Abstract
Investigating the factors affecting students’ academic failure in online and/or blended courses by analyzing students’ learning behavior data gathered from Learning Management Systems (LMS) is a challenging area in intelligent learning analytics and education data mining area. It has been argued that the actual course design and the instructor’s intentions is critical to determine which variables meaningfully represent student effort that should be included/excluded from the list of predicting factors. In this paper we describe such an approach for identifying students at risk of failure in online courses. For the proof of our concept we used the data of two cohorts of an online course implemented in Moodle LMS. Using the data of the first cohort we developed a prediction model by experimenting with certain base classifiers available in Weka. To improve the observed performance of the experimented base classifiers, we enhanced further our model with the Majority Voting ensemble classifier. The final model was used at the next cohort of students in order to identify those at risk of failure before the final exam. The prediction accuracy of the model was high which show that the findings of such a process can be generalized.
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Anagnostopoulos, T., Kytagias, C., Xanthopoulos, T., Georgakopoulos, I., Salmon, I., Psaromiligkos, Y. (2020). Intelligent Predictive Analytics for Identifying Students at Risk of Failure in Moodle Courses. In: Kumar, V., Troussas, C. (eds) Intelligent Tutoring Systems. ITS 2020. Lecture Notes in Computer Science(), vol 12149. Springer, Cham. https://doi.org/10.1007/978-3-030-49663-0_19
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