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A predictive model for the identification of learning styles in MOOC environments

  • Brahim HmednaEmail author
  • Ali El Mezouary
  • Omar Baz
Article

Abstract

Massive online open course (MOOC) platform generates a large amount of data, which provides many opportunities for studying the behaviors of learners. In parallel, recent advancements in machine learning techniques and big data analysis have created new opportunities for a better understanding of how learners behave and learn in environments known for their massiveness and openness. The work is about predicting learners’ learning styles based on their learning traces. The Felder Silverman learning style model (FSLSM) is adopted since it is one of the most commonly used models in technology-enhanced learning. In order to attend our objective, we analyzed data collected from the edX course “statistical learning” (session Winter 2015 and Winter 2016), administered via Stanford’s Logunita platform. The results show that decision tree performs best for all 3 dimensions, with an accuracy of higher than 98% and a reduced risk of overfitting the training data.

Keywords

MOOC Learning styles FSLSM Machine learning 

Notes

Acknowledgements

The authors are grateful to CAROL (the center for advanced research through online learning), university of Stanford, for providing the Dataset necessary for accomplishing this research.

References

  1. 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 Analysis of Large Scale Social Interaction in MOOCs, pp. 60–65 (2014)Google Scholar
  2. 2.
    Papathoma, T., Blake, C., Clow, D., Scanlon, E.: Investigating learners’ views of assessment types in Massive Open Online Courses (MOOCs). In: Design for Teaching and Learning in a Networked World, pp. 617–621. Springer (2015)Google Scholar
  3. 3.
    Bakki, A., Oubahssi, L., George, S., Cherkaoui, C.: A Model to assist pedagogical scenario building process in cMOOCs. In: 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT), pp. 5–7. IEEE (2017)Google Scholar
  4. 4.
    Guàrdia, L., Maina, M., Sangrà, A.: Mooc design principles: a pedagogical approach from the learner’s perspective. eLearning Papers (2013)Google Scholar
  5. 5.
    Mackness, J., Mak, S., Williams, R.: The ideals and reality of participating in a mooc. In: Proceedings of the 7th international Conference on Networked Learning 2010. University of Lancaster (2010)Google Scholar
  6. 6.
    Cisel, M.: MOOC: ce que les taux d’abandon signifient | La révolution MOOC (2013)Google Scholar
  7. 7.
    Onah, D.F.O., Sinclair, J., Boyatt, R.: Dropout rates of massive open online courses: behavioural patterns. EDULEARN14 proceedings 1, 5825–5834 (2014)Google Scholar
  8. 8.
    Nordin, N., Norman, H., Embi, M.A.: Technology acceptance of massive open online courses in malaysia. Malaysian J. Dist. Educ., 17(2), (2015)CrossRefGoogle Scholar
  9. 9.
    Coffield, F., Moseley, D., Hall, E., Ecclestone, K., et al.: Learning styles and pedagogy in post-16 learning: asystematic and critical review. Learning and Skills Research Centre London (2004). http://www.voced.edu.au/content/ngv:13692
  10. 10.
    Felder, R.M., Silverman, L.K.: Learning and teaching styles in engineering education. Eng. Educ. 78(7), 674–681 (1988)Google Scholar
  11. 11.
    Graf, S., Liu, T.-C.: Supporting teachers in identifying students’ learning styles in learning management systems: an automatic student modelling approach. J. Educ. Technol. Soc. 12(4), 3 (2009)Google Scholar
  12. 12.
    Bernard, J., Chang, T.-W., Popescu, E., Graf, S.: Learning style Identifier: improving the precision of learning style identification through computational intelligence algorithms. Expert Syst. Appl. 75, 94–108 (2017)CrossRefGoogle Scholar
  13. 13.
    Kuljis, J., Liu, F.: A comparison of learning style theories on the suitability for elearning. Web Technol., Appl., and Serv. 191–197, 2005 (2005)Google Scholar
  14. 14.
    Li, C., Zhou, H.: Enhancing the efficiency of massive online learning by integrating intelligent analysis into moocs with an application to education of sustainability. Sustainability 10(2), 468 (2018)CrossRefGoogle Scholar
  15. 15.
    Graf, S., Kinshuk, L.T.C.: Identifying learning styles in learning management systems by using indications from students’ behaviour. In: 2008 Eighth IEEE International Conference on Advanced Learning Technologies, pp. 482–486 (2008).  https://doi.org/10.1109/ICALT.2008.84
  16. 16.
    Blagojević, M., Milosević, M.: Collaboration and learning styles in pure online courses: an action research. J. Univ. Comput. Sci. 19(7), 984–1002 (2013)Google Scholar
  17. 17.
