Educational data mining applications and tasks: A survey of the last 10 years

Abstract

Educational Data Mining (EDM) is the field of using data mining techniques in educational environments. There exist various methods and applications in EDM which can follow both applied research objectives such as improving and enhancing learning quality, as well as pure research objectives, which tend to improve our understanding of the learning process. In this study we have studied various tasks and applications existing in the field of EDM and categorized them based on their purposes. We have compared our study with other existing surveys about EDM and reported a taxonomy of task.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

References

  1. Agrawal, R., Gollapudi, S., Kannan, A., & Kenthapadi, K. (2014). Study navigator: An algorithmically generated aid for learning from electronic textbooks. JEDM-Journal of Educational Data Mining, 6(1), 53–75.

    Google Scholar 

  2. Alaofi, M., & Rumantir, G. (2015). Personalisation of generic library search results using student enrolment information. JEDM-Journal of Educational Data Mining, 7(3), 68–88.

    Google Scholar 

  3. Azarnoush, B., Bekki, J.M., Runger, G.C., Bernstein, B.L., & Atkinson, R.K. (2013). Toward a framework for learner segmentation. JEDM-Journal of Educational Data Mining, 5(2), 102–126.

    Google Scholar 

  4. Baker, RS, & Yacef, K (2009). The state of educational data mining in 2009: A review and future visions. JEDM-Journal of Educational Data Mining, 1(1), 3–17.

    Google Scholar 

  5. Bates, A.W. (2015). Teaching in a digital age: Guidelines for designing teaching and learning. Tony Bates Associates.

  6. Bravo, J., & Ortigosa, A. (2009). Detecting symptoms of low performance using production rules. In International working group on educational data mining.

  7. Chrysafiadi, K., & Virvou, M. (2013). Student modeling approaches: A literature review for the last decade. Expert Systems with Applications, 40(11), 4715–4729.

    Article  Google Scholar 

  8. Cocea, M., & Weibelzahl, S. (2006). Can log files analysis estimate learners’ level of motivation?

  9. Dekker, G.W., Pechenizkiy, M., & Vleeshouwers, J.M. (2009). Predicting students drop out: A case study. In International working group on educational data mining.

  10. Delavari, N., Phon-Amnuaisuk, S., & Beikzadeh, M.R. (2008). Data mining application in higher learning institutions. Informatics in Education-International Journal, 7, 31–54.

    Google Scholar 

  11. Galyardt, A., & Goldin, I. (2015). Move your lamp post: Recent data reflects learner knowledge better than older data. JEDM-Journal of Educational Data Mining, 7(2), 83–108.

    Google Scholar 

  12. García, E., Romero, C., Ventura, S., & Castro, C.D. (2009). An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering. User Modeling and User-Adapted Interaction, 19(1–2), 99–132.

    Article  Google Scholar 

  13. Hao, J., Shu, Z., & von Davier, A. (2015). Analyzing process data from game/scenario-based tasks: An edit distance approach. JEDM-Journal of Educational Data Mining, 7(1), 33–50.

    Google Scholar 

  14. Harley, J.M., Trevors, G.J., & Azevedo, R. (2013). Clustering and profiling students according to their interactions with an intelligent tutoring system fostering self-regulated learning. JEDM-Journal of Educational Data Mining, 5(1), 104–146.

    Google Scholar 

  15. Hegazi, M.O., & Abugroon, M.A. (n.d.) The state of the art on educational data mining in higher education.

  16. Hsia, T.C., Shie, A.J., & Chen, L.C. (2008). Course planning of extension education to meet market demand by using data mining techniques—An example of Chinkuo technology university in Taiwan. Expert Systems with Applications, 34(1), 596–602.

    Article  Google Scholar 

  17. Huang, C.T., Lin, W.T., Wang, S.T., & Wang, W.S. (2009). Planning of educational training courses by data mining: Using China Motor Corporation as an example. Expert Systems with Applications, 36(3), 7199–7209.

    Article  Google Scholar 

  18. Kinnebrew, J.S., Loretz, K.M., & Biswas, G. (2013). A contextualized, differential sequence mining method to derive students’ learning behavior patterns. JEDM-Journal of Educational Data Mining, 5(1), 190–219.

    Google Scholar 

  19. Knowles, J.E. (2015). Of needles and haystacks: Building an accurate statewide dropout early warning system in Wisconsin. JEDM-Journal of Educational Data Mining, 7(3), 18–67.

    Google Scholar 

  20. Lee, C.H., Lee, G.G., & Leu, Y. (2009). Application of automatically constructed concept map of learning to conceptual diagnosis of e-learning. Expert Systems with Applications, 36(2), 1675–1684.

    Article  Google Scholar 

  21. Lykourentzou, I, Giannoukos, I, Nikolopoulos, V, Mpardis, G, & Loumos, V (2009). Dropout prediction in e-learning courses through the combination of machine learning techniques. Computers & Education, 53(3), 950–965.

