The role of students' motivation and participation in predicting performance in a MOOC
Over the last 5 years, massive open online courses (MOOCs) have increasingly provided learning opportunities across the world in a variety of domains. As with many emerging educational technologies, why and how people come to MOOCs needs to be better understood and importantly what factors contribut...
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| Published in | Journal of computer assisted learning Vol. 32; no. 3; pp. 218 - 231 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.06.2016
Wiley-Blackwell Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0266-4909 1365-2729 |
| DOI | 10.1111/jcal.12130 |
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| Abstract | Over the last 5 years, massive open online courses (MOOCs) have increasingly provided learning opportunities across the world in a variety of domains. As with many emerging educational technologies, why and how people come to MOOCs needs to be better understood and importantly what factors contribute to learners' MOOC performance. It is known that online learning environments require greater levels of self‐regulation, and that high levels of motivation are crucial to activate these skills. However, motivation is a complex construct and research on how it functions in MOOCs is still in its early stages. Research presented in this article investigated how motivation and participation influence students' performance in a MOOC, more specifically those students who persist to the end of the MOOC. Findings indicated that the strongest predictor of performance was participation, followed by motivation. Motivation influenced and was influenced by students' participation during the course. Moreover, situational interest played a crucial role in mediating the impact of general intrinsic motivation and participation on performance. The results are discussed in relation to how educators and designers of MOOCs can use knowledge emerging from motivational assessments and participation measures gleaned from learning analytics to tailor the design and delivery of courses. © 2016 John Wiley & Sons Ltd |
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| AbstractList | Over the last 5years, massive open online courses (MOOCs) have increasingly provided learning opportunities across the world in a variety of domains. As with many emerging educational technologies, why and how people come to MOOCs needs to be better understood and importantly what factors contribute to learners' MOOC performance. It is known that online learning environments require greater levels of self-regulation, and that high levels of motivation are crucial to activate these skills. However, motivation is a complex construct and research on how it functions in MOOCs is still in its early stages. Research presented in this article investigated how motivation and participation influence students' performance in a MOOC, more specifically those students who persist to the end of the MOOC. Findings indicated that the strongest predictor of performance was participation, followed by motivation. Motivation influenced and was influenced by students' participation during the course. Moreover, situational interest played a crucial role in mediating the impact of general intrinsic motivation and participation on performance. The results are discussed in relation to how educators and designers of MOOCs can use knowledge emerging from motivational assessments and participation measures gleaned from learning analytics to tailor the design and delivery of courses. Over the last 5 years, massive open online courses (MOOCs) have increasingly provided learning opportunities across the world in a variety of domains. As with many emerging educational technologies, why and how people come to MOOCs needs to be better understood and importantly what factors contribute to learners' MOOC performance. It is known that online learning environments require greater levels of self‐regulation, and that high levels of motivation are crucial to activate these skills. However, motivation is a complex construct and research on how it functions in MOOCs is still in its early stages. Research presented in this article investigated how motivation and participation influence students' performance in a MOOC, more specifically those students who persist to the end of the MOOC. Findings indicated that the strongest predictor of performance was participation, followed by motivation. Motivation influenced and was influenced by students' participation during the course. Moreover, situational interest played a crucial role in mediating the impact of general intrinsic motivation and participation on performance. The results are discussed in relation to how educators and designers of MOOCs can use knowledge emerging from motivational assessments and participation measures gleaned from learning analytics to tailor the design and delivery of courses. © 2016 John Wiley & Sons Ltd Over the last 5years, massive open online courses (MOOCs) have increasingly provided learning opportunities across the world in a variety of domains. As with many emerging educational technologies, why and how people come to MOOCs needs to be better understood and importantly what factors contribute to learners' MOOC performance. It is known that online learning environments require greater levels of self-regulation, and that high levels of motivation are crucial to activate these skills. However, motivation is a complex construct and research on how it functions in MOOCs is still in its early stages. Research presented in this article investigated how motivation and participation influence students' performance in a MOOC, more specifically those students who persist to the end of the MOOC. Findings indicated that the strongest predictor of performance was participation, followed by motivation. Motivation influenced and was influenced by students' participation during the course. Moreover, situational interest played a crucial role in mediating the impact of general intrinsic motivation and participation on performance. The results are discussed in relation to how educators and designers of MOOCs can use knowledge emerging from motivational assessments and participation measures gleaned from learning analytics to tailor the design and delivery of courses. © 2016 John Wiley & Sons Ltd Over the last 5 years, massive open online courses (MOOCs) have increasingly provided learning opportunities across the world in a variety of domains. As with many emerging educational technologies, why and how people come to MOOCs needs to be better understood and importantly what factors contribute to learners' MOOC performance. It is known that online learning environments require greater levels of self-regulation, and that high levels of motivation are crucial to activate these skills. However, motivation is a complex construct and research on how it functions in MOOCs is still in its early stages. Research presented in this article investigated how motivation and participation influence students' performance in a MOOC, more specifically those students who persist to the end of the MOOC. Findings indicated that the strongest predictor of performance was participation, followed by motivation. Motivation influenced and was influenced by students' participation during the course. Moreover, situational interest played a crucial role in mediating the impact of general intrinsic motivation and participation on performance. The results are discussed in relation to how educators and designers of MOOCs can use knowledge emerging from motivational assessments and participation measures gleaned from learning analytics to tailor the design and delivery of courses. |
| Author | de Barba, P.G. Kennedy, G.E. Ainley, M.D. |
| Author_xml | – sequence: 1 givenname: P.G. surname: de Barba fullname: de Barba, P.G. email: paula.de@unimelb.edu.au organization: Melbourne Centre for the Study of Higher Education, The University of Melbourne, Australia – sequence: 2 givenname: G.E. surname: Kennedy fullname: Kennedy, G.E. organization: Melbourne Centre for the Study of Higher Education, The University of Melbourne, Australia – sequence: 3 givenname: M.D. surname: Ainley fullname: Ainley, M.D. organization: Melbourne School of Psychological Sciences, The University of Melbourne, Australia |
