathematical concepts beyond the classroom environment if they have a negative attitude towards the subject. This may lead to their failure to positively transfer mathematical knowledge and skills in solving societal problems.

Mathematicians have attempted to research and understand affective variables that significantly influence students’ attitude towards mathematics (e.g., [ 7 , 10 – 20 ]). Some researchers and authors have gone ahead to ask fundamental questions on whether or not students’ attitude towards mathematics is a general phenomenon or dependent on some specific variables. To this effect, some empirical findings report students’ attitudes towards specific units in mathematics to enhance the learning of that specific content and mathematics generally. (e.g., [ 6 , 21 – 26 ]).

Rather than investigating students’ general attitudes toward mathematics, recent research has also attempted to identify background factors that may provide a basis for understanding students’ attitude towards and performance in learning mathematics. Thus, students at different academic levels may have negative or positive attitude towards mathematics due to fundamentally different reasons. Yet, some empirical studies have shown existence of significant relationships between students’ attitude and performance in mathematics (e.g., [ 7 , 19 , 27 – 36 ]). From the above studies, it appears multiple factors for instance students’ demographic characteristics, teachers, parents, and the classroom instructional practices influence students’ attitude towards learning mathematics.

This paper presents results from a more specific investigation into the relationship between students’ attitude towards, and performance in linear programming mathematics word tasks (Appendix 1 in S1 File ). This is because studies concerning attitudes towards and achievement in mathematics have begun to drift from examining general mathematics attitudes to a more differentiated conceptualization of specific students’ attitude formations, and in different units (topics). Although different attitudinal scales (e.g., [ 2 , 34 , 37 ]) were developed to measure different variables influencing students’ attitude towards mathematics, this study investigated the influence of some of these constructs on students’ performance in linear programming. According to the above authors (and other empirical findings), students’ attitude towards mathematics may be the consequence of general and specific latent factors. Thus, attitude towards learning LP word tasks was investigated with specific reference to students’ performance, controlling for other variables e.g., learning strategies, students’ gender, school type, and school location.

1.2 Mathematics word problems

[ 38 ] define word problems as “verbal descriptions of problem situations wherein one or more questions have raised the answer to which can be obtained by the application of mathematical operations to numerical data available in the problem statement.” The authors categorized word problems based on their inclusion in real-life world scenarios. Thus, mathematics word problems play significant roles in equipping learners with the basic knowledge, skills, and, understanding of problem-solving and mathematical modeling. Some empirical findings (e.g., [ 39 ]) show that mathematics word problems link school mathematics to real-life world applications. However, the learning of mathematics word problems and related algebraic concepts is greatly affected by students’ cognitive and affective factors [ 18 , 40 ]). Mathematics word problems are an area where the majority of students experience learning obstacles [ 23 , 40 – 47 ]. By contrast, comprehension of mathematics word problems mainly accounts for students’ difficulties. Consequently, this has undermined students’ competence, confidence and achievement in word problems and mathematization in general.

Yet, mathematical word problems are intended to help learners to apply mathematics beyond the classroom in solving real-life-world problems. [ 38 , 39 ] have argued that mathematics word problems are difficult, complex, and pause comprehension challenges to most learners. This is because word problems require learners to understand and adequately apply previously learned basic algebraic concepts, principles, rules and/or techniques. Indeed, most learners find it difficult to understand the text in the word problems before transformation into models. This is partly due to variation in their comprehension abilities and language [ 48 ]. Consequently, learners fail to write required mathematical algebraic symbolic operations and models. Yet, incorrect models lead to wrong algebraic manipulations and consequently wrong graphical representations and solutions.

Notably, research findings by [ 12 ] indicate that students’ mathematical proficiency is partly explained by their transitional epistemological and ontological challenges from primary to secondary education. This may consequently affect their attitude towards mathematics during the transition from secondary to tertiary [ 11 ]. The authors attribute this trend to mainly three dimensions (emotional disposition, vision of mathematics and perceived competence). Other studies (e.g., [ 28 , 49 – 51 ] attribute students’ performance and achievement in mathematics to gender differences. Research by [ 52 ] shows that the challenges experienced in mathematics education are a by-product of those in education in general, and they span from policy, curriculum, instruction, learning, and information technology to infrastructure. Thus, students may start excellently learning and performing mathematics from primary but gradually lose interest in some specific units and finally in mathematics generally. Several strategies have been adopted and/or adapted to boost students’ attitude in specific topics. For the case of LP, students’ attitude towards mathematics and in equations and inequalities in particular gradually drop in favor of other presumably simpler topics. To boost performance in mathematics word problems, [ 44 ] proposed step-by-step problem-solving strategies to enhance mastery and performance.

Students’ attitudes should, therefore, be investigated as well as their influence on their conceptual changes. Several empirical studies have also investigated the relationship between attitude towards, and achievement in mathematics across all levels, and in different contexts (e.g., [ 7 , 9 , 20 , 24 , 53 – 61 ]). In particular, these studies generally focused on students’ attitude towards mathematics, and many of them from the western context [ 62 ]. Yet, students may have different perceptions and attitudes towards specific content (topics) in mathematics irrespective of their setting, context and the learning environment.

Several authors (e.g., [ 63 – 68 ]) have highlighted numerous difficulties encountered by students in solving algebraic inequalities. These difficulties include transformation of mathematics word text into models, wrong graphical representations, wrong optimal solutions; etc. Thus, a combination of methods (strategies) rather than one specific method [ 69 ] (e.g., heuristics, problem solving, group discussions, giving frequent exercises, etc.) can be applied to minimize specific students’ learning challenges. To enhance mathematical conceptual proficiency, educators should boost students’ cognitive and affective domains in specific mathematics content. This is because students’ affective domain may directly influence their cognitive and psychomotor domains. In this study, it was predicted that students’ proficiency in solving LP word tasks is largely influenced by their attitude and inadequate prior algebraic knowledge, skills, and experiences. [ 23 ] noted that prior conceptual understanding coupled with students’ attitudes towards solving algebraic concepts impacted students’ inherent procedures in writing relational symbolic mathematical models from word problems, and provision of correct numerical solutions.

1.3 Attitude towards mathematics word problems

Improving students’ achievement and attitude in mathematics is the recent global talk and practice in the 21st century education systems. Educational stakeholders seem to agree in unison that learning experiences, achievement and success in mathematics is a function of attitude [ 70 ]. Researchers have argued that learners’ positive attitude toward mathematics is key. Fostering a positive attitude towards mathematics means making the learning of different topics, related mathematical concepts and experiences positive. Whether it is classroom learning activities, homework, practice, or a test, it means supporting and encouraging learners to perform the assigned tasks so that they are motivated, feel confident about their mathematical skills, and during problem-solving. This is because, learning mathematics goes beyond memorization and passing examinations in a school setting. It is about going an extra mile to solve problems, and this helps greatly in applying mathematics outside the classroom environment in real-life scenarios. Thus, as a child grows and steps into the real world, mathematics helps a great deal in achieving the skill of decision making and problem-solving.

In relation to achievement, a positive attitude towards mathematics increases learner’s odds to select mathematics courses in high school and beyond, especially in making mathematics related career choices. A number of researchers endeavored to formulate a valid and reliable measure of mathematics attitude. Some of the attitudinal scales were measured uni-dimensionally while others multidimensionally (e.g., [ 2 , 33 ] developed scales designed to measure enjoyment and value of mathematics. This led to Fennema-Sherman Attitudinal scales. According to [ 34 ], this tool has been adapted to measure students’ attitude in mathematics education research in different contexts and settings for the last three decades. Specifically, the Fennema-Sherman Mathematics Attitude Scales consist of a group of nine instruments with 108 items. These subscales include (1) attitude toward success in mathematics scale, (2) mathematics as a male domain scale, (3) and (4) mother/father scale, (5) teacher scale, (6) confidence in learning mathematics scale, (7) anxiety in mathematics scale, (8) motivation in mathematics scale, and (9) value (usefulness) in mathematics scale.

The attitude towards mathematics inventory (ATMI) measures attitude in terms of four sub-scales (self-confidence, enjoyment, motivation, and value). Self-confidence sub-scale assesses the level of confidence to do mathematics; enjoyment taps the extent to which a student enjoys doing mathematics; motivation refers to the level of motivation to do mathematics; and value assesses the extent to which the student attaches value to doing mathematics. The ATMI has a strong construct validity and reliability [ 71 ]. Moreover, recent findings (e.g., & [ 72 , 73 ]) have revealed a strong positive relationship between students’ attitude towards, and achievement in mathematics.

1.4 Linear programming

The history of linear programming is dated as far back as in the 1940s. The concept of linear programming arose due to the need to find solutions to complex planning problems during World wartime operations [ 74 ]. Linear programming was used extensively during World War II to deal with the transportation, scheduling, and allocation of resources subject to certain restrictions (e.g., costs and availability). Today, linear programming is still being applied in business and industry, specifically in production planning, transportation and routing, and in various types of scheduling. In addition, and to date, airlines use linear programs to schedule their flights, taking into account both scheduling aircraft and scheduling staff. Developed by [ 74 ], the simplex method was efficiently applied to solve linear programming problems. Later, the Neumann, also established the theory of duality to complement the simplex method. Presently, the above methods of solving linear programming problems are still being taught to students at university and other tertiary institutions.

Linear programming is also one of the topics in Ugandan secondary school mathematics curriculum. It is one of the topics that require students’ understanding of basic mathematical algebraic principles and rules. It is a basic introduction to other advanced methods for solving and optimizing LP problems. At secondary school level, solving LP problems is a classical unit, “the cousin” of mathematics word problems which has gained significant applications in the last decades in mathematics, science, and technology [ 75 – 79 ]. This is because LP links theoretical to practical mathematical applications. The topic provides elementary modeling skills for later applications in modelling.

Previous empirical studies have revealed that LP and/or related concepts are not only difficult for learners but also challenging to teach [ 40 – 42 , 44 , 80 ]. Different cognitive factors account for learners’ challenges in mathematics word problems [ 81 , 82 ]. The learning challenges mainly stem from students’ difficulties in comprehending mathematics word problem statements, application of suitable algebraic principles, negative attitude, and transformation from conceptual to procedural knowledge and understanding [ 18 , 38 , 40 – 42 ]. Learners’ attitudes toward solving mathematics word problems should, therefore, be investigated and integrated during classroom instruction to help educational stakeholders provide appropriate and/or specific instructional strategies.

1.5 The Ugandan context

In Uganda, studies on predictors of students’ attitude towards science and mathematics are scanty. There seems to be no recent empirical findings on students’ attitude or performance in mathematics and mathematics word problems in particular. Solving LP tasks (by graphical method) is one of the topics taught to the 11th grade Ugandan lower secondary school students [ 83 , 84 ]. Despite students’ general and specific learning challenges in mathematics, the objectives of learning LP are embedded within the aims of the Ugandan lower secondary school mathematics curriculum. Some of the specific aims of learning mathematics in Ugandan secondary schools include…enabling individuals to apply acquired skills and knowledge in solving community problems, instilling a positive attitude towards productive work …” [ 84 ]. Generally, the learning of LP word problems aims to develop students’ problem-solving abilities, application of prior algebraic conceptual knowledge and understanding of linear equations and inequalities in writing models from word problems, and from real-life-world problems. Despite the learning challenges, the topic of LP is aimed at equipping learners with adequate knowledge and skills for doing advanced mathematics courses beyond the compulsory level at Uganda Certificate of Education (UCE).

However, every academic year, the Uganda National Examinations Board (UNEB) highlights students’ strengths and weaknesses in previous examinations at UCE. The consistent reports [ 85 – 88 ] on previous examinations on work of candidates show that students’ performance in mathematics is not satisfactory especially at distinction level. In particular, previous examiners’ reports show students’ poor performance in mathematics word problems. The examination reports further revealed numerous students’ specific deficiencies in the topic of LP (Appendix 1 in S1 File ). Students’ challenges in LP mainly stem from comprehension of word problems to formation of wrong linear equations and inequalities (in two dimensions) from the given word problem in real-life situations. Thus, wrong models derived from questions may result in incorrect graphical representations, and consequently wrong solutions and interpretations of optimal solutions. These challenges (and others) may consequently hinder and/or interfere with students’ construction of relevant models in science, mathematics and technology. Moreover, learners have consistently demonstrated cognitive obstacles in answering questions on LP, while others elude these questions (Appendix 1 in S1 File ) during national examinations. Noticeably absent in all the UNEB reports are factors that account for students’ weaknesses in learning LP and the specific interventions to overcome students’ challenges in LP. Some students have developed a negative attitude towards the topic. Yet, students’ attitudes may directly impact on their learning outcomes [ 37 ].

Several attitudinal scales (with both cognitive and behavioral components) have been developed [ 61 , 89 ] adopted or adapted [ 3 ] to assess students’ attitude towards mathematics and in specific mathematics content. For instance, Geometry Attitude Scales [ 90 ], Statistics Attitude Scales [ 57 , 91 ], Attitudes toward Mathematics Word Problem Inventory [ 40 ], the Attitude Towards Geometry Inventory (ATGI) instrument [ 92 ], and others. In this study, we adapted the Attitude towards Mathematics Inventory-Short Form (ATMI-SF) instrument [ 3 ] to investigate the effect of the heuristic problem-solving approach on students’ attitude towards learning LP word problems. Specifically, this research explored the 11th-grade students’ attitude towards learning LP word problems (see Appendix 1 in S1 File ). Taken together, research shows that a high percentage of educational stakeholders around the world are concerned about performance and attitude towards mathematics word tasks in particular. However, to fully understand students’ attitude towards and performance in mathematics, it is necessary to investigate beyond general mathematics attitudes and examine specific underlying aspects for these attitudes.

1.6 Objectives of the study

The purpose of this study was to explore the relationship between students’ attitude towards, and performance in LP mathematics word problems. Specifically, the present study aimed at:

• -Investigating the direct effect of students’ attitude towards, and performance in mathematics word problems.
• -Exploring the indirect effect of students’ attitude towards, and performance in LP word tasks mediated by active learning heuristic problem solving strategies.

1.7 The Theoretical framework

This study was situated on the theoretical framework according to constructivism, and Eccles, Wigfield, and colleagues’ expectancy-value model of achievement motivation [ 93 ]. The expectancy-value model is based on the expectancy-value theories of achievement. The theory combines the motivational components of competency beliefs, importance of the subject, and utilitarian beliefs, and focuses on both the role of students’ beliefs about their competence and the value they place on the activity. The Expectancy-Value Theory is a theory of motivation that describes the relationship between a student’s expectancy for success at a task or the achievement of a goal about the value of task completion or goal attainment.

Expectancy refers to a student’s expectation for success on a given task (in this case, LP tasks). In addition, the social constructivist theory guided the epistemological and pedagogical perspectives. This theory is grounded in the notion that learners construct their knowledge and understanding through participatory learning and interaction with one another within the prevailing learning environment. The learners’ prior basic cognitive knowledge and experiences influence subsequent learning practices, as witnessed through assessment procedures [ 68 ]. The constructivist theory is based on the premise that success on specific tasks and the values inherent in those tasks is positively correlated with students’ previous experiences, achievement, and consequently students’ prior abilities. Constructivism is directly linked to the Expectancy-Value Theory, students’ levels of cognitive demand, and the teachers’ pedagogical content knowledge [ 94 ].

This research is a subset of a large study that investigated the effect of active learning through the heuristic problem-solving approach on students’ achievement and attitude towards learning mathematics word problems and LP in particular. It is expected that the findings will be applied to enhance mastery of students’ mathematical concepts within the learners’ zone of proximal development. This can be attained by engaging learners, devising effective procedures, and application of prior conceptual and procedural knowledge in subsequent learning. In so doing, educators and learners may devise suitable and multiple problem-solving methods and develop a positive attitude towards learning. To achieve the objectives of this study, the framework provided by [ 94 ] was used to map students’ LP cognitive demands. Cognitive demand was described by [ 94 ] as “the kind and level of thinking required of students to successfully engage with and solve the task” ([ 94 ] p.11).

[ 94 ] interpreted these levels as problem-solving strategies. According to [ 94 ] teachers should take into account different levels of cognitive demand with varying mathematics tasks given to students. The authors reasoned that students’ mathematical proficiency and competency are determined by the tasks they are given during instruction. Mathematics tasks at the lower cognitive stage (memorization level, and connection with procedures), for example, must be different from those at the highest cognitive level (connection without procedures, and doing mathematics). During task review, this approach supports teachers’ instructional activities and approaches (what is learned, how it is learned, and when it should be learned) following Stein et al.’s levels of cognitive demand. In the context of learning LP, students should first understand and appropriately apply the previous knowledge of equations and inequalities in solving LP non-routine mathematics word problems.

The practical aspect of [ 94 ] lies in its implementation [ 94 ]. Indeed, if students are only exposed to solving memorization tasks, they may not be able to adequately master non-routine high-level (higher-order thinking) tasks that require critical thinking skills (doing mathematics). Thus, as students advance through their academic stages, teachers need to adjust and involve them in answering high-level tasks from the beginning to improve their critical thinking, and problem-solving abilities. To effectively achieve this, [ 94 ] suggested four levels of cognitive demand: two lower-level demand tasks (memorization and procedures with connection to concepts) and two higher-level demand tasks (procedures without connections and doing mathematics). According to Stein et al., students’ proficiency in "doing mathematics" tasks may improve their problem-solving abilities, especially in solving non-routine tasks in new contexts. In this case, cognitive level characteristics formed a conceptual framework for evaluating individual students’ levels of cognitive demand in learning LP tasks. These characteristics are important in the sense that they highlight specific cognitive levels needed for students to correctly perform LP mathematics word tasks as well as specifying students’ cognitive levels at the time of administering LP tasks (pre-test and post-test).

To link the above two theories (Expectancy-Value Theory and social constructivism), this research also applied the Pedagogical Content Knowledge (PCK) conceptual framework based on [ 95 ]). conceptualized that “pedagogical content knowledge also includes an understanding of what makes the learning of specific topics easy or difficult: the conceptions and preconceptions that students of different ages and backgrounds bring with them to the learning of those most frequently taught topics and lessons” (p. 9). According to [ 95 ], effective learning strategies involve teachers’ integration of students’ preconceptions and misconceptions held previously and how these preconceptions relate to subsequent learning. In this case, teachers were expected to apply suitable learning strategies to boost students’ understanding of LP concepts. In supporting students’ problem-solving strategies, mathematical thinking and understanding, [ 96 ] developed a framework for examining mathematics teachers’ PKC. The framework is related to Shulman’s conceptualization of PCK and is important in enhancing teachers’ PCK (e.g., the use of graphics, and manipulatives) with the main objective of understanding students’ learning challenges, their mathematical thinking and reasoning.

1.8 Conceptual framework of the study

Based on the theoretical framework discussed above, a conceptual framework shows the direct relationship between students’ attitude towards, and performance in mathematics word problems, mediated by active learning heuristic problem solving strategies. Active learning encompasses different instructional strategies that promote students’ engagement and active participation in constructing knowledge and understanding of particular concepts. These strategies may take the form of hands-on activities, problem-solving tasks, critical thinking etc. This approach involves learners’ individual or collaborative performance on routine and non-routine tasks. In this case, learners are directly involved in thinking and doing through discussions, reviews, evaluations, concept maps, role plays, hands-on projects, and cooperative group studies. Often, active learning tasks require learners to make their thinking explicit, allowing educators to gauge and understand students’ learning.

One major benefit of active learning is that it develops students’ higher-order thinking skills (analysis, synthesis, and evaluation) and also prepares them to apply mathematics in real-life scenarios. Several studies have shown that students in typical active learning classrooms perform better than those taught conventionally [ 97 , 98 ]. This is because they have an opportunity to reflect, conjecture, or predict outcomes, and then to share and discuss their concepts with teachers and their peers to activate and re-activate their cognitive processes. Active learning helps students to reflect on their understanding by encouraging them to make connections between prior mathematical knowledge and new concepts. The conceptual model hypothesizes the existence of a direct relationship among the stated variables. In particular, the conceptual model is shown in Fig 1 below. The model assumes the existence of a relationship between attitude and performance in post-test (including pre-attitude and post-attitude). There exist other indirect inherent relationships which are not part of this study.

2.0 Materials and methods

A quantitative survey research approach [ 99 – 101 ], was used to collect, analyze, and describe students’ experiences and latent behavior in learning linear programming mathematics word tasks. The present study was guided by a philosophical pragmatic paradigm. Pragmatists believe that the world has several realities, and that there is no unique way of describing and interpreting these realities. The pragmatism paradigm aligns with the quantitative approach [ 102 , 103 ]. This paradigm was suitable for studying and understanding the underlying relationships between the application of active learning through the heuristic approach (independent variable) and students’ achievement and attitude towards learning LP (dependent variable).

2.1 Research design

This study investigated the relationship between students’ attitude towards, and performance in mathematics word tasks. The study adopted a quantitative approach to gain a deeper and broader understanding [ 99 , 102 , 104 ]. A quasi-experimental pre-test, post-test, non-equivalent control group research design was adopted with the objective of observing the response of subjects to the treatment [ 105 ]. The quasi-experimental design was applied to observe and examine individual students’ attitude, and achievements in LP, including their conceptual and procedural understanding of LP concepts. It was also used to establish how and why, and to provide similar findings like in cases where a typical classical (true) experimental design is applied [ 105 ]. The researchers’ awareness of non-random selection could influence the possibility of errors in the final results [ 106 ]. However, the justification is that the more similar intact classes for experimental and comparison groups are in terms of baseline characteristics, the closer the results of the quasi-experimental non-equivalent control group research design approximate to the results of a true experimental design. By using the stated approach and design, researchers ably compared and contrasted students’ attitude towards learning LP word problems. Learners from the sampled schools (experimental and comparison groups), and in their intact classes participated. Intact classes were maintained to avoid interfering with the respective schools’ set timetables and schedules.

2.2 Sample and sampling methods

The population includes all people with related characteristics who qualify to be respondents in a given study [ 106 ] while the target population is the specific group of respondents on whom a researcher plans to generalize the sample research findings [ 107 ]. A sample is a group of respondents selected from the target population [ 102 ]. It is laborious, expensive and time consuming to conduct a census survey. Most often, the population is large and scattered geographically. Thus, sampling techniques become inevitable. In this study, the sampling frame was mutually homogeneous and internally heterogeneous. The sampling frame (units of analysis) were all the 11th grade students for the academic year 2020/2021. Both private and public secondary schools (rural and urban) participated.

Thus, the sampling frame was clustered using a multi-stage cluster sampling method, followed by purposive and convenient sampling [ 102 , 107 ]. Clusters were selected using simple random sampling. The first stage involved the selection of two districts stratified by regions (Mbale district in the Eastern region and Mukono district in the Central region respectively). The multistage cluster sampling method was used to select four secondary schools from each region. First, the two regions were selected based on student’s previous academic performance at UCE and inherent school characteristics. Second, purposive and convenient sampling were used to select four secondary schools from each region (two schools from each region were assigned to the experimental group). To avoid sampling errors [ 105 ], the use of purposive and convenient sampling aided the selection of schools within the study context with similar (almost similar) educational characteristics (government/private, rural/urban, with low/high enrolment) for experimental and comparison groups.

In this study, researchers carefully selected subjects with a purpose in mind, and based on the believe that the selected subjects had the key information for the study [ 105 , 106 ]. One or more specific predetermined groups were considered as likely to have and capable of providing the required data. The purpose of purposive sampling was to permit in-depth exploration subject to the available resources, time and the theoretical saturation [ 108 ]. It was intended to access the target sample as quickly as possible especially where sampling for proportionality was not the primary concern. It was also intended to obtain the opinions of the target population easily.

Finally, to avoid disrupting the already existing research setting in sampled secondary schools, all students in their intact classes constituted the units of analysis. According to [ 108 ], multi-stage cluster sampling is suitable due to its flexibility, saves time and money, and is applicable in the collection of primary data from a heterogeneously dispersed population. Selected secondary schools for the experimental group were approximately 250 km away from one another for two reasons; first, to avoid diffusion and spurious results, and second, to compare and contrast students’ abilities, achievements and their attitude towards LP word tasks in the two regions. The units of analysis in their intact classes were all selected as clusters to represent the entire sampling frame. A sufficient number of respondents from the entire population was selected in such a way that the sample findings would be used to make generalizations to the whole population [ 106 ].

2.3 Participants

To compensate for non-response and considering intact classes, 608 respondents were selected from a heterogeneously large population to achieve precision and external validity. There were 639 students at the time of administering the pre-test achievement tests and questionnaires. During data entry and analysis, the scores of thirty-one (31) students from the experimental group were excluded due to missing post-test average scores. The students missed the post-test because they were absent on the day the post-test was administered. Consequently, the findings reported in this research were based on data from 608 students from eight randomly selected secondary schools. Hence, the sample size (n) = 608 [ 109 ]) was adequate. According to [ 107 ], the application of [ 109 ] is appropriate when the preferred margin of error is 0.05, which is in confirmation with the above sample size.

Four schools were selected from Mbale district, eastern Uganda, and four from Mukono district, central Uganda. Schools were allocated to the experimental and comparison groups by a toss of a coin. Two schools from each region were assigned to the experimental group. Selection of the 11 grade students was based on the content covered in this class as outlined in the Ugandan mathematics curriculum materials [ 84 ]. Of the 608 students, 291 (47.86%) were males and 317 (52.14%) were females with a mean age of 18.36 (S.D = 0.94) years. Three hundred seventeen (52.1%) students were from the comparison group while two hundred ninety-one (47.9%) were from the experimental group. The selection of students from the two distant schools within/outside the regions and assigning them to experimental group was to avoid spurious results. A situation where a particular school had more than one class “stream”, at least one hundred students with varied academic abilities were randomly selected from different classes to respond to the attitudinal questionnaires. The school (subject) heads revealed that the mathematics syllabus containing LP word problems (Appendix 1 in S1 File ) had been completed at the time of data collection. The students were selected to provide their experiences and attitudes toward learning LP word problems.

2.4 Ethical considerations

Ethical clearance was sought from the Research and Ethics committee of corresponding authors’ university (Directorate of Research and Innovation, University of Rwanda–Ref: 03/DRI-CE/061(b)/EN/gi/2020). Subsequent permission was sought and granted by the permanent secretary ministry of education and sports, the district education officers, and finally from the headteachers of sampled secondary schools before accessing research sites. Upon accessing research participants, they were informed and clearly explained to the purpose of the study. Written informed consent was obtained from participants of at least 18 years. Where necessary, consent from parents or guardians was obtained for respondents below 18 years. Finally, identification numbers were allotted to participants before they anonymously and voluntarily completed questionnaires. Questionnaires were administered to the respondents during school working hours without interfering with the school set timetables. The heads of the sampled schools provided appropriate schedules and personnel to help the principal researcher together with research assistants to quickly and effectively administer questionnaires. All participants were assured of confidentiality and anonymity before they willingly consented. Those who opted not to participate even after the distribution of questionnaires were allowed to withdraw.

2.5 Research instruments and procedure for administration

In addition to demographic questions, the Attitude Towards Mathematics Inventory-Short Form (ATMI-SF) [ 3 ], a 14-item instrument questionnaire consisting of four subscales (enjoyment, motivation, value/usefulness and self-confidence) was adapted (Appendix 2 in S1 File ) to measure the relationship between students’ performance and their attitude towards learning LP word tasks. The ATMI-SF, a 5-point likert-type scale with response options ranging from “Strongly Disagree (1)” to “Strongly Agree (5)” was used. The ATMI-SF items were developed from [ 89 ], which were also developed and validated from several mathematics attitudinal questionnaire items [ 2 , 31 , 34 , 62 , 110 ]. The ATMI-SF was adapted (Appendix 2 in S1 File ) because it directly correlates with the learning of LP, “the cousin of mathematics word problems.” English being the language of instruction in Ugandan secondary schools’ curricula, translation of questionnaire items was not required.

Content validity of the questionnaire was assessed by three experts (one senior teacher for mathematics, one senior lecturer for mathematics education, and one tutor at teacher training institution). The experts were selected based on their vast experience in teaching mathematics at various academic levels. They evaluated the appropriateness and relevance of the adapted questionnaire items. Based on their recommendations, suggestions and comments, some questionnaire items were adjusted to suit students’ academic level and language to adequately answer the research objectives. Reliability of the construct was ascertained using Cronbach alpha (0.75). This threshold is acceptable based on recommendations from [ 111 ].

The ATMI-SF was administered to the experimental and comparison groups. Both the experimental and comparison groups were taught LP (equations, inequalities and LP) following the designed and approved Ugandan mathematics curriculum materials [ 84 ]. The experimental group was taught LP using the heuristic problem-solving approach before and after an intervention. The learning in the comparison group was purely conventional, and teachers did not follow proper and organized strategies as it was the case with the experimental group. In particular, students from the experimental group were taught LP using several active learning heuristic strategies following clearly outlined principles and strategies.

To adequately implement active learning heuristic problem-solving strategies, teachers from the experimental group were trained. First, students’ prior conceptual knowledge of equations and inequalities plus the basic algebraic principles and understanding were reviewed to link previous concepts to learning of LP. Second, several learning materials were applied to help students adequately master LP concepts. The materials included the use of graphs, grid boards, excel and GeoGebra software (see Appendix 3 in S1 File for G01, G02, and G03). Empirical studies (e.g., [ 42 , 112 , 113 ]) have found that the stated materials enhance students’ conceptual understanding and critical thinking abilities. These strategies were further integrated in problem solving strategies [ 114 ] by ensuring that students understand the LP word problem, devise a plan, adequately carry out the plan and finally look back to verify solution sketches (see Appendix 3 in S1 File for PS01, PS02, PS03, PS04). To ensure that students minimize errors and misconceptions, the learning of LP was further integrated with Newman Error Analysis (NEA) model [ 115 ]).

