A Blending Ensemble Approach to Predicting Student Dropout in Massive Open Online Courses (MOOCs)
The problem faced in the implementation of Massive Open Online Course (MOOC) is the high dropout rate (DO) reaching 90% which exceeds the formal school dropout rate. Preventive action needs to be taken to minimize the impact on MOOCs, instructors, and students. One solution is to do machine learning...
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| Format: | Article |
| Language: | Indonesian |
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Universitas Muhammadiyah Purwokerto
2025-03-01
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| Series: | Jurnal Informatika |
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| Online Access: | http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/24061 |
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| author | Muhammad Ricky Perdana Putra Ema Utami |
| author_facet | Muhammad Ricky Perdana Putra Ema Utami |
| author_sort | Muhammad Ricky Perdana Putra |
| collection | DOAJ |
| description | The problem faced in the implementation of Massive Open Online Course (MOOC) is the high dropout rate (DO) reaching 90% which exceeds the formal school dropout rate. Preventive action needs to be taken to minimize the impact on MOOCs, instructors, and students. One solution is to do machine learning (ML) based prediction. The use of ML does not escape the problem of prediction performance that is still less accurate so it needs to be improved by blending ensemble learning (BEL). This research builds a BEL model consisting of two layers including base model with KNN, Decision Tree, and Naïve Bayes algorithms, then meta model with XGBoost. The dataset from KDD Cup 2015 contains clickstream from XuetangX website. The pre-processing stage includes selecting the course with the most participants, normalization, SMOTE, feature selection, and breaking it into three: ensemble, blender, and test data. The BEL model evaluation results obtained an accuracy value of 90.16%, precision of 85.64%, recall of 97.31%, F1-Score of 91.10%, and AUC of 92.83%. |
| format | Article |
| id | doaj-art-1c088a08e95642dc9530451953630b4b |
| institution | Kabale University |
| issn | 2086-9398 2579-8901 |
| language | Indonesian |
| publishDate | 2025-03-01 |
| publisher | Universitas Muhammadiyah Purwokerto |
| record_format | Article |
| series | Jurnal Informatika |
| spelling | doaj-art-1c088a08e95642dc9530451953630b4b2025-08-20T03:59:25ZindUniversitas Muhammadiyah PurwokertoJurnal Informatika2086-93982579-89012025-03-01111810.30595/juita.v13i1.2406119431A Blending Ensemble Approach to Predicting Student Dropout in Massive Open Online Courses (MOOCs)Muhammad Ricky Perdana Putra0Ema Utami1Universitas Amikom YogyakartaUniversitas Amikom YogyakartaThe problem faced in the implementation of Massive Open Online Course (MOOC) is the high dropout rate (DO) reaching 90% which exceeds the formal school dropout rate. Preventive action needs to be taken to minimize the impact on MOOCs, instructors, and students. One solution is to do machine learning (ML) based prediction. The use of ML does not escape the problem of prediction performance that is still less accurate so it needs to be improved by blending ensemble learning (BEL). This research builds a BEL model consisting of two layers including base model with KNN, Decision Tree, and Naïve Bayes algorithms, then meta model with XGBoost. The dataset from KDD Cup 2015 contains clