Advertisement

Predicting Learners’ Success in a Self-paced MOOC Through Sequence Patterns of Self-regulated Learning

  • Jorge Maldonado-MahauadEmail author
  • Mar Pérez-Sanagustín
  • Pedro Manuel Moreno-Marcos
  • Carlos Alario-Hoyos
  • Pedro J. Muñoz-Merino
  • Carlos Delgado-Kloos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11082)

Abstract

In the past years, predictive models in Massive Open Online Courses (MOOCs) have focused on forecasting learners’ success through their grades. The prediction of these grades is useful to identify problems that might lead to dropouts. However, most models in prior work predict categorical and continuous variables using low-level data. This paper contributes to extend current predictive models in the literature by considering coarse-grained variables related to Self-Regulated Learning (SRL). That is, using learners’ self-reported SRL strategies and MOOC activity sequence patterns as predictors. Lineal and logistic regression modelling were used as a first approach of prediction with data collected from N = 2,035 learners who took a self-paced MOOC in Coursera. We identified two groups of learners: (1) Comprehensive, who follow the course path designed by the teacher; and (2) Targeting, who seek for the information required to pass assessments. For both type of learners, we found a group of variables as the most predictive: (1) the self-reported SRL strategies ‘goal setting’, ‘strategic planning’, ‘elaboration’ and ‘help seeking’; (2) the activity sequences patterns ‘only assessment’, ‘complete a video-lecture and try an assessment’, ‘explore the content’ and ‘try an assessment followed by a video-lecture’; and (3) learners’ prior experience, together with the self-reported interest in course assessments, and the number of active days and time spent in the platform. These results show how to predict with more accuracy when students reach a certain status taking in to consideration not only low-level data, but complex data such as their SRL strategies.

Keywords

Self-regulated learning Prediction Massive Open Online Courses Sequence patterns Achievement Success 

Notes

Acknowledgments

This work was supported by FONDECYT (Chile) under project initiation grant No.11150231, the MOOC-Maker Project (561533-EPP-1-2015-1-ES-EPPKA2-CBHE-JP), the LALA Project (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), and CONICYT/DOCTORADO NACIONAL 2016/21160081, the Spanish Ministry of Education, Culture and Sport, under an FPU fellowship (FPU016/00526) and the Spanish Ministry of Economy and Competiveness (Smartlet project, grant number TIN2017-85179-C3-1-R) funded by the Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER).

References

  1. 1.
    Van der Aalst, W.M.P.: Process mining: data science in action. Springer, Heidelberg (2016)CrossRefGoogle Scholar
  2. 2.
    Bannert, M.: Promoting self-regulated learning through prompts. Zeitschrift für Pädagogische Psychol. 23(2), 139–145 (2009)CrossRefGoogle Scholar
  3. 3.
    de Barba, P.G., et al.: The role of students’ motivation and participation in predicting performance in a MOOC. J. Comput. Assist. Learn. 32(3), 218–231 (2016)CrossRefGoogle Scholar
  4. 4.
    Boekaerts, M.: Self-regulated learning: a new concept embraced by researchers, policy makers, educators, teachers, and students. Learn. Instr. 7(2), 161–186 (1997)CrossRefGoogle Scholar
  5. 5.
    Brinton, C.G., et al.: Mining MOOC clickstreams: video-watching behavior vs. in-video quiz performance. IEEE Trans. Signal Process. 64(14), 3677–3692 (2016)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Broadbent, J.: Comparing online and blended learner’s self-regulated learning strategies and academic performance. Internet High. Educ. 33, 24–32 (2017)CrossRefGoogle Scholar
  7. 7.
    Broadbent, J., Poon, W.L.: Self-regulated learning strategies & academic achievement in online higher education learning environments: a systematic review. Internet High. Educ. 27, 1–13 (2015)CrossRefGoogle Scholar
  8. 8.
    Chuang, I., Ho, A.D.: HarvardX and MITx: Four Years of Open Online Courses–Fall 2012-Summer 2016 (2016)Google Scholar
  9. 9.
    Corrin, L., et al.: Using learning analytics to explore help-seeking learner profiles in MOOCs. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 424–428 (2017)Google Scholar
  10. 10.
    Davis, D., et al.: Activating learning at scale: a review of innovations in online learning strategies. Comput. Educ. 125, 327–344 (2018)CrossRefGoogle Scholar
  11. 11.
    Grainger, B.: Massive open online course (MOOC) report 2013. University of London. (2013)Google Scholar
  12. 12.
    Günther, C.W., Rozinat, A.: Disco: discover your processes. Bus. Process Manag. 940, 40–44 (2012)Google Scholar
  13. 13.
    Hood, N., et al.: Context counts: how learners’ contexts influence learning in a MOOC. Comput. Educ. 91, 83–91 (2015)CrossRefGoogle Scholar
  14. 14.
    Kizilcec, R.F., et al.: Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses. Comput. Educ. 104, 18–33 (2017)CrossRefGoogle Scholar
  15. 15.
    Kocdar, S., et al.: Measuring self-regulation in self-paced open and distance learning environments. Int. Rev. Res. Open Distrib. Learn. 19, 1 (2018)Google Scholar
  16. 16.
    Kovanović, V. et al.: Penetrating the black box of time-on-task estimation. In: Proceedings of the Fifth International Conference Learning Analytics and Knowledge - LAK 2015. October, pp. 184–193 (2015)Google Scholar
  17. 17.
    Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics. 33(1), 159–174 (1977)CrossRefGoogle Scholar
  18. 18.
    Littlejohn, A., et al.: Learning in MOOCs: motivations and self-regulated learning in MOOCs. Internet High. Educ. 29, 40–48 (2016)CrossRefGoogle Scholar
  19. 19.
    Maldonado-Mahauad, J., et al.: Mining theory-based patterns from Big data: identifying self-regulated learning strategies in Massive Open Online Courses. Comput. Hum. Behav. 80, 179–196 (2018)CrossRefGoogle Scholar
  20. 20.
    Maldonado, J.J., et al.: Exploring differences in how learners navigate in MOOCs based on self-regulated learning and learning styles: A process mining approach. In: Computing Conference (CLEI), 2016 XLII Latin American, pp. 1–12 (2016)Google Scholar
  21. 21.
    Mezaour, A.-D.: Filtering web documents for a thematic warehouse case study: eDot a food risk data warehouse (extended). In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds.) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol. 31, pp. 269–278. Springer, Berlin, Heidelberg (2005).  https://doi.org/10.1007/3-540-32392-9_28CrossRefGoogle Scholar
  22. 22.
    Moreno-Marcos, P.M., et al.: Analysing the predictive power for anticipating assignment grades in a massive open online course. Behav. Inf. Technol. 37(5), 1–16 (2018)Google Scholar
  23. 23.
    Pardo, A. et al.: Generating actionable predictive models of academic performance. In: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, pp. 474–478 (2016)Google Scholar
  24. 24.
    Reich, J.: Rebooting MOOC research. Science 347(6217), 34–35 (2015)CrossRefGoogle Scholar
  25. 25.
    Sinha, T. et al.: Your click decides your fate: Inferring information processing and attrition behavior from mooc video clickstream interactions. arXiv Prepr. arXiv1407.7131. (2014)Google Scholar
  26. 26.
    Xu, B., Yang, D.: Motivation classification and grade prediction for MOOCs learners. Comput. Intell. Neurosci. 2016, 4 (2016)MathSciNetGoogle Scholar
  27. 27.
    You, J.W.: Identifying significant indicators using LMS data to predict course achievement in online learning. Internet High. Educ. 29, 23–30 (2016)CrossRefGoogle Scholar
  28. 28.
    Zhao, C., et al.: Discover learning behavior patterns to predict certification. In: 2016 11th International Conference on Computer Science & Education (ICCSE), pp. 69–73 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jorge Maldonado-Mahauad
    • 1
    • 3
    Email author
  • Mar Pérez-Sanagustín
    • 1
    • 4
  • Pedro Manuel Moreno-Marcos
    • 2
  • Carlos Alario-Hoyos
    • 2
  • Pedro J. Muñoz-Merino
    • 2
  • Carlos Delgado-Kloos
    • 2
  1. 1.Department of Computer SciencePontificia Universidad Católica de ChileSantiagoChile
  2. 2.Department of Telematics EngineeringUniversidad Carlos III de MadridMadridSpain
  3. 3.Department of Computer ScienceUniversity of CuencaCuencaEcuador
  4. 4.Institut de Recherche en Informatique de ToulouseUniversité Tolouse III Paul SabatierToulouseFrance

Personalised recommendations