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Pregled bibliografske jedinice broj: 1035300

Using LMS Activity Logs to Predict Student Failure with Random Forest Algorithm


Ljubobratović, Dejan; Matetić, Maja
Using LMS Activity Logs to Predict Student Failure with Random Forest Algorithm // 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. (ur.).
Zagreb: Faculty of Humanities and Social Sciences, Department of Information Sciences, 2019. str. 113-119 doi:10.17234/INFUTURE.2019.14 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 1035300 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Using LMS Activity Logs to Predict Student Failure with Random Forest Algorithm

Autori
Ljubobratović, Dejan ; Matetić, Maja

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
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, 2019, 113-119

Skup
INFuture 2019.

Mjesto i datum
Zagreb, Hrvatska, 21-22.11.2019

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
LMS system, Random forest algorithm, Educational data mining, Predicting student success, Interpretability, Interpretable machine learning

Sažetak
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".

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Ustanove:
Sveučilište u Rijeci - Odjel za informatiku

Profili:

Avatar Url Maja Matetić (autor)

Avatar Url Dejan Ljubobratović (autor)

Citiraj ovu publikaciju

Ljubobratović, Dejan; Matetić, Maja
Using LMS Activity Logs to Predict Student Failure with Random Forest Algorithm // 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. (ur.).
Zagreb: Faculty of Humanities and Social Sciences, Department of Information Sciences, 2019. str. 113-119 doi:10.17234/INFUTURE.2019.14 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Ljubobratović, D. & Matetić, M. (2019) Using LMS Activity Logs to Predict Student Failure with Random Forest Algorithm. U: Bago, P., Hebrang Grgić, I., Ivanjko, T., Juričić, V., Miklošević, Ž. & Stublić, H. (ur.)Proceedings of the 7th International ConferenceThe Future of Information Sciences (INFuture 2019), Zagreb, 21-22- November 2019 doi:10.17234/INFUTURE.2019.14.
@article{article, year = {2019}, pages = {113-119}, DOI = {10.17234/INFUTURE.2019.14}, keywords = {LMS system, Random forest algorithm, Educational data mining, Predicting student success, Interpretability, Interpretable machine learning}, doi = {10.17234/INFUTURE.2019.14}, title = {Using LMS Activity Logs to Predict Student Failure with Random Forest Algorithm}, keyword = {LMS system, Random forest algorithm, Educational data mining, Predicting student success, Interpretability, Interpretable machine learning}, publisher = {Faculty of Humanities and Social Sciences, Department of Information Sciences}, publisherplace = {Zagreb, Hrvatska} }
@article{article, year = {2019}, pages = {113-119}, DOI = {10.17234/INFUTURE.2019.14}, keywords = {LMS system, Random forest algorithm, Educational data mining, Predicting student success, Interpretability, Interpretable machine learning}, doi = {10.17234/INFUTURE.2019.14}, title = {Using LMS Activity Logs to Predict Student Failure with Random Forest Algorithm}, keyword = {LMS system, Random forest algorithm, Educational data mining, Predicting student success, Interpretability, Interpretable machine learning}, publisher = {Faculty of Humanities and Social Sciences, Department of Information Sciences}, publisherplace = {Zagreb, Hrvatska} }

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