The instrument was adapted and validated by mathematics education experts as an error analysis tool to adequately provide teachers with a framework to examine underlying reasons why students make errors and misconceptions when answering mathematics word problems. In this tool, teachers emphasized question reading and decoding, comprehension, transformation, process skills and encoding (see Appendix 3 in S1 File for NEA01, NEA02, NEA03, NEA04 and NEA05). Effective utilization of this tool means that students are capable of understanding, confidently and correctly answering mathematics word problems. Previous errors and misconceptions can easily be identified, and applied in subsequent learning to boost students’ conceptual and procedural understanding hence providing independent reasoning to their own solutions. Error analysis also helps educators to identify factual, procedural, and conceptual mistakes commonly made by students to provide academic and conceptual support whenever it is needed. In conclusion, this research will provide remedies for correcting students’ challenges in learning mathematics word problems, and to boost their problem solving and critical thinking skills.

The mathematics word problem-solving strategies supported students’ thinking, and reasoning. Consequently, teachers’ identified students’ preconceptions, misconceptions and errors as they solved LP word tasks. Multiple representations and demonstrations were carried out to help students understand preliminary concepts and use them to optimize LP tasks. Students were engaged in the typical PS scenario individually, in pairs and in small groups. Questions and specific classroom tasks ranged from simple to complex and from concrete to abstract. Materials for instance graph papers were provided as teachers guided and demonstrated the graphical solution of LP problems. Teachers provided procedures by supporting and guiding learners during the learning process. All students’ preconceptions, misconceptions and errors were addressed. For instance, confusion on whether or not to use dotted lines or solid lines to represent the inequality y >a+b x , plotting lines to represent an equation or inequality, shading wanted or unwanted regions, etc.

All learners were assigned varied tasks to apply suitable strategies for yielding the optimal solution of a LP word problem. The “bright” students were allowed to share their experiences with the low attainers in pairs or in their small groups. Where necessary, local language was allowed for students’ peer instruction in pairs or small groups to master the underlying concepts. The classroom interaction and presentations enhanced students’ conceptual knowledge and procedural understanding of LP concepts. This further improved their reasoning, creativity and critical thinking. Consequently, constructive and informative feedback to retain their conceptual understanding was fostered. All challenging concepts on LP were harmonized by individual teachers during reflection and the evaluation phase. All students who had challenges even after this phase were allowed to repeat the tasks by doing corrections and submitting their work for re-marking. The intervention took approximately three months from October 2020 to February 2021. To find out whether or not active learning heuristic problem-solving approach changed students’ attitude and performance, the post attitude questionnaire and achievement test were administered to both groups by the principal researcher (assisted by research assistants), and both descriptive and inferential analysis was done using SPSS (V.26). Students’ feedback on attitudinal constructs and the post-test scores were compared and contrasted. Finally, this study established if there was a significant relationship between students’ performance and their attitude towards mathematics word tasks, mediated by active learning heuristic problem-solving approach (Appendix 3 in S1 File ).

4.0 Results

4.1 procedure for data analysis.

The ATMI-SF questionnaires were completed by the sampled students at their respective schools in their natural classroom setting. The questionnaires were completed in at most 30 minutes on average. The survey instrument contained a ‘filter statement’, as a Social Desirability Response (SDR) to verify and discard respondents’ questionnaires especially those who did not read (see item 15 in Appendix 1 in S1 File ) or finish answering all questionnaire items [ 116 , 117 ]. Written consent was received from all participants and participation in this study was completely voluntary and confidential. Participants who felt uncomfortable to complete the questionnaire items were not penalized. Data were collected with the help of mathematics heads of department who were selected from sampled schools on the basis of their expertise and experience. Participants were explained to, the purpose of the study before administering and/or filling in of questionnaire items. In the presence of the principal researcher, research assistants and some selected school administrators, participants completed and returned all the questionnaires. Descriptive and inferential statistics were used to analyze the collected data with reference to the background characteristics and stated hypothesis. Data were analyzed using the Statistical Package for Social Sciences (SPSS) version 26, with [ 118 ] PROCESS (v.4) macro. This provided the analysis for exploring whether or not there exist a significant relationship between students’ performance and their attitude towards LP mathematics word problems.

4.2 Findings and interpretation

Psychometric properties.

IBM SPSS (version 26) software package was used for analysis. Preliminary statistical analysis revealed no evidence of missing data due to a few cases which were ignored because they did not exceed 5% of sample cases [ 89 , 119 , 120 ]. However, out of 639 questionnaires distributed, 31 questionnaires were removed because the participants did not either conform to SDR [ 116 , 117 ] or the questionnaires were incomplete. Univariate analysis was run to examine the degree of normality [ 111 , 121 ]. The indices for skewness and kurtosis were within the acceptable ranges (±2 and ±7 respectively) [ 111 , 122 ]. Thus, data were fairly normally distributed.

We tested the psychometric properties (reliability and factor analysis) of the two instruments. The Cronbach alpha coefficient of the adapted ATMI-SF was α = 0.75. Factor analysis was performed using the principal component (with varimax rotation) [ 119 , 121 , 123 ]. The values obtained were consistent with [ 3 , 40 ] findings. The Kaiser-Meyer-Olkin Measure of Sampling Adequacy Test (KMO) and Bartlett’s test of sphericity was conducted. The value of KMO = 0.77> 0.60; and that of Bartlett’s test of sphericity was significant (894.349, p<0.05) indicating a substantial correlation in the data and an acceptable fit. For a self-developed standardized active learning heuristic problem-solving (ALHPS) tool, α = 0.71, KMO = 0.74 > 0.60; and that of Bartlett’s test of sphericity was significant (253.092, p<0.05). Following the above recommendations, all items were found to be acceptable with adequate construct validity, internal consistency and homogeneity [ 121 , 124 ]. Overall, these items were deemed fit to measure the relationship between active learning heuristic problem-solving and students’ attitude towards linear programming word tasks.

Tables ​ Tables1 1 and ​ and2 2 show descriptive statistics (mean, standard deviation, skewness and kurtosis). The values in the table show students’ scores on ATMI-SF and ATLPWTs. The results do not show any significant differences between the relationship between ALHPS approach and students’ attitude towards linear programming (enjoyment, motivation, usefulness and self-confidence). Indeed, both experimental and comparison groups were similar during pre-test. There was however a change in students’ ATLPWTs due to the intervention administered. The findings show that students generally held negative attitude towards learning LP word tasks. These findings are consistent with other empirical research findings (e.g., [ 40 ]).

Tables ​ Tables3 3 and ​ and4 4 show that although the data is explained by 10% of the model, the active learning heuristic problem solving approach has a significant effect on attitude (p < 0.05). Moreover, the narrow confidence interval (0.154) shows a greater degree of precision.

Standardized coefficients .3130.

Tables ​ Tables5 5 and ​ and6 6 show that students’ attitude towards mathematics word problems has a direct and significant effect on performance. This model accounted for by approximately 50% This means that a positive change in students’ attitude directly affects students’ performance and vice versa.

Standardized coefficients Attitude .4442, Active Learning .4314.

Table 7 shows the significant direct effect of students’ attitude towards and performance in LP mathematics word tasks.

However, from Tables ​ Tables8 8 and ​ and9, 9 , the indirect effect of attitude on performance mediated by active learning heuristic problem-solving strategies exists with the effect size of 5.18. This effect size is large enough [ 111 ]) to conclude that the relationship between students’ attitude towards and performance in mathematics word problems exists.

5. Discussions and educational implications

This study explored the relationship between students’ attitude towards, and performance in LP mathematics word problems, mediated by active learning heuristic problem-solving (ALHPS) approach. Students’ attitude towards mathematics has been a central concern for the last five decades. To date, attitude towards mathematics is still being studied due to the affective role it plays in mathematics education. Studies on attitude towards mathematics have been conducted globally (e.g., [ 11 , 13 , 28 , 125 ]). Although there are limitations in assessing students’ attitude towards mathematics the ATMI-SF, a reduced scale with fewer subdomains yielded significant results and a better fit to the data. According to [ 27 ], students’ attitudes towards mathematics may result in students leaving school with a positive emotional disposition and confidence towards mathematics. However, this greatly depends on the instructional methods as defined in different mathematics curricula.

In this study, preliminary analysis revealed that the psychometric properties of the adapted ATLPWTs and the ALHPS approach instruments were found acceptable. The data was collected from 608 students from eight secondary schools in eastern Uganda and central Uganda. The data analysis revealed existence of a direct significant positive relationship between students’ performance and their attitude towards learning LP mathematics word problems mediated by active learning heuristic problem solving approach. These findings are of great educational implications to the learning of mathematics in Uganda, and supplements other empirical findings (e.g., [ 32 ]). Thus, by boosting students’ positive attitude towards mathematics, this may change their performance in mathematics word problems and mathematics generally.

This study indicates that students’ attitude enhanced their performance in learning LP concepts. The effectiveness of several approaches aimed at altering students’ negative attitude towards learning mathematics greatly depend not only on students but also on how educators react to and incorporate curriculum changes. The findings concord with other previous empirical studies on the contribution of positive attitude in enhancing students’ performance [ 36 , 55 , 126 , 127 ]. Seen in this way, the findings are further in agreement with the constructivism theoretical framework suggesting that educators should always review previous concepts and use them to construct new knowledge to enhance secondary school attitude in mathematics. The results of this study are likely to inform educational stakeholders in assessing students’ ATLPWTs and provide remediation and interventional strategies aimed at creating a conceptual change in students’ attitude towards LP and mathematics generally. This will further act as a lens in improving students’ achievement, as indicators of students’ confidence, motivation, usefulness, and enjoyment in learning LP word problems and mathematics generally.

These findings show that students generally had negative attitude towards LP word problems. Although some students’ ratings were below the neutral attitude (3), they indicated the usefulness of LP in their individual daily lives. The experimental group showed a slightly favorable attitude towards LP word problems after an intervention because the problem-solving heuristic instruction was used during instruction as compared to the students from the comparison group who learned LP conventionally. Some students and teachers revealed that LP concepts are more stimulating, require prior conceptual knowledge and understanding of equations and inequalities, and are not interesting to learn just like other mathematics topics. The explanation provided indicated that some teachers do not adequately apply suitable instructional techniques and learning materials to fully explain the concepts. It was, however, observed that teachers encouraged students to constantly practice model formation from word problems to demystify the negative belief that LP is a hard topic, thereby encouraging them to understand LP and related concepts. However, students’ performance especially those from the experimental group improved compared to their counterparts from the comparison group who almost had similar attitude towards LP before and after an intervention. The learning of mathematics should promote students’ engagement where learning is by doing through practice and that errors and misconceptions should be considered as part and parcel of learning.

The results of this study are consistent with the theoretical framework [ 93 , 128 ] and conceptual framework [ 94 ]. To achieve the purpose of this study, teachers in the experimental group varied tasks to examine students’ attitude, problem-solving and critical thinking skills. Both the experimental group and the control group acknowledged the fact that LP is a challenging topic (see Appendix 1 in S1 File ), although they highly recognized its significance in constructing models, and optimization in real life. The importance of LP rests in its application and thus teachers were tasked to help learners to develop a positive attitude towards mathematics word tasks, to boost their conceptual understanding, reason insightfully, think logically, critically and, coherently. The teachers’ competence in applying instructional strategies helped learners from the experimental group to gain deeper and broader insight, conceptual and procedural understanding, reasoning, and positive attitude. The control group in their conventional instruction still perceived LP as one of the hardest topics. Negative attitude was observed as it was indicated in the most learners’ ATMI-SF filled questionnaires. Thus, teachers recognized application of active learning by engaging students with frequent classwork in form of exercises and assignments is paramount. In addition, application of prior conceptual knowledge and understanding may favorably help students to develop a positive attitude and perform better in LP mathematics word tasks. Generally, students from the comparison group seemed not to have adequately developed knowledge of problem solving, logical thinking and reasoning. They did not view the learning of LP from a broader perspective beyond passing national examinations at Uganda Certificate of Education.

The results of this study point to important issues to the educational stakeholders in cultivating an early positive attitude in mathematics which is aimed at investigating specific topics from primary to secondary school mathematics. This may be a potential strategy for applying different heuristic problem-solving approaches and strategies to significantly improve students’ attitudes and performance. [ 129 ] have stated that epistemological beliefs of learners greatly determine the learning strategies that teachers should apply to stimulate their attitude and performance. In this study, the problem-solving heuristic method supported collaboration and discussions between teachers and amongst students during the learning process. Thus, students from the experimental group worked collaboratively in their small groups. The students helped and guided each other hence boosting their attitude and performance. As noted by [ 130 ], the teachers’ instructional strategies of considering individual students’ differences may consequently change students’ attitude, thereby providing both academic and social support.

The findings support the recent Ugandan curriculum reforms in lower secondary school curriculum. By boosting students’ attitude towards learning mathematics, their deep and broader conceptual understanding, active engagement, during the learning process, exploring problem-solving strategies, critical thinking, logical reasoning, effective communication, effective utilization of technology, supporting individual students’ learning gaps and provision of meaningful assessment practices will be guaranteed. Indeed, when the ALHPS approach was applied to the experimental group, the low performing students greatly gained conceptual understanding and also acquired problem-solving skills. This enhanced students’ learning and attitude towards mathematics and solving LP word tasks in particular. Besides, the problem-solving heuristic approach applied to the experimental group boosted students’ confidence in answering both routine and non-routine problems. Students’ fear in comprehending LP word problems and their negative attitude towards answering LP questions decreased. Students were actively involved in problem-solving. This gradually built students’ competence and confidence in learning LP and related concepts which significantly fostered students’ positive attitude towards learning LP word problems.

[ 131 ] Kilpatrick, Swafford, and Findell (2001) developed five interwoven strands for achieving mathematical proficiency. The five strands are conceptual understanding, procedural fluency, strategic competence, adaptive reasoning, and productive disposition [ 132 ]). The main objective is to develop and foster students’ abilities in the above five strands. Accordingly, proficiency in mathematics problem-solving entails students’ holistic achievement of the three domains: cognitive, affective, and psychomotor. Thus, the methods of teaching mathematics should emphasize and promote learners’ acquisition and application of knowledge, beliefs, and skills in solving real-life problems. The authors have argued that “proficiency should enable them to cope with the mathematical challenges of daily life and enable them to continue their study of mathematics in high school and beyond" (p. 116). The above five interrelated strands align with the theoretical and conceptual framework and are inevitable for learning mathematics in the sense that they foster, support, and promote students’ identification and acquisition of conceptual knowledge, procedural knowledge, critical thinking, and problem-solving abilities. According to [ 132 ], students’ effective learning “depends fundamentally on what happens inside the classroom as teachers and learners interact over the curriculum” (p. 8).

6. Conclusion and future research directions

The purpose of this research was to explore the relationship between students’ attitude towards, and performance in LP mathematics word problems. This study generates and supports the policy implications linked to the recent education and mathematics curriculum reforms in Uganda. The findings provide preliminary insights into the fundamental concepts and provide an introduction to LP concepts for advanced mathematics. Students’ attitudes point to issues related to the demographic variables and latent constructs for learning mathematics. Attitude towards mathematics has both significant direct and indirect effect on students’ performance mediated by ALHPS strategies. However, some attitudinal dimensions have only direct effect. Wigfield, and colleagues’ expectancy-value model of achievement motivation [ 93 , 128 ] supports this claim. The theory is based on the premise that success on specific tasks and the values inherent in those tasks is positively correlated with performance, and attitude. Thus, the ATMI-SF constructs combining motivation, enjoyment, confidence, and usefulness, and related latent variables are good mediators to explain students’ success in learning LP and mathematics generally. The educational stakeholders and experts in mathematics education should embrace suitable learning strategies that cultivate a positive attitude towards learning and consequently performance.

6.1 Limitations and future research

As earlier mentioned, studies on attitude towards and performance in mathematics have gradually shifted from general to topic domain-specific studies. Thus, instead of investigating the students’ attitude towards mathematics generally, the current research focused on exploring the influence of attitude towards and performance in mathematics word problems and LP in particular. This study adopted a quantitative approach. Despite the conceptual, theoretical, and methodological contributions of this study, several limitations must be considered.

First, to gain more insight, we recommend that future researchers should fill the gap through triangulation. The use of qualitative methods such as interviews and observation may provide more evidence on students’ experiences in learning LP word problems, including students’ emotional experiences and the general latent behavior. This would help enrich the existing body of knowledge on attitude towards and performance in mathematics. The teachers ‘attitude towards the domain specific LP word problems is also a potential area for further investigation aimed at improving the instructional strategies. To achieve this, teachers’ content knowledge and pedagogical content knowledge of both in-service and pre-service teachers should be investigated. Also, the teachers’ professional development programs should emphasize content knowledge and pedagogical content knowledge. Teachers coming together to share learning experiences and strategies, may help to improve students’ attitude and the learning of presumably “difficult topics” including LP and mathematics generally. Indeed, teachers need routine professional development support to successfully implement the stated learning activities. Another potential area for further research is the relationship between students’ and teachers’ demographic factors mediated by active learning heuristic problem-solving strategies, on students’ performance and attitude towards LP and mathematics generally. For this reason, we believe that further comparative studies are needed to better understand the relationship between students’ attitude and performance in particular mathematics units (topics). A better understanding of these influences is crucial to design actions that promote students’ academic achievement.

Supporting information

Acknowledgments.

This research is part of the Ph.D. Thesis that investigated the effect of the heuristic problem-solving approach on students’ achievement and attitude towards LP in Ugandan secondary schools. We appreciate useful information provided by the students and teachers in the study sample, which helped us, write this research article. The views expressed herein are those of the authors and not necessarily those of our funders. This is because our funders were not involved in identifying the suitable study design, methods of data collection and analysis, publication decision, or manuscript preparation.

Funding Statement

This research received support from the African Centre of Excellence for Innovative Teaching and Learning Mathematics and Science ([ACEII(P151847)], University of Rwanda, College of Education. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability

• PLoS One. 2024; 19(2): e0278593.

Decision Letter 0

27 Feb 2023

PONE-D-22-31844Relationship between Students’ Attitude towards, and Performance in Mathematics Word ProblemsPLOS ONE

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Reviewer #1: The study presents the results of original research. Experiments, statistics, and other analyses are performed to a good technical standard and are described in sufficient detail. Conclusions are presented in an appropriate fashion and are supported by the data. The article is presented in an intelligible fashion and is written in standard English.

Still, there are some issues for the authors' consideration and revisions.

Para 1.1 Please revise the citation format in the first sentence.

Para 3.2 Of the 608 students, 291 (47.86%) were males and 492 (57.8%) were females with a mean age of 18.36 (S.D = 0.94) years. 291 plus 492 does not equal 608. Please double check.

For the Data Analysis part, please present the tables according the APA style. For example, it should be three digits after decimal point rather than four digits. The data analysis part needs to be enhanced.

Reviewer #2: This research seems to be interesting, but the author did not introduce it following rigorous academic norms. The theoretical basis, methodology, analysis and discussion of the results have not been fully delivered. In the literature review section, many ideas are vague and need to be expanded. In the methodology section, does the research design effectively respond to the purpose of the research, and does each subsection follow a logical sequence? The discussion section lacks systematic analysis, as well as critical discussion. In the Results section, there is no response to the purpose of the study. The following are some specific comments including textual and logical issues:

P3, 1.1 Verschaffel et al., 2010),

P5, 1.3 there is no “in the middle of the sentence.

P2 and P3, there are many references supporting one statement, which is a very general conclusion, for example, "some empirical findings report students’ attitudes towards specific units or topics in mathematics to enhance the learning of that specific content and mathematics”, and “Yet, some empirical studies have shown existence of significant

relationships between students’ attitude and performance in mathematics”. Do they say the same thing or have the same effect? And could you kindly clarify this statement and make those references indicating the specific results?

P3, “it appears that multiple factors ranging from students’ demographic factors to teachers’ classroom instructional practices influence students’ attitude towards learning mathematics" What multiple factors? I would suggest authors indicate this clearly.

P4, “Despite numerous difficulties encountered by students in algebraic inequalities (e.g., Fernández & Molina, 2017; Molina et al., 2017;Bazzini & Tsamir, 2004; Tsamir & Almog, 2001; Tsamir & Bazzini, 2004, 2006; Tsamir & Tirosh, 2006), a combination of methods (strategies) rather than one specific method can be applied to overcome specific students’ learning challenges.” These sentences seem not to be connected with the prior sentences. Please re-organize this part so that it could be aligned with this section.

Section 1.2 is too simple, authors should expand some points, for example, “Previous empirical studies have revealed that LP and/or related concepts are not only difficult for learners but also challenging to teach (Awofala, 2014; Goulet-Lyle et al., 2020; Kenney et al., 2020; Verschaffel et al., 2020a, 2020b).” What difficulties did the students encounter, cognitive or non-cognitive? “Different factors account for learners’ challenges in mathematics word problems?” What are these different factors? Is there existing research on attitudes towards Mathematics Word Problems? What kind of attitudes students held?

P6, not sure how these two theoretical frameworks connected. Could you clarify how "expectancy-value model theory within the constructivism paradigm” works?

P7, “Active Learning Heuristic Problem Solving Strategies" is here for the first time. More information should be provided in the literature review about these strategies.

Methodology, authors should make sure the correct methodology, “A quantitative survey research design” or “A quasi-experimental pre-test, post-test, non-equivalent, non-randomized control group study design". Please check the methodology and method. They are different.

P9, Treatment group vs. Experimental group, please use the same term throughout the context.

P9, “The learning in the comparison group was purely conventional, and teachers did not follow proper and organized strategies as was the case with the treatment group.” It seems that the treatment group has been involved in a more scientific, systematic, and better teaching method than the comparison group, so it is certain that the treatment group will achieve better results. In this case, the conclusion is obvious.

“Empirical studies (e.g., Abdul et al., 2010; Sari et al., 2012) have found that the stated materials enhance students’ conceptual understanding and critical thinking abilities. These strategies were further integrated in problem solving strategies (Polya, 2014) by ensuring that students understand the LP word problem, devise a plan, adequately carry out the plan and finally look back to verify solution sketches (see Appendix 3 for PS01, PS02, PS03, PS04). To ensure that students minimize errors and misconceptions, the learning of LP was further integrated with Newman Error Analysis (NEA) model (Mushlihah, 2018)." Those statements should be put in the literature review.

“The instrument was designed by the researcher and validated by experts in mathematics education as an error analysis tool to provide teachers with a framework to consider the underlying reasons why students answer mathematics word problems incorrectly. The teachers emphasized question reading and decoding, comprehension, transformation, process skills and encoding (see Appendix 3 for NEA01, NEA02, NEA03, NEA04 and NEA05).” You started to say new points. This paragraph contains two many ideas: strategies, instruments, coding.

P10. “the post attitude questionnaire and achievement test were administered to both groups, and analysis was done by comparing and contrasting students’ feedback on attitudinal constructs and the post-test scores." Not sure what the achievement test is. You should clearly state how many instruments you apply and how many hours implement the treatment group. Who implemented it in the treatment group and comparison group?

For table 4 and table 5, there is not enough explanation, what statistical method is used, and how to explain the data?

Again, the description of table8,9,10 is also very simple and unclear.

The main problem in the discussion part is that there is a lack of comparison with previous literature, and critical discussion, as with the total number of literature, the author needs to make a more detailed comparison with the relevant literature instead of citing a lot of literature.

The conclusion section does not respond well to the purpose of the study.

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Reviewer #1: No

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Author response to Decision Letter 0

27 Sep 2023

Revision of the manuscript titled “Relationship between Students’ Attitude towards, and Performance in Mathematics Word Problems”

Re: Response to Reviewer’s Comments on the Manuscript PONE-D-22-31844

From: "PLOS ONE" gro.solp@enosolp

We appreciate you for your precious time that was dedicated in reviewing our manuscript and providing valuable comments. Thank you for giving us the opportunity to submit a revised version of our manuscript titled “Relationship between Students’ Attitude towards, and Performance in Mathematics Word Problems”. These valuable and insightful comments have led to great improvements in the current version. We have been able to incorporate changes to reflect most of the suggestions provided by the esteemed reviewers. We have collectively and carefully considered your comments aimed at improving the quality of our paper and we have tried to address each of them. We hope that our manuscript after careful revision especially with your guidance will meet your standards. However, should there be more points not adequately addressed, we kindly welcome further constructive input.

Addressed below are point-by-point responses. All modifications in the manuscript have been highlighted in yellow.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. Thank you, this has been revised although I did not get a word document.

2. Please provide additional details regarding participant consent. In the Methods section, please ensure that you have specified (1) whether consent was informed and (2) what type you obtained (for instance, written or verbal). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information. Details of consent have been included as proposed.

3. Please change "female” or "male" to "woman” or "man" as appropriate, when used as a noun (see for instance https://apastyle.apa.org/style-grammar-guidelines/bias-free-language/gender ). This was not changed as suggested because the data was for students under 20 years and are locally addressed as females and males and not women and males.

4. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide. This has now been included.

Reviewer #1

Para 3.2 Of the 608 students, 291 (47.86%) were males and 492 (57.8%) were females with a mean age of 18.36 (S.D = 0.94) years. 291 plus 492 does not equal 608. Please double check. This has been addressed

For the Data Analysis part, please present the tables according the APA style. For example, it should be three digits after decimal point rather than four digits. The data analysis part needs to be enhanced. The tables and analysis section has been enhanced

Reviewer #2:

This research seems to be interesting, but the author did not introduce it following rigorous academic norms. The theoretical basis, methodology, analysis and discussion of the results have not been fully delivered. In the literature review section, many ideas are vague and need to be expanded. In the methodology section, does the research design effectively respond to the purpose of the research, and does each subsection follow a logical sequence? The discussion section lacks systematic analysis, as well as critical discussion. In the Results section, there is no response to the purpose of the study. The following are some specific comments including textual and logical issues:

P3, 1.1 Verschaffel et al., (2010), has been corrected

P5, 1.3 there is no “in the middle of the sentence. Was corrected

relationships between students’ attitude and performance in mathematics”. Do they say the same thing or have the same effect? And could you kindly clarify this statement and make those references indicating the specific results? Was clarified and additional literature included.

P3, “it appears that multiple factors ranging from students’ demographic factors to teachers’ classroom instructional practices influence students’ attitude towards learning mathematics" What multiple factors? I would suggest authors indicate this clearly. These factors were cited.

P4, “Despite numerous difficulties encountered by students in algebraic inequalities (e.g., Fernández & Molina, 2017; Molina et al., 2017;Bazzini & Tsamir, 2004; Tsamir & Almog, 2001; Tsamir & Bazzini, 2004, 2006; Tsamir & Tirosh, 2006), a combination of methods (strategies) rather than one specific method can be applied to overcome specific students’ learning challenges.” These sentences seem not to be connected with the prior sentences. Please re-organize this part so that it could be aligned with this section. This has been improved.

Section 1.2 is too simple, authors should expand some points, for example, “Previous empirical studies have revealed that LP and/or related concepts are not only difficult for learners but also challenging to teach (Awofala, 2014; Goulet-Lyle et al., 2020; Kenney et al., 2020; Verschaffel et al., 2020a, 2020b).” What difficulties did the students encounter, cognitive or non-cognitive? “Different factors account for learners’ challenges in mathematics word problems?” What are these different factors? Is there existing research on attitudes towards Mathematics Word Problems? What kind of attitudes students held? Explanations and additional literature included

P6, not sure how these two theoretical frameworks connected. Could you clarify how "expectancy-value model theory within the constructivism paradigm” works? The two theories have been linked

P7, “Active Learning Heuristic Problem Solving Strategies" is here for the first time. More information should be provided in the literature review about these strategies. The manuscript was written to publish just one of the objectives of this major study. This has been explained.

Methodology, authors should make sure the correct methodology, “A quantitative survey research design” or “A quasi-experimental pre-test, post-test, non-equivalent, non-randomized control group study design". Please check the methodology and method. They are different. A quantitative was adopted. This has been clarified.

P9, Treatment group vs. Experimental group, please use the same term throughout the context. Thank you, this has been used consistently.

P9, “The learning in the comparison group was purely conventional, and teachers did not follow proper and organized strategies as was the case with the treatment group.” It seems that the treatment group has been involved in a more scientific, systematic, and better teaching method than the comparison group, so it is certain that the treatment group will achieve better results. In this case, the conclusion is obvious. No, it is not obvious but depends on the findings and the decision on whether or not the Null hypothesis is rejected or accepted. There are cases when an intervention does not yield significant results.

“Empirical studies (e.g., Abdul et al., 2010; Sari et al., 2012) have found that the stated materials enhance students’ conceptual understanding and critical thinking abilities. These strategies were further integrated in problem solving strategies (Polya, 2014) by ensuring that students understand the LP word problem, devise a plan, adequately carry out the plan and finally look back to verify solution sketches (see Appendix 3 for PS01, PS02, PS03, PS04). To ensure that students minimize errors and misconceptions, the learning of LP was further integrated with Newman Error Analysis (NEA) model (Mushlihah, 2018)." Those statements should be put in the literature review. This has been re-arranged accordingly

“The instrument was designed by the researcher and validated by experts in mathematics education as an error analysis tool to provide teachers with a framework to consider the underlying reasons why students answer mathematics word problems incorrectly. The teachers emphasized question reading and decoding, comprehension, transformation, process skills and encoding (see Appendix 3 for NEA01, NEA02, NEA03, NEA04 and NEA05).” You started to say new points. This paragraph contains two many ideas: strategies, instruments, coding. This has been improved.

P10. “the post attitude questionnaire and achievement test were administered to both groups, and analysis was done by comparing and contrasting students’ feedback on attitudinal constructs and the post-test scores." Not sure what the achievement test is. You should clearly state how many instruments you apply and how many hours implement the treatment group. Who implemented it in the treatment group and comparison group? This has been explained in methodology section

For table 4 and table 5, there is not enough explanation, what statistical method is used, and how to explain the data? Additional explanations have been provided

Again, the description of table8,9,10 is also very simple and unclear. I have tried to added few lines for clarity

The main problem in the discussion part is that there is a lack of comparison with previous literature, and critical discussion, as with the total number of literature, the author needs to make a more detailed comparison with the relevant literature instead of citing a lot of literature. We have tried to discuss and provide relevant additional literature.

The conclusion section does not respond well to the purpose of the study. This has been improved.

Corresponding Author

Decision Letter 1

28 Sep 2023

Relationship between Students’ Attitude towards, and Performance in Mathematics Word Problems

PONE-D-22-31844R1

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Acceptance letter

13 Nov 2023

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