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| References | Durik, A. M., & Harackiewicz, J. M. (2007). Different strokes for different folks: How individual interest moderates the effects of situational factors on task interest. Journal of Educational Psychology, 99, 597-610. doi:10.1037/0022-0663.99.3.597. Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109-132. doi:10.1146/annurev.psych.53.100901.135153. Gogol, K., Brunner, M., Goetz, T., Martin, R., Ugen, S., Keller, U., ... Prenzel, F. (2014). 'My questionnaire is too long!' The assessments of motivational-affective constructs with three-item and single-item measures. Contemporary Educational Psychology, 39, 188-205. doi:10.1016/j.cedpsych.2014.04.002. Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41, 111-127. doi:10.1207/s15326985ep4102_4. Breslow, L., Pritchard, D. E., DeBoer, J., Stump, G. S., Ho, A. D., & Seaton, D. T. (2013). Studying learning in the worldwide classroom: Research into edX's first MOOC. Research & Practice in Assessment, 8, 13-25. Retrieved from http://mooc.pku.edu.cn/source/resource/01.pdf McGregor, H. A., & Elliot, A. J. (2002). Achievement goals as predictors of achievement-relevant processes prior to task engagement. Journal of Educational Psychology, 94, 381-394. doi:10.1037/0022-0663.94.2.381. Yuan, L., & Powell, S. (2013). MOOCs and open education: Implications for higher education [White paper]. Bolton, UK: Joint Information Systems Committee, Centre for Educational Technology and Interoperability Standards. Retrieved from http://publications.cetis.ac.uk/2013/667 Hart, C. (2012). Factors associated with student persistence in an online program of study: a review of the literature. Journal of Interactive Online Learning, 11, 19-42. Retrieved from http://www.ncolr.org/jiol/issues/pdf/11.1.2.pdf Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25, 68-81. doi:10.1006/ceps.1999.1015. Daniel, J. (2012). Making sense of MOOCs: Musings in a maze of myth, paradox and possibility. Journal of Interactive Media in Education, 3, 1-20. doi:10.5334/2012-18. Mooney, C. Z., & Duval, R. D. (1993). Bootstrapping: A nonparametric approach to statistical inference. Newbury Park, CA: Sage. Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95, 667-686. doi:10.1037/0022-0663.95.4.667. Hulleman, C. S. (2007). The role of utility value in the development of interest and achievement. Madison, WI: Unpublished doctoral dissertation, University of Wisconsin. Vanthournout, G., Gijbels, D., Coertjens, L., Donche, V., & Van Petegem, P. (2012). Students' persistence and academic success in a first-year professional bachelor program: The influence of students' learning strategies and academic motivation. Education Research International, 2012, 1-10. doi:10.1155/2012/152747. Blackwell, L. S., Trzesniewski, K. H., & Dweck, C. S. (2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78, 246-263. doi:10.1111/j.1467-8624.2007.00995.x. Garson, G. D. (2012). Structural equation modeling. Asheboro, NC: Statistical Associates Publishers. Ainley, M. D., & Patrick, L. (2006). Measuring self-regulated learning processes through tracking patterns of student interaction with achievement activities. Educational Psychology Review, 18, 267-286. doi:10.1007/s10648-006-9018-z. Senko, C., Hulleman, C. S., & Harackiewicz, J. M. (2011). Achievement goal theory at the crossroads: Old controversies, current challenges, and new directions. Educational Psychologist, 46, 26-47. doi:10.1080/00461520.2011.538646. Liyanagunawardena, T. R., Adams, A. A., & Williams, S. A. (2013). MOOCs: a systematic study of the published literature 2008-2012. The International Review of Research in Open and Distance Learning, 14. Retrieved from http://www.irrodl.org Wigfield, A., & Cambria, J. (2010). Students' achievement values, goal orientations, and interest: Definitions, development, and relations to achievement outcomes. Developmental Review, 30(1), 1-35. doi:10.1016/j.dr.2009.12.001. Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2. Retrieved from http://www.itdl.org Kennedy, G. E. (2004). Promoting cognition in multimedia interactivity research. Journal of Interactive Learning Research, 15, 43-61. Yang, C. C., Tsai, I., Kim, B., Cho, M. H., & Laffey, J. M. (2006). Exploring the relationships between students' academic motivation and social ability in online learning environments. The Internet and Higher Education, 9, 277-286. doi:10.1016/j.iheduc.2006.08.002. Ames, C. (1992). Classrooms: Goals, structures, and student motivation. Journal of Educational Psychology, 84, 261-271. doi:10.1037/0022-0663.84.3.261. Milligan, C., Littlejohn, A., & Margaryan, A. (2013). Patterns of engagement in connectivist MOOCs. MERLOT Journal of Online Learning and Teaching, 9. Retrieved from http://jolt.merlot.org Hidi, S. (1990). Interest and its contribution as a mental resource for learning. Review of Educational Research, 60, 549-571. doi:10.3102/00346543060004549. Wanous, J. P., Reichers, A. E., & Hudy, M. J. (1997). Overall job satisfaction: How good are single-item measures? Journal of Applied Psychology, 82, 247-252. doi:10.1037/0021-9010.82.2.247. Harackiewicz, J. M., Durik, A. M., Barron, K. E., Linnenbrink-Garcia, L., & Tauer, J. M. (2008). The role of achievement goals in the development of interest: Reciprocal relations between achievement goals, interest, and performance. Journal of Educational Psychology, 100, 105-122. doi:10.1037/0022-0663.100.1.105. Sun, J. C. Y., & Rueda, R. (2012). Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43, 191-204. doi:10.1111/j.1467-8535.2010.01157.x. Pintrich, P. R. (2000). Multiple goals, multiple pathways: The role of goal orientation in learning and achievement. Journal of Educational Psychology, 92, 544-555. doi:10.1037/0022-0663.92.3.544. Littlejohn, A. (2013). Understanding Massive Open Online Courses. UK, Caledonian Academy Glasgow, Caledonian University. Retrieved from http://cemca.org.in/ckfinder/userfiles/files/EdTech%20Notes%202_Littlejohn_final_1June2013.pdf Nicholls, J. G. (1984). Achievement motivation: Conceptions of ability, subjective experience, task choice, and performance. Psychological Review, 91, 328-346. doi:10.1037/0033-295X.91.3.328. Pintrich, P. R., Smith, D. A., García, T., & McKeachie, W. J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). Ann Arbor, MI: National Centre For Research to Improve Postsecondary Teaching and Learning. Elliot, A. J., & McGregor, H. A. (2001). A 2 × 2 achievement goal framework. Journal of Personality and Social Psychology, 80, 501-519. doi:10.1037/0022-3514.80.3.501. Christensen, G., Steinmetz, A., Alcorn, B., Bennett, A., Woods, D., & Emanuel, E. J. (2013). The MOOC phenomenon: Who takes massive open online courses and why? Social Science Research Network. doi:10.2139/ssrn.2350964. Vallerand, R. J., Fortier, M. S., & Guay, F. (1997). Self-determination and persistence in a real-life setting: Toward a motivational model of high school dropout. Journal of Personality and Social Psychology, 72, 1161-1175. doi:10.1037/0022-3514.72.5.1161. Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist, 41, 1040-1048. doi:10.1037/0003-066X.41.10.1040. Ainley, M. D. (2006). 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Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42, 185-227. doi:10.1080/00273170701341316. Belenky, D. M., & Nokes-Malach, T. J. (2012). Motivation and transfer: The role of mastery-approach goals in preparation for future learning. Journal of the Learning Sciences, 21, 399-432. doi:10.1080/10508406.2011.651232. Rotgans, J. I., & Schmidt, H. G. (2011). Situational interest and academic achievement in the active-learning classroom. Learning and Instruction, 21, 58-67. doi:10.1016/j.learninstruc.2009.11.001. Bollen, K. A., & Stine, R. A. (1992). Bootstrapping goodness-of-fit measures in structural equation models. 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(e_1_2_7_42_1) 1991 e_1_2_7_6_1 e_1_2_7_4_1 Siemens G. (e_1_2_7_47_1) 2005; 2 e_1_2_7_8_1 Eccles J. S. (e_1_2_7_17_1) 1998 e_1_2_7_18_1 Liyanagunawardena T. R. (e_1_2_7_32_1) 2013; 14 e_1_2_7_16_1 e_1_2_7_40_1 e_1_2_7_2_1 e_1_2_7_14_1 e_1_2_7_12_1 e_1_2_7_10_1 e_1_2_7_46_1 Milligan C. (e_1_2_7_34_1) 2013; 9 e_1_2_7_48_1 e_1_2_7_29_1 De Barba P. (e_1_2_7_13_1) 2015 e_1_2_7_51_1 e_1_2_7_30_1 e_1_2_7_53_1 e_1_2_7_24_1 e_1_2_7_55_1 e_1_2_7_22_1 e_1_2_7_20_1 e_1_2_7_36_1 e_1_2_7_38_1 Renninger K. A. (e_1_2_7_44_1) 1990 |
