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How to Quantify Student’s Regularity?

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9891))

Abstract

Studies carried out in classroom-based learning context, have consistently shown a positive relation between students’ conscientiousness and their academic success. We hypothesize that time management and regularity are main constructing blocks of students’ conscientiousness in the context of online education. In online education, despite intuitive arguments supporting on-demand courses as more flexible delivery of knowledge, completion rate is higher in the courses with rigid temporal constraints and structure. In this study, we further investigate how students’ regularity affects their learning outcome in MOOCs. We propose several measures to quantify students regularity. We validate accuracy of these measures as predictors of students’ performance in the course.

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References

  1. Blair, C., Diamond, A.: Biological processes in prevention and intervention: the promotion of self-regulation as a means of preventing school failure. Dev. Psychopathol. 20(03), 899–911 (2008)

    Article  Google Scholar 

  2. Brockwell, P.J., Davis, R.A.: Time Series: Theory and Methods. Springer Science & Business Media, New York (2013)

    MATH  Google Scholar 

  3. Dillenbourg, P., Li, N., Kidziński, Ł.: The complications of the orchestration clock. In: From Books to MOOCs? Emerging Models of Learning and Teaching in Higher Education. Portland Press (2016)

    Google Scholar 

  4. Eyal, N.: Hooked: How to Build Habit-Forming Products. Penguin Canada, Toronto (2014)

    Google Scholar 

  5. Ferrari, J.R., Ware, C.B.: Academic procrastination: personality. J. Soc. Behav. Pers. 7(3), 495–502 (1992)

    Google Scholar 

  6. Jönsson, P., Eklundh, L.: Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans. Geosci. Remote Sens. 40(8), 1824–1832 (2002)

    Article  Google Scholar 

  7. Kennedy, G., Coffrin, C., de Barba, P., Corrin, L.: Predicting success: how learners’ prior knowledge, skills and activities predict mooc performance. In: Proceedings of the Fifth International Conference on Learning Analytics and Knowledge, pp. 136–140. ACM (2015)

    Google Scholar 

  8. Kizilcec, R.F., Halawa, S.: Attrition and achievement gaps in online learning. In: Proceedings of the Second ACM Conference on Learning@Scale, pp. 57–66. ACM (2015)

    Google Scholar 

  9. Klassen, R.M., Krawchuk, L.L., Rajani, S.: Academic procrastination of undergraduates: low self-efficacy to self-regulate predicts higher levels of procrastination. Contemp. Educ. Psychol. 33(4), 915–931 (2008)

    Article  Google Scholar 

  10. Lauría, E.J., Baron, J.D., Devireddy, M., Sundararaju, V., Jayaprakash, S.M.: Mining academic data to improve college student retention: an open source perspective. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 139–142. ACM (2012)

    Google Scholar 

  11. Lay, C.H.: At last, my research article on procrastination. J. Res. Pers. 20(4), 474–495 (1986)

    Article  Google Scholar 

  12. Li, N., Kidziński, Ł., Jermann, P., Dillenbourg, P.: MOOC video interaction patterns: what do they tell us? In: Conole, G., Klobučar, T., Rensing, C., Konert, J., Lavoué, E. (eds.) Design for Teaching and Learning in a Networked World. LNCS, vol. 9307, pp. 197–210. Springer, Heidelberg (2015)

    Google Scholar 

  13. Lin, J.: Divergence measures based on the shannon entropy. IEEE Trans. Inf. Theor. 37(1), 145–151 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  14. McAuley, A., Stewart, B., Siemens, G., Cormier, D.: The MOOC model for digital practice (2010)

    Google Scholar 

  15. Nawrot, I., Doucet, A.: Building engagement for MOOC students: introducing support for time management on online learning platforms. In: Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion, pp. 1077–1082 (2014)

    Google Scholar 

  16. OConnor, M.C., Paunonen, S.V.: Big five personality predictors of post-secondary academic performance. Pers. Individ. Differ. 43(5), 971–990 (2007)

    Article  Google Scholar 

  17. Paredes, W.C., Chung, K.S.K.: Modelling learning & performance: a social networks perspective. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 34–42. ACM (2012)

    Google Scholar 

  18. Percival, D.B., Walden, A.T.: Spectral Analysis for Physical Applications. Cambridge University Press, Cambridge (1993)

    Book  MATH  Google Scholar 

  19. Poropat, A.E.: A meta-analysis of the five-factor model of personality and academic performance. Psychol. Bull. 135(2), 322 (2009)

    Article  Google Scholar 

  20. Boroujeni, M.S., Kidziński, Ł., Dillenbourg, P.: How employment constrains participation in MOOCS? In: Proceedings of the 9th International Conference on Educational Data Mining, pp. 376–377 (2016)

    Google Scholar 

  21. Christopher, A.: Sims: seasonality in regression. J. Am. Stat. Assoc. 69(347), 618–626 (1974)

    Article  Google Scholar 

  22. Solomon, L.J., Rothblum, E.D.: Academic procrastination: frequency and cognitive-behavioral correlates. J. Couns. Psychol. 31(4), 503 (1984)

    Article  Google Scholar 

  23. Trapmann, S., Hell, B., Hirn, J.-O.W., Schuler, H.: Meta-analysis of the relationship between the big five and academic success at university. Zeitschrift für Psychologie/J. Psychol. 215(2), 132–151 (2007)

    Article  Google Scholar 

  24. Vetterli, M., Kovačević, J., Goyal, V.K.: Foundations of Signal Processing. Cambridge University Press, Cambridge (2014)

    Google Scholar 

  25. Wolff, A., Zdrahal, Z., Nikolov, A., Pantucek, M.: Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment. In: Proceedings of the Third International Conference on Learning Analytics and Knowledge, pp. 145–149. ACM (2013)

    Google Scholar 

  26. Zimmerman, B.J.: Investigating self-regulation and motivation: historical background methodological developments and future prospects. Am. Educ. Res. J. 45(1), 166–183 (2008)

    Article  Google Scholar 

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Correspondence to Mina Shirvani Boroujeni .

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Boroujeni, M.S., Sharma, K., Kidziński, Ł., Lucignano, L., Dillenbourg, P. (2016). How to Quantify Student’s Regularity?. In: Verbert, K., Sharples, M., Klobučar, T. (eds) Adaptive and Adaptable Learning. EC-TEL 2016. Lecture Notes in Computer Science(), vol 9891. Springer, Cham. https://doi.org/10.1007/978-3-319-45153-4_21

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  • DOI: https://doi.org/10.1007/978-3-319-45153-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45152-7

  • Online ISBN: 978-3-319-45153-4

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