    Article  Google Scholar 

  22. Lynch, C., Ashley, K., Aleven, V., & Pinkwart, N. (2006). Defining ill-defined domains; a literature survey. In Proceedings of the workshop on intelligent tutoring systems for ill-defined domains at the 8th international conference on intelligent tutoring systems (pp. 1–10).

  23. Macfadyen, L.P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588–599.

    Article  Google Scholar 

  24. Mallavarapu, A., Lyons, L., Shelley, T., & Slattery, B. (2015). Developing computational methods to measure and track learners’ spatial reasoning in an open-ended simulation. JEDM-Journal of Educational Data Mining, 7(2), 49–82.

    Google Scholar 

  25. Miller, L.D., Soh, L.-K., Samal, A., Kupzyk, K., & Nugent, G. (2015). A comparison of educational statistics and data mining approaches to identify characteristics that impact online learning. JEDM-Journal of Educational Data Mining, 7(3), 117–150.

    Google Scholar 

  26. Naveh, G., Tubin, D., & Pliskin, N. (2012). Student satisfaction with learning management systems: A lens of critical success factors. Technology, Pedagogy and Education, 21(3), 337–350.

    Article  Google Scholar 

  27. Novak, J.D., & Cañas, A.J. (2008). The theory underlying concept maps and how to construct and use them.

  28. O’Mahony, M.P., & Smyth, B. (2007). A recommender system for on-line course enrolment: An initial study. In Proceedings of the 2007 ACM conference on recommender systems (pp. 133–136). ACM.

  29. Peña-Ayala, A. (2013). Educational data mining: Applications and trends (Vol. 524). Berlin: Springer.

    Google Scholar 

  30. Rallo, R., Gisbert, M., & Salinas, J. (1999). Using data mining and social networks to analyze the structure and content of educative on-line communities. Analysis, 468(472), 473.

    Google Scholar 

  31. Reffay, C., & Chanier, T. (2003). How social network analysis can help to measure cohesion in collaborative distance-learning. In (pp. 343–352). Springer.

  32. Reyes, P., & Tchounikine, P. (2005). Mining learning groups’ activities in Forum-type tools. In Proceedings of th 2005 conference on computer support for collaborative learning: Learning 2005: The next 10 years! (pp. 509–513). International Society of the Learning Sciences.

  33. Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135–146.

  34. Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 40(6), 601–618.

    Article  Google Scholar 

  35. Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12–27.

    Google Scholar 

  36. Romero, C., Ventura, S., Pechenizkiy, M., & Baker, R.S. (2010). Handbook of educational data mining. Boca Raton: CRC Press.

    Google Scholar 

  37. Romero, C., Zafra, A., Luna, J.M., & Ventura, S. (2013). Association rule mining using genetic programming to provide feedback to instructors from multiple-choice quiz data. Expert Systems, 30(2), 162–172.

    Article  Google Scholar 

  38. Sabourin, J.L., Rowe, J.P., Mott, B.W., & Lester, J.C. (2013). Considering alternate futures to classify off-task behavior as emotion self-regulation: A supervised learning approach. JEDM-Journal of Educational Data Mining, 5(1), 9–38.

    Google Scholar 

  39. Self, J.A. (n.d.) Bypassing the intractable problem of student modelling.

  40. Tang, C., Lau, R.W., Li, Q., Yin, H., Li, T., & Kilis., D (2000). Personalized courseware construction based on web data mining. In Proceedings of the first international conference on web information systems engineering, 2000 (Vol. 2, pp. 204–211). IEEE.

  41. Vialardi, C., Agapito, J.B., Shafti, L.S., & Ortigosa, A. (2009). Recommendation in higher education using data mining techniques. In T. Barnes, M. Desmarais, C. Romero, S. Ventura.

  42. Wang, V.C. (2014). Handbook of research on education and technology in a changing society. IGI Global.

  43. Waters, A., Studer, C., & Baraniuk, R. (2014). Collaboration-type identification in educational datasets. JEDM-Journal of Educational Data Mining, 6(1), 28–52.

    Google Scholar 

  44. Zimmermann, J., Brodersen, K.H., Heinimann, H.R., & Buhmann, J.M. (2015). A model-based approach to predicting graduate-level performance using indicators of undergraduate-level performance. JEDM-Journal of Educational Data Mining, 7(3), 151–176.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Samira ElAtia.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bakhshinategh, B., Zaiane, O.R., ElAtia, S. et al. Educational data mining applications and tasks: A survey of the last 10 years. Educ Inf Technol 23, 537–553 (2018). https://doi.org/10.1007/s10639-017-9616-z

Download citation

Keywords

  • Educational data mining
  • Surveys
  • Taxonomy of applications