    Chatti, M.A., Lukarov, V., Thüs, H., Muslim, A., Yousef, A.M.F., Wahid, U., Greven, C., Chakrabarti, A., Schroeder, U.: Learning analytics: challenges and future research directions. eleed, 10(1) (2014)Google Scholar
  18. 18.
    Keefe, J.W.: Learning style: an overview. Stud. Llearn. Styles: Diagn. Prescrib. Prog. 1, 1–17 (1979)Google Scholar
  19. 19.
    Felder, R.M.: Matters of style. ASEE Prism 6(4), 18–23 (1996)Google Scholar
  20. 20.
    Oxford, R.L.: Language learning styles and strategies: concepts and relationships. IRAL 41(4), 271–278 (2003).  https://doi.org/10.1515/iral.2003.012 CrossRefGoogle Scholar
  21. 21.
    Pashler, H., McDaniel, M., Rohrer, D., Bjork, R.: Learning styles: Concepts and evidence. Psychol. Sci. Public Interest 9(3), 105–119 (2008)CrossRefGoogle Scholar
  22. 22.
    Zaric, N., Roepke, R., Schroeder, U.: concept for linking learning analytics and learning styles in e-learning environmentsGoogle Scholar
  23. 23.
    Graf, S., Kinshuk, K.: Providing adaptive courses in learning management systems with respect to learning styles. In: E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, pp. 2576–2583. Association for the Advancement of Computing in Education (AACE) (2007)Google Scholar
  24. 24.
    Carver, C.A., Howard, R.A., Lane, W.D.: Enhancing student learning through hypermedia courseware and incorporation of student learning styles. IEEE Trans. Educ. 42(1), 33–38 (1999).  https://doi.org/10.1109/13.746332 CrossRefGoogle Scholar
  25. 25.
    Felder, R.M., Spurlin, J.: Applications, reliability and validity of the index of learning styles. Int. J. Eng. Educ. 21(1), 103–112 (2005)Google Scholar
  26. 26.
    Brusilovsky, P.: Methods and techniques of adaptive hypermedia. User Modeling User-Adapted Interaction, 6(2-3), 87–129, (July 1996). ISSN 0924-1868, 1573–1391.  https://doi.org/10.1007/BF00143964 CrossRefGoogle Scholar
  27. 27.
    García, Patricio, Amandi, Analía, Schiaffino, Silvia, Campo, Marcelo: Evaluating Bayesian networks’ precision for detecting students’ learning styles. Comput. Educ. 49(3), 794–808 (2007a)CrossRefGoogle Scholar
  28. 28.
    Villaverde, J.E., Godoy, D., Amandi, A.: Learning styles’ recognition in e-learning environments with feed-forward neural networks. J. Comput. Assist. Learn. 22(3), 197–206 (2006)CrossRefGoogle Scholar
  29. 29.
    Özpolat, E., Akar, G.B.: Automatic detection of learning styles for an e-learning system. Comput. Educ. 53(2), 355–367 (2009)CrossRefGoogle Scholar
  30. 30.
    Feldman, J., Monteserin, A., Amandi, A.: Detecting students’ perception style by using games. Comput. Educ. 71, 14–22 (2014)CrossRefGoogle Scholar
  31. 31.
    Dorça, F.A., Lima, L.V., Fernandes, M.A., Lopes, C.R.: Comparing strategies for modeling students learning styles through reinforcement learning in adaptive and intelligent educational systems: an experimental analysis. Expert Syst. Appl. 40(6), 2092–2101 (2013)CrossRefGoogle Scholar
  32. 32.
    Durand, G., Laplante, F., Kop, R.: A learning design recommendation system based on markov decision processes. In: KDD-2011: 17th ACM SIGKDD conference on knowledge discovery and data mining (2011)Google Scholar
  33. 33.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surveys (CSUR) 41(3), 15 (2009)CrossRefGoogle Scholar
  34. 34.
    Liu, F.T., Ting, K.M., Zhou, Zhi-Hua: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pages 413 – 422. IEEE, (2008)Google Scholar
  35. 35.
    Uddin, M.T., Uddiny, M.A.: A guided random forest based feature selection approach for activity recognition. In: 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), pp. 1–6. IEEE (2015)Google Scholar
  36. 36.
    Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining world wide web browsing patterns. Knowled. Inf. Syst. 1(1), 5–32 (1999)CrossRefGoogle Scholar
  37. 37.
    Singh, B.K., Verma, K., Thoke, AS: Investigations on impact of feature normalization techniques on classifier’s performance in breast tumor classification. Int. J. Comput. Appl., 116 (19) (2015)Google Scholar
  38. 38.
    García, P., Amandi, A., Schiaffino, S., Campo, M.: Evaluating Bayesian networks’ precision for detecting students’ learning styles. Comput. Educ. 49(3), 794–808 (2007b)CrossRefGoogle Scholar
  39. 39.
    Graf, S., Kinshuk, Zhang, Q., Maguire, P., Shtern, V.: Facilitating learning through dynamic student modelling of learning styles. In: Towards Learning and Instruction in Web 3.0, pp. 3–16. Springer, New York (2012)Google Scholar
  40. 40.
    Latham, A., Crockett, K., McLean, D., Edmonds, B.: A conversational intelligent tutoring system to automatically predict learning styles. Comput. Educ. 59(1), 95–109 (2012).  https://doi.org/10.1016/j.compedu.2011.11.001 CrossRefGoogle Scholar
