Abstract
Learning was once defined as the function of efforts spent in relation to efforts needed [3]. Provided that effort is closely linked to time, previous research has found a positive relationship between student effort over time and student success, both in university education and Massive Open Online Courses (MOOCs). With the complex environment of tracing and identifying relevant data of student learning processes in MOOCs, this study employs learning analytics to examine this relationship for MITx 6.00x, an introductory programming and computer science MOOC hosted on the edX MOOC platform. A population sample from the MOOC (Nā=ā32,621) was examined using logistic regression, controlling for variables that may also influence the outcome. Conversely, the outcome of this research study suggests that there is a curvilinear relationship between effort over time and student success, meaning those who exert effort for the longest amount of time in the MOOC actually have a lower probability of obtaining a certificate than others who exert effort over somewhat less time. Finally, research implications are discussed.
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References
Baker, R.S., Inventado, P.S.: Educational data mining and learning analytics. In: Learning Analytics: From Research to Practice, pp. 61ā75. Springer, New York (2014)
Bowman, N.A., Hill, P.L., Denson, N., Bronkema, R.: Keep on truckināor stay the course? Exploring grit dimensions as differential predictors of educational achievement, satisfaction, and intentions. Soc. Psychol. Pers. Sci. 6(6), 639ā645 (2015)
Carroll, J.: A model of school learning. Teach. Coll. Rec. 64(8), 723ā733 (1963)
Clow, D.: MOOCs and the funnel of participation. In: Proceedings of the Third International Conference on Learning Analytics and Knowledge, pp. 185ā189 (2013)
Cross, T.M.: The gritty: grit and non-traditional doctoral student success. J. Educ. Online 11(3), 1ā30 (2014)
Dweck, C.S.: The secret to raising smart kids. Sci. Am. Mind 18(6), 36ā43 (2007)
Field, A.P., Miles, J., Field, Z.: Discovering Statistics Using R. Sage, London (2012)
Firmin, R., Schiorring, E., Whitmer, J., Willett, T., Collins, E.D., Sujitparapitaya, S.: Case study: using MOOCs for conventional college coursework. Distance Educ. 35(2), 178ā201 (2014)
GaÅ”eviÄ, D., Dawson, S., Rogers, T., Gasevic, D.: Learning analytics should not promote one size fits all: the effects of instructional conditions in predicting academic success. Internet High. Educ. 28, 68ā84 (2016)
Gurria, A.: PISA 2015 results in focus. http://www.oecd.org/pisa/pisa-2015-results-in-focus.pdf (2018)
Hill, C., Corbett, C., St. Rose, A.: Why So Few? Women in Science, Technology, Engineering, and Mathematics. American Association of University Women (AAUW) (2010)
Khalil, H., Ebner, M.: MOOCs completion rates and possible methods to improve retention-a literature review. In: World Conference on Educational Multimedia, Hypermedia and Telecommunications, pp. 1305ā1313 (2014)
Khalil, M., Ebner, M.: What massive open online course (MOOC) stakeholders can learn from learning analytics? In: Spector, M., Lockee, B., Childress, M. (eds.) Learning, Design, and Technology: An International Compendium of Theory, Research, Practice, and Policy, pp. 1ā30. Springer, Heildelberg (2016). http://dx.doi.org/10.1007/978-3-319-17727-4_3-1
Kloft, M., Stiehler, F., Zheng, Z., Pinkwart, N.: Predicting MOOC dropout over weeks using machine learning methods. In: Proceedings of the EMNLP 2014 Workshop on Modeling Large Scale Social Interaction in MOOCs, pp. 60ā65 (2014)
Kuh, G.D., Cruce, T.M., Shoup, R., Kinzie, J., Gonyea, R.M.: Unmasking the effects of student engagement on first-year college grades and persistence. J. High. Educ. 79(5), 540ā563 (2008)
Lackner, E., Khalil, M., Ebner, M.: How to foster forum discussions within MOOCs: a case study. Int. J. Acad. Res. Educ. 2(2) (2016)
McAuley, A., Stewart, B., Siemens, G., Cormier, D.: The MOOC Model for Digital Practice (2010)
Mehmetoglu, M., Jakobsen, T.G.: Applied Statistics Using Stata: A Guide for the Social Sciences. Sage (2016)
MITx, HarvardX: HarvardX-MITx Person-Course Academic Year 2013 De-Identified dataset, version 2.0. Harvard Dataverse (2014). https://doi.org/10.7910/DVN/26147
Reeve, J.: Understanding Motivation and Emotion, 6th edn. Wiley (2014)
Reich, J., Emanuel, J., Nesterko, S., Seaton, D.T., Mullaney, T., Waldo, J., ā¦, Ho, A.D.: HeroesX: The Ancient Greek Hero: Spring 2013 Course Report (2014)
Seaton, D.T., Reich, J., Nesterko, S.O., Mullaney, T., Waldo, J., Ho, A.D., Chuang, I.: 6.00 x Introduction to Computer Science and Programming MITx on edX Course Report - 2012 Fall (2014)
Strayhorn, T.L.: What role does grit play in the academic success of black male collegians at predominantly white institutions? J. Afr. Am. Stud. 18(1), 1ā10 (2014)
Zhang, G., Anderson, T.J., Ohland, M.W., Thorndyke, B.R.: Identifying factors influencing engineering student graduation: a longitudinal and cross-institutional study. J. Eng. Educ. 93(4), 313ā320 (2004)
Wilkowski, J., Deutsch, A., Russell, D.M.: Student skill and goal achievement in the mapping with google MOOC. In: Proceedings of the First ACM Conference on Learning@ Scale Conference, pp. 3ā10 (2014)
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Samuelsen, J., Khalil, M. (2020). Study Effort and Student Success: A MOOC Case Study. In: Auer, M., Tsiatsos, T. (eds) The Challenges of the Digital Transformation in Education. ICL 2018. Advances in Intelligent Systems and Computing, vol 916. Springer, Cham. https://doi.org/10.1007/978-3-030-11932-4_22
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