| References_xml | – reference: Ainley, M. D., & Patrick, L. (2006). Measuring self-regulated learning processes through tracking patterns of student interaction with achievement activities. Educational Psychology Review, 18, 267-286. doi:10.1007/s10648-006-9018-z. – reference: Liyanagunawardena, T. R., Adams, A. A., & Williams, S. A. (2013). MOOCs: a systematic study of the published literature 2008-2012. The International Review of Research in Open and Distance Learning, 14. Retrieved from http://www.irrodl.org – reference: Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36, 717-731. doi:10.3758/BF03206553. – reference: Breslow, L., Pritchard, D. E., DeBoer, J., Stump, G. S., Ho, A. D., & Seaton, D. T. (2013). Studying learning in the worldwide classroom: Research into edX's first MOOC. Research & Practice in Assessment, 8, 13-25. Retrieved from http://mooc.pku.edu.cn/source/resource/01.pdf – reference: Kennedy, G. E. (2004). Promoting cognition in multimedia interactivity research. Journal of Interactive Learning Research, 15, 43-61. – reference: Ainley, M. D. (2006). Connecting with learning: Motivation, affect and cognition in interest processes. Educational Psychology Review, 18, 391-405. doi:10.1007/s10648-006-9033-0. – reference: Blackwell, L. S., Trzesniewski, K. H., & Dweck, C. S. (2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78, 246-263. doi:10.1111/j.1467-8624.2007.00995.x. – reference: Gogol, K., Brunner, M., Goetz, T., Martin, R., Ugen, S., Keller, U., ... Prenzel, F. (2014). 'My questionnaire is too long!' The assessments of motivational-affective constructs with three-item and single-item measures. Contemporary Educational Psychology, 39, 188-205. doi:10.1016/j.cedpsych.2014.04.002. – reference: Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42, 185-227. doi:10.1080/00273170701341316. – reference: Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41, 111-127. doi:10.1207/s15326985ep4102_4. – reference: Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109-132. doi:10.1146/annurev.psych.53.100901.135153. – reference: Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2. Retrieved from http://www.itdl.org – reference: Vanthournout, G., Gijbels, D., Coertjens, L., Donche, V., & Van Petegem, P. (2012). Students' persistence and academic success in a first-year professional bachelor program: The influence of students' learning strategies and academic motivation. Education Research International, 2012, 1-10. doi:10.1155/2012/152747. – reference: Christensen, G., Steinmetz, A., Alcorn, B., Bennett, A., Woods, D., & Emanuel, E. J. (2013). The MOOC phenomenon: Who takes massive open online courses and why? Social Science Research Network. doi:10.2139/ssrn.2350964. – reference: Pintrich, P. R., Smith, D. A., García, T., & McKeachie, W. J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). Ann Arbor, MI: National Centre For Research to Improve Postsecondary Teaching and Learning. – reference: Yuan, L., & Powell, S. (2013). MOOCs and open education: Implications for higher education [White paper]. Bolton, UK: Joint Information Systems Committee, Centre for Educational Technology and Interoperability Standards. Retrieved from http://publications.cetis.ac.uk/2013/667 – reference: Rotgans, J. I., & Schmidt, H. G. (2011). Situational interest and academic achievement in the active-learning classroom. Learning and Instruction, 21, 58-67. doi:10.1016/j.learninstruc.2009.11.001. – reference: Pintrich, P. R. (2000). Multiple goals, multiple pathways: The role of goal orientation in learning and achievement. Journal of Educational Psychology, 92, 544-555. doi:10.1037/0022-0663.92.3.544. – reference: Belenky, D. M., & Nokes-Malach, T. J. (2012). Motivation and transfer: The role of mastery-approach goals in preparation for future learning. Journal of the Learning Sciences, 21, 399-432. doi:10.1080/10508406.2011.651232. – reference: Yang, C. C., Tsai, I., Kim, B., Cho, M. H., & Laffey, J. M. (2006). Exploring