A Run-Time Detector of Hardworking E-Learners with Underperformance
Due to the lack of a face-to-face interaction between teachers and students in virtual courses, the identification of at-risk learners among those who appear to show normal activity is a challenge. Particularly, we refer to those who are very active in the Learning Management System, but their performance is low in comparison with their peers. To fix this issue, we describe a method aimed to discover learners with an inconsistent performance with respect to their activity, by using an ensemble of classifiers. Its effectiveness will be shown by its application on data from virtual courses and its comparison with the results achieved by two well-known outlier detection techniques.
KeywordsAt-risk students Warning system Educational data mining
This work has been partially funded by the Spanish Government under grant TIN2014-56158-C4-2-P (M2C2).
- 1.Hara, N., Kling, R.: Student distress in web-based distance education. Educause Q. 24(3), 68–69 (2001)Google Scholar
- 4.Wolff, A., Zdrahal, Z., et al. : Developing predictive models for early detection of at-risk students on distance learning modules. In: 4th International Conference on Learning Analytics and Knowledge, pp. 24–28. Indianapolis (2014)Google Scholar
- 6.Koprinska, I., Stretton, J., Yacef, K.: Predicting student performance from multiple data sources. In: International Conference on Artificial Intelligence in Education, pp. 678–681 (2015)Google Scholar
- 10.Breunig, M.M., Kriegel, H.P., et al.: LOF: identifying density-based local outliers. In: ACM International Conference on Management of Data, Dallas, pp. 93–104 (2000)Google Scholar
- 11.Saad, M.K., Hewahi, N.M.: A comparative study of outlier mining and class outlier mining. Comput. Sci. Lett. 1(1) (2009)Google Scholar