  41. 41.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)zbMATHGoogle Scholar
  42. 42.
    Brown, R.D., Martin, Y.C.: Use of structure- activity data to compare structure-based clustering methods and descriptors for use in compound selection. J. Chem. Inf. Comput. Sci. 36(3), 572–584 (1996)CrossRefGoogle Scholar
  43. 43.
    El Aissaoui, O., El Madani, Y.A., Oughdir, L., El Allioui, Y.: A fuzzy classification approach for learning style prediction based on web mining technique in e-learning environments. Education and Information Technologies, pp. 1–17 (2018)Google Scholar
  44. 44.
    Kolekar, S.V., Pai, R.M., Manohara Pai, M.M.: Prediction of learner’s profile based on learning styles in adaptive e-learning system. Int. J. Emerg. Technol. Learn. (iJET) 12(06), 31–51 (2017)CrossRefGoogle Scholar
  45. 45.
    Hartigan, J.A., Wong, M.A.: Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100–108 (1979)zbMATHGoogle Scholar
  46. 46.
    Khan, F.: An initial seed selection algorithm for k-means clustering of georeferenced data to improve replicability of cluster assignments for mapping application. Appl. Soft Comput. 12(11), 3698–3700 (2012)CrossRefGoogle Scholar
  47. 47.
    Arthur, D., Vassilvitskii, S.: k-means++: The advantages of careful seeding. In: Proceedings of the Eighteenth annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. Society for Industrial and Applied Mathematics (2007)Google Scholar
  48. 48.
    Ketchen, D.J., Shook, C.L.: The application of cluster analysis in strategic management research: an analysis and critique. Strat. Manage. J. 17(6), 441–458 (1996)CrossRefGoogle Scholar
  49. 49.
    Liu, Y., Li, Z., Xiong, H., Gao, X., Wu, J.: Understanding of internal clustering validation measures. In: 2010 IEEE International Conference on Data Mining, pp. 911–916. IEEE (2010)Google Scholar
  50. 50.
    Caliński, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat.-Theory Methods 3(1), 1–27 (1974)MathSciNetCrossRefGoogle Scholar
  51. 51.
    Chen, G., Jaradat, S.A., Banerjee, N., Tanaka, T.S., Ko, M.S.H., Zhang, M.Q.: Evaluation and comparison of clustering algorithms in analyzing es cell gene expression data. Stat. Sin., pp. 241–262 (2002)Google Scholar
  52. 52.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  53. 53.
    Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Syst., Man, Cybernet. 21(3), 660–674 (1991)MathSciNetCrossRefGoogle Scholar
  54. 54.
    Song, Y.-Y., Ying, L.U.: Decision tree methods: applications for classification and prediction. Shanghai Arch. Psychiatry 27(2), 130 (2015)Google Scholar
  55. 55.
    Breiman, L.: Random forests. Mach. Learn.g 45(1), 5–32 (2001)CrossRefGoogle Scholar
  56. 56.
    Rodriguez-Galiano, V.F., Ghimire, B., Rogan, J., Chica-Olmo, M., Rigol-Sanchez, J.P.: An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogram. Remote Sens. 67, 93–104 (2012)CrossRefGoogle Scholar
  57. 57.
    De’Ath, G.: Boosted trees for ecological modeling and prediction. Ecology 88(1), 243–251 (2007)CrossRefGoogle Scholar
  58. 58.
    Liyanage, M., Prabhani Pitigala, K.S.,Gunawardena, L., Hirakawa, M.: Detecting learning styles in learning management systems using data mining. J. Inf. Process. 24(4), 740–749 (2016)CrossRefGoogle Scholar
  59. 59.
    Murphy, P.M.: Uci repository of machine learning databases [machine-readable data repository]. Technical report, Department of Information and Computer Science, University of California (1992)Google Scholar
  60. 60.
    Haixiang, G., Yijing, L., Jennifer Shang, G., Mingyun, H.Y., Bing, G.: Learning from class-imbalanced data: review of methods and applications. Expert Syst. Appl. 73, 220–239 (2017)CrossRefGoogle Scholar
  61. 61.
    Chang, Y.-C., Kao, W.-Y., Chu, C.-P., Chiu, C.-H.: A learning style classification mechanism for e-learning. Comput. Educ. 53(2), 273–285 (2009)CrossRefGoogle Scholar
  62. 62.
    Fausett, L.V., et al.: Fundamentals of Neural Networks: Architectures, Algorithms, and Applications, vol 3. Prentice-Hall Englewood Cliffs (1994)Google Scholar
  63. 63.
    van Rijn, J.N., Abdulrahman, S.M., Brazdil, P., Vanschoren, J.: Fast algorithm selection using learning curves. In: International Symposium on Intelligent Data Analysis, pp. 298–309. Springer (2015)Google Scholar
  64. 64.
    Sokolova, M., Japkowicz, N., Szpakowicz, S.: Beyond accuracy, f-score and roc: a family of discriminant measures for performance evaluation. In: Australasian Joint Conference on Artificial Intelligence, pp. 1015–1021. Springer (2006)Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.IRF-SIC Laboratory Ibn Zohr University Agadir MoroccoAgadirMorocco

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