the relationships between students' academic motivation and social ability in online learning environments. The Internet and Higher Education, 9, 277-286. doi:10.1016/j.iheduc.2006.08.002. – reference: Daniel, J. (2012). Making sense of MOOCs: Musings in a maze of myth, paradox and possibility. Journal of Interactive Media in Education, 3, 1-20. doi:10.5334/2012-18. – reference: Wigfield, A., & Cambria, J. (2010). Students' achievement values, goal orientations, and interest: Definitions, development, and relations to achievement outcomes. Developmental Review, 30(1), 1-35. doi:10.1016/j.dr.2009.12.001. – reference: Garson, G. D. (2012). Structural equation modeling. Asheboro, NC: Statistical Associates Publishers. – reference: Hidi, S. (1990). Interest and its contribution as a mental resource for learning. Review of Educational Research, 60, 549-571. doi:10.3102/00346543060004549. – reference: Hidi, S., & Harackiewicz, J. M. (2000). Motivating the academically unmotivated: A critical issue for the 21st century. Review of Educational Research, 70, 151-179. doi:10.3102/00346543070002151. – reference: Ames, C. (1992). Classrooms: Goals, structures, and student motivation. Journal of Educational Psychology, 84, 261-271. doi:10.1037/0022-0663.84.3.261. – reference: Milligan, C., Littlejohn, A., & Margaryan, A. (2013). Patterns of engagement in connectivist MOOCs. MERLOT Journal of Online Learning and Teaching, 9. Retrieved from http://jolt.merlot.org – reference: Wigfield, A., & Eccles, J. S. (1992). The development of achievement task values: A theoretical analysis. Developmental Review, 12, 265-310. doi:10.1016/0273-2297(92)90011-P. – reference: Harackiewicz, J. M., Durik, A. M., Barron, K. E., Linnenbrink-Garcia, L., & Tauer, J. M. (2008). The role of achievement goals in the development of interest: Reciprocal relations between achievement goals, interest, and performance. Journal of Educational Psychology, 100, 105-122. doi:10.1037/0022-0663.100.1.105. – reference: Hart, C. (2012). Factors associated with student persistence in an online program of study: a review of the literature. Journal of Interactive Online Learning, 11, 19-42. Retrieved from http://www.ncolr.org/jiol/issues/pdf/11.1.2.pdf – reference: Wanous, J. P., Reichers, A. E., & Hudy, M. J. (1997). Overall job satisfaction: How good are single-item measures? Journal of Applied Psychology, 82, 247-252. doi:10.1037/0021-9010.82.2.247. – reference: De Barba, P. (2015). Achievement motivation and autonomous learning in online environments. Australia: PhD thesis in preparation, The University of Melbourne. – reference: Littlejohn, A. (2013). Understanding Massive Open Online Courses. UK, Caledonian Academy Glasgow, Caledonian University. Retrieved from http://cemca.org.in/ckfinder/userfiles/files/EdTech%20Notes%202_Littlejohn_final_1June2013.pdf – reference: McGregor, H. A., & Elliot, A. J. (2002). Achievement goals as predictors of achievement-relevant processes prior to task engagement. Journal of Educational Psychology, 94, 381-394. doi:10.1037/0022-0663.94.2.381. – reference: Durik, A. M., & Harackiewicz, J. M. (2007). Different strokes for different folks: How individual interest moderates the effects of situational factors on task interest. Journal of Educational Psychology, 99, 597-610. doi:10.1037/0022-0663.99.3.597. – reference: Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist, 41, 1040-1048. doi:10.1037/0003-066X.41.10.1040. – reference: Elliot, A. J., & McGregor, H. A. (2001). A 2 × 2 achievement goal framework. Journal of Personality and Social Psychology, 80, 501-519. doi:10.1037/0022-3514.80.3.501. – reference: Mooney, C. Z., & Duval, R. D. (1993). Bootstrapping: A nonparametric approach to statistical inference. Newbury Park, CA: Sage. – reference: Senko, C., Hulleman, C. S., & Harackiewicz, J. M. (2011). Achievement goal theory at the crossroads: Old controversies, current challenges, and new directions. Educational Psychologist, 46, 26-47. doi:10.1080/00461520.2011.538646. – reference: Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95, 667-686. doi:10.1037/0022-0663.95.4.667. – reference: Sun, J. C. Y., & Rueda, R. (2012). Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43, 191-204. doi:10.1111/j.1467-8535.2010.01157.x. – reference: Vallerand, R. J., Fortier, M. S., & Guay, F. (1997). Self-determination and persistence in a real-life setting: Toward a motivational model of high school dropout. Journal of Personality and Social Psychology, 72, 1161-1175. doi:10.1037/0022-3514.72.5.1161. – reference: Hulleman, C. S. (2007). The role of utility value in the development of interest and achievement. Madison, WI: Unpublished doctoral dissertation, University of Wisconsin. – reference: Nicholls, J. G. (1984). Achievement motivation: Conceptions of ability, subjective experience, task choice, and performance. Psychological Review, 91, 328-346. doi:10.1037/0033-295X.91.3.328. – reference: Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25, 68-81. doi:10.1006/ceps.1999.1015. – reference: Bollen, K. A., & Stine, R. A. (1992). Bootstrapping goodness-of-fit measures in structural equation models. 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success in a first‐year professional bachelor program: The influence of students' learning strategies and academic motivation publication-title: Education Research International – volume: 39 start-page: 188 year: 2014 end-page: 205 article-title: ‘My questionnaire is too long!’ The assessments of motivational‐affective constructs with three‐item and single‐item measures publication-title: Contemporary Educational Psychology – start-page: 127 year: 1990 end-page: 165 – volume: 92 start-page: 544 year: 2000 end-page: 555 article-title: Multiple goals, multiple pathways: The role of goal orientation in learning and achievement publication-title: Journal of Educational Psychology – volume: 12 start-page: 265 year: 1992 end-page: 310 article-title: The development of achievement task values: A theoretical analysis publication-title: Developmental Review – volume: 95 start-page: 667 year: 2003 end-page: 686 article-title: A motivational science perspective on the role of student motivation in learning and teaching contexts publication-title: Journal of Educational Psychology – start-page: 170 year: 2013 end-page: 179 – volume: 43 start-page: 191 year: 2012 end-page: 204 article-title: Situational interest, computer self‐efficacy and self‐regulation: Their impact on student engagement in distance education publication-title: British Journal of Educational Technology – volume: 53 start-page: 109 year: 2002 end-page: 132 article-title: Motivational beliefs, values, and goals publication-title: Annual Review of Psychology – volume: 91 start-page: 328 year: 1984 end-page: 346 article-title: Achievement motivation: Conceptions of ability, subjective experience, task choice, and performance publication-title: Psychological Review – volume: 70 start-page: 151 year: 2000 end-page: 179 article-title: Motivating the academically unmotivated: A critical issue for the 21st century publication-title: Review of Educational Research – volume: 78 start-page: 246 year: 2007 end-page: 263 article-title: Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention publication-title: Child Development – volume: 3 start-page: 1 year: 2012 end-page: 20 article-title: Making sense of MOOCs: Musings in a maze of myth, paradox and possibility publication-title: Journal of Interactive Media in Education – volume: 2 year: 2005 article-title: Connectivism: A learning theory for the digital age publication-title: International Journal of Instructional Technology and Distance Learning – volume: 18 start-page: 267 year: 2006 end-page: 286 article-title: Measuring self‐regulated learning processes through tracking patterns of student interaction with achievement activities publication-title: Educational Psychology Review – year: 1993 – year: 2015 – start-page: 289 year: 2013 end-page: 292 – volume: 99 start-page: 597 year: 2007 end-page: 610 article-title: Different strokes for different folks: How individual interest 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| Title | The role of students' motivation and participation in predicting performance in a MOOC |
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