Using LMS Activity Logs to Predict Student Failure with Random Forest Algorithm (CROSBI ID 684524)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Ljubobratović, Dejan ; Matetić, Maja
engleski
Using LMS Activity Logs to Predict Student Failure with Random Forest Algorithm
The paper presents a Random forest model in the task of predicting student success (grade) on the base of input predictors (lectures, quizzes, labs and videos) extracted from Moodle activity logs. Since 2010. University of Rijeka is using Moodle based Learning Management Systems (LMS) to complement traditional teaching. LMS is used for documents sharing, quizzes, assessments, video lecturing, tracking student progress and much more. When student access an LMS using his personal account, a digital profile is created that is saved in LMS log files. These logs were used to create a dataset with couple of hundreds observations. However building a prediction model using Random forest algorithm is relatively easy comparing to explaining the results. Interpreting Random forest and other machine learning black box models is a challenge regarding to complexity of their decision making mechanisms. There are a number of new techniques allowing us to interpret such models, and couple of them is used in this paper for that purpose. Another problem a researcher is facing using black box algorithms is GDPR. General Data Protection Regulation has a significant impact on many aspects of EU citizen’s data collection and processing. This paper will highlight most challenging GDPR restrictions on data mining including GDPR’s "right to explanation".
LMS system, Random forest algorithm, Educational data mining, Predicting student success, Interpretability, Interpretable machine learning
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Podaci o prilogu
113-119.
2019.
objavljeno
10.17234/INFUTURE.2019.14
Podaci o matičnoj publikaciji
Proceedings of the 7th International ConferenceThe Future of Information Sciences (INFuture 2019), Zagreb, 21-22- November 2019
Bago, P. ; Hebrang Grgić, I. ; Ivanjko, T. ; Juričić, V. ; Miklošević, Ž. ; Stublić, H.
Zagreb: Faculty of Humanities and Social Sciences, Department of Information Sciences
2706-3518
Podaci o skupu
7th International Conference The Future of Information Sciences (INFuture 2019)
predavanje
21.11.2019-22.11.2019
Zagreb, Hrvatska
Povezanost rada
Informacijske i komunikacijske znanosti, Računarstvo