This study investigates goal orientations, and self-regulated learning (SRL) strategies, particularly for differential equations (DEs) based problem-solving. Two adapted self-designed questionnaires for goal orientations, and SRL and an assessment test containing five self-developed DEs tasks were distributed among 430 students studying in inter-colleges. Collected data was further examined through SPSS and Smart PLS software. Initially, direct effects of goal orientations (mastery, performance, and avoidance goal) and SRL (elaboration and critical thinking) were considered. Findings revealed that mastery, avoidance goals, and elaboration had a significant direct effect on DEs’ problem-solving. However, no such effect was observed for performance goals and critical thinking. Similarly, it was revealed that only elaboration had the role of mediation for both mastery and performance goals. Likewise, in the case of critical thinking, no significant effects were observed. The current study confirmed that goal orientations and SRL strategies influence DE problem-solving. Therefore, educators and teachers may structure their classroom activities to review and incorporate these learning strategies, which will enhance students’ internal motivation, resulting in significant improvement in their problem-solving ability.

• Ahmed, W., Van der Werf, G., Kuyper, H., & Minnaert, A. (2013). Emotions, self-regulated learning, and achievement in mathematics: A growth curve analysis. Journal of Educational Psychology, 105(1), 150-161. https://doi.org/10.1037/a0030160

• Akter, S., D’Ambra, J., & Ray, P. (2011, August 4-7). An evaluation of PLS based complex models: The roles of power analysis, predictive relevance and GoF index. Proceedings of the Seventeenth Americas Conference on Information Systems, Detroit, Michigan. https://aisel.aisnet.org/amcis2011_submissions/151/

• Ames, C. (1992). Classrooms: Goals, structures, and student motivation. Journal of Educational Psychology, 84(3), 261, https://doi.org/10.1037%2F0022-0663.84.3.261.

• Arslan, S. (2010). Traditional instruction of differential equations and conceptual learning. Teaching Mathematics and its Applications, 29(2), 94-107, https://doi.org/10.1093/teamat/hrq001.

• Artigue, M. (1989). Qualitative study of differential equations: Results of some experiments with microcomputers. Computing in Mathematics, 135-143.

• Barron, K. E., & Harackiewicz, J. M. (2001). Achievement goals and optimal motivation: Testing multiple goal models. Journal of Personality and Social Psychology, 80(5), 706-722. https://doi.org/10.1037/0022-3514.80.5.706

• Bibi, A., Abedalaziz, N. A. M., Ahmad, M., & Satti, U. (2018). Factors affecting differential equation problem solving ability of students at pre-university level: A conceptual model. MOJES: Malaysian Online Journal of Educational Sciences, 5(4), 13-24.

• Bibi, A., Zamri, S. N. S., Abedalaziz, N. A. M., Ahmad, M., & Umbreen, S. (2017). Factors affecting differential equation problem solving ability of students at pre-university level: A conceptual model. Malaysian Online Journal of Educational Sciences, 5(4), 13-24.

• Briggs, S. R., & Cheek, J. M. (1986). The role of factor analysis in the development and evaluation of personality scales. Journal of Personality, 54(1), 106-148, https://doi.org/10.1111/j.1467-6494.1986.tb00391.x

• Camacho-Machín, M., Perdomo-Díaz, J., & Santos-Trigo, M. (2012). An exploration of students’ conceptual knowledge built in a first ordinary differential equations course (Part I). The Teaching of Mathematics, XV(1), 1-20.

• Charles, R., Lester, F., & O’Daffer, P. (1987). How to evaluate progress in problem solving. The National Council of Teachers of Mathematics.

• Chin, W. W. (2010). How to write up and report PLS analyses. In Handbook of partial least squares (pp. 655-690). Springer. https://doi.org/https://doi.org/10.1007/978-3-540-32827-8_29

• Cohen, J. (1988). Statistical power analysis for the social sciences. Erlbaum.

• Coutinho, S. A. (2007). The relationship between goals, metacognition, and academic success. Educate~, 7(1), 39-47.

• Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.

• Duncan, T. G., & McKeachie, W. J. (2005). The making of the motivated strategies for learning questionnaire. Educational Psychologist, 40(2), 117-128, https://doi.org/10.1207/s15326985ep4002_6.

• Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist, 41(10), 1040-1048. https://doi.org/10.1037/0003-066X.41.10.1040

• Elliot, A. J., & McGregor, H. A. (2001). A 2×2 achievement goal framework. Journal of Personality and Social Psychology, 80(3), 501-519. https://doi.org/10.1037/0022-3514.80.3.501

• Elliot, A. J., McGregor, H. A., & Gable, S. (1999). Achievement goals, study strategies, and exam performance: A mediational analysis. Journal of Educational Psychology, 91(3), 549-563. https://doi.org/10.1037/0022-0663.91.3.549

• Fadlelmula, F. K., Cakiroglu, E., & Sungur, S. (2015). Developing a structural model on the relationship among motivational beliefs, self-regulated learning strategies, and achievement in mathematics. International Journal of Science and Mathematics Education, 13(6), 1355-1375. https://doi.org/10.1007/s10763-013-9499-4

• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50, https://doi.org/10.1177/002224378101800104

• Fritz, M. S., Taylor, A. B., & MacKinnon, D. P. (2012). Explanation of two anomalous results in statistical mediation analysis. Multivariate Behavioral Research, 47(1), 61-87, https://doi.org/10.1080/00273171.2012.640596.

• Gefen, D., & Straub, D. (2005). A practical guide to factorial validity using PLS-Graph: Tutorial and annotated example. Communications of the Association for Information systems, 16(1), 5. https://doi.org/10.17705/1CAIS.01605

• George, D., & Mallery, M. (2003). Using SPSS for Windows step by step: A simple guide and reference (10th ed.). Dorling Kindersley (India) Pvt. Ltd.

• Gunzler, D., Chen, T., Wu, P., & Zhang, H. (2013). Introduction to mediation analysis with structural equation modeling. Shanghai Archives of Psychiatry, 25(6), 390-394. https://doi.org/10.3969/j.issn.1002-0829.2013.06.009

• Hair, J. F. (2010). Multivariate data analysis. Pearson College Division.

• Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications. https://doi.org/https://doi.org/10.1080/1743727X.2015.1005806

• Hair Jr, J. F., & Lukas, B. (2014). Marketing research. McGraw-Hill Education Australia.

• He, T. (2004). The relations among trichotomous achievement goals, self–efficacy, and self–regulation in EFL sixth-grade classes in Taiwan. Journal of National Taipei Teachers College, 17(1), 111-134.

• Hoffmann, A. O., & Birnbrich, C. (2012). The impact of fraud prevention on bank-customer relationships: An empirical investigation in retail banking. International Journal of Bank Marketing, 30(5), 390-407. https://doi.org/doi/10.1108/02652321211247435

• Hulland, J., & Business, R. I. S. o. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195-204. https://doi.org/10.1002/(SICI)1097-0266

• Jansen, R. S., Van Leeuwen, A., Janssen, J., Jak, S., & Kester, L. (2019). Self-regulated learning partially mediates the effect of self-regulated learning interventions on achievement in higher education: A meta-analysis. Educational Research Review, 28, 100292. https://doi.org/10.1016/j.edurev.2019.100292

• Jansen, R. S., Van Leeuwen, A., Janssen, J., Kester, L., & Kalz, M. (2017). Validation of the self-regulated online learning questionnaire. Journal of Computing in Higher Education, 29(1), 6-27.

• Kadioglu, C., & Kondakci, E. U. (2014). Relationship between learning strategies and goal orientations: A multilevel analysis. Eurasian Journal of Educational Research, 56(1), 1-22.

• Kaplan, A., Middleton, M. J., Urdan, T., & Midgley, C. (2002). Goals, goal structures, and patterns of adaptive learning. In Achievement goals and goal structures (pp. 21-53). Routledge. https://doi.org/https://doi.org/10.4324/9781410602152

• Kingir, S., Tas, Y., Gok, G., & Vural, S. S. (2013). Relationships among constructivist learning environment perceptions, motivational beliefs, self-regulation and science achievement. Research in Science & Technological Education, 31(3), 205-226, https://doi.org/10.1080/02635143.2013.825594

• Kloosterman, P. (2002). Beliefs about mathematics and mathematics learning in the secondary school: Measurement and implications for motivation. In Beliefs: A Hidden Variable in Mathematics Education? (pp. 247-269). Springer. https://doi.org/https://doi.org/10.1007/0-306-47958-3_15

• Liem, A. D., Lau, S., & Nie, Y. (2008). The role of self-efficacy, task value, and achievement goals in predicting learning strategies, task disengagement, peer relationship, and achievement outcome. Contemporary Educational Psychology, 33(4), 486-512.

• Lin, S.-H., & Hsieh, P.-J. (2010). Book review: Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: Guilford. 366 pp., $40.50 paperback, ISBN 978-1-57230-690-5. Research on Social Work Practice, 20(1), 126-128, https://doi.org/10.1177/1049731509336986.

• Locke, E. A., & Latham, G. P. (2006). New directions in goal-setting theory. Current Directions in Psychological ScIence, 15(5), 265-268.

• Locke, E. A., & Latham, G. P. (2012). Goal setting theory. In Motivation: Theory and research (pp. 23-40). Routledge.

• Locke, E. A., & Latham, G. P. (2013). Goal setting theory: The current state. In New developments in goal setting and task performance (pp. 623-630). Routledge/Taylor & Francis Group.

• Locke, E. A., & Latham, G. P. (2015). Breaking the rules: A historical overview of goal-setting theory. In Advances in motivation science (Vol. 2, pp. 99-126). Elsevier.

• Locke, E. A., & Latham, G. P. (2019). The development of goal setting theory: A half century retrospective. Motivation Science, 5(2), 93-105.

• Lunenburg, F. C. (2011). Goal-setting theory of motivation. International Journal of Management, Business, and Administration, 15(1), 1-6.

• Mattern, K. D., & Shaw, E. J. (2010). A look beyond cognitive predictors of academic success: Understanding the relationship between academic self-beliefs and outcomes. Journal of College Student Development, 51(6), 665-678. https://doi.org/10.1353/csd.2010.0017

• Midgley, C., Kaplan, A., & Middleton, M. (2001). Performance-approach goals: Good for what, for whom, under what circumstances, and at what cost? Journal of Educational Psychology, 93(1), 77-86. https://doi.org/10.1037/0022-0663.93.1.77

• Midgley, C., Maehr, M., Hicks, L., Roeser, R., Urdan, T., Anderman, E., Kaplan, A., Arunkumar, R., & Middleton, M. (1996). Patterns of adaptive learning survey (PALS). The University of Michigan.

• Midgley, C., Maehr, M. L., Hruda, L. Z., Anderman, E., Anderman, L., Freeman, K. E., & Urdan, T. (2000). Manual for the patterns of adaptive learning scales. The University of Michigan.

• Mohsenpour, M., Hejazi, E., & Kiamanesh, A. (2006). The role of self-efficacy, achievement goals, learning strategies, and persistence in mathematics achievement. Journal of Educational Innovations, 16, 9-36.

• Muis, K. R., Chevrier, M., & Singh, C. A. (2018). The role of epistemic emotions in personal epistemology and self-regulated learning. Educational Psychologist, 53(3), 165-184, https://doi.org/10.1080/00461520.2017.1421465.

• Özcan, Z. Ç. (2016). The relationship between mathematical problem-solving skills and self-regulated learning through homework behaviours, motivation, and metacognition. International Journal of Mathematical Education in Science and Technology, 47(3), 408-420, https://doi.org/10.1080/0020739X.2015.1080313

• Phan, H. P. (2008). Unifying different theories of learning: Theoretical framework and empirical evidence. Educational Psychology, 28(3), 325-340, https://doi.org/10.1080/01443410701591392

• Phan, H. P. (2009). Exploring students’ reflective thinking practice, deep processing strategies, effort, and achievement goal orientations. Educational Psychology, 29(3), 297-313. https://doi.org/10.1080/01443410902877988

• Pintrich, P. R. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). National Center for Research to Improve Postsecondary Teaching and Learning.

• Pintrich, P. R. (1999). The role of motivation in promoting and sustaining self-regulated learning. International Journal of Educational Research, 31(6), 459-470, https://doi.org/10.1016/S0883-0355(99)00015-4

• Pintrich, P. R. (2000). An achievement goal theory perspective on issues in motivation terminology, theory, and research. Contemporary Educational Psychology, 25(1), 92-104. https://doi.org/10.1006/ceps.1999.1017

• Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33-40. https://doi.org/10.1037/0022-0663.82.1.33

• Pollak, H. O. (2015). The Place of Mathematical modelling in the system of mathematics education: Perspective and prospect. In Mathematical Modelling in Education Research and Practice (pp. 265-276). Springer International Publishing. https://doi.org/10.1007/978-3-319-18272-8_21

• Raykov, T., & Marcoulides, G. A. (2006). On multilevel model reliability estimation from the perspective of structural equation modeling. Structural Equation Modeling, 13(1), 130-141, https://doi.org/10.1207/s15328007sem1301_7.

• Rheinberg, F., Vollmeyer, R., & Rollett, W. (2000). Motivation and action in self-regulated learning. In Handbook of self-regulation (pp. 503-529). Academic Press. https://doi.org/10.1016/B978-012109890-2/50044-5

• Rokhmat, J., Marzuki, M., Hikmawati, H., & Verawati, N. N. S. P. (2017). The causal model in physics learning with a causalitic-thinking approach to increase the problem-solving ability of pre-service teachers. Pertanika Journal of Social Science and Humanities, 25(S), 153-168.

• Rowland, D. R. (2006). Student difficulties with units in differential equations in modelling contexts. International Journal of Mathematical Education in Science and Technology, 37(5), 553-558. https://doi.org/10.1080/00207390600597690

• Ryan, T. A. (1970). Intentional behavior: An approach to human motivation. The Ronald Press Company.

• Sahdan, S., Masek, A., & Zainal Abidin, N. (2017). Student’s readiness on self-regulated learning implementation for 21 st century learning approaches. Pertanika Journal of Social Sciences & Humanities, 25(S), 195-203.

• Schwartz, N. H., Andersen, C., Howard, B., Hong, N., & McGee, S. (1998, April 13-17). The influence of configurational knowledge on children’s problem-solving performance in a hypermedia environment. Annual meeting of the American Educational Research Association, San Diego, CA.

• Sommet, N., & Elliot, A. J. (2017). Achievement goals, reasons for goal pursuit, and achievement goal complexes as predictors of beneficial outcomes: Is the influence of goals reducible to reasons? Journal of Educational Psychology, 109(8), 1141, https://psycnet.apa.org/doi/10.1037/edu0000199

• Steiger, J. H. (2007). Understanding the limitations of global fit assessment in structural equation modeling. Personality and Individual Differences, 42(5), 893-898. https://doi.org/10.1016/j.paid.2006.09.017

• Stockton, J. C. (2010). A study of the relationships between epistemological beliefs and self-regulated learning among advanced placement calculus students in the context of mathematical problem solving. Kennesaw State University Kennesaw.

• Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (Vol. 5). Pearson.

• Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205. https://doi.org/10.1016/j.csda.2004.03.005.

• Valente, M. J., Gonzalez, O., Miočević, M., & MacKinnon, D. P. (2016). A note on testing mediated effects in structural equation models: Reconciling past and current research on the performance of the test of joint significance. Educational and Psychological Measurement, 76(6), 889-911. https://doi.org/10.1177%2F0013164415618992

• Villavicencio, F. T. (2011). Critical thinking, negative academic emotions, and achievement: A mediational analysis. The Asia-Pacific Education Researcher, 20(1), 118-126.

• West, B., Strogatz, S., McDill, J. M., & Cantwell, J. (1997). Interactive differential. Arbor, 1050, 48106.

• Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. Educational and Psychological Measurement, 73(6), 913-934. https://doi.org/10.1177%2F0013164413495237

• Wolters, C. A. (2004). Advancing achievement goal theory: Using goal structures and goal orientations to predict students’ motivation, cognition, and achievement. Journal of Educational Psychology, 96(2), 236-250. https://doi.org/10.1037/0022-0663.96.2.236

• Wolters, C. A., Shirley, L. Y., & Pintrich, P. R. (1996). The relation between goal orientation and students’ motivational beliefs and self-regulated learning. Learning and Individual Differences, 8(3), 211-238. https://doi.org/10.1016/S1041-6080(96)90015-1

• Yumusak, N., Sungur, S., & Cakiroglu, J. (2007). Turkish high school students’ biology achievement in relation to academic self-regulation. Educational Research and Evaluation, 13(1), 53-69. https://doi.org/10.1080/13803610600853749

• Zhou, J., & Urhahne, D. (2017). Self-regulated learning in the museum: Understanding the relationship of visitor’s goals, learning strategies, and appraisals. Scandinavian Journal of Educational Research, 61(4), 394-410. https://doi.org/10.1080/00313831.2016.1147071

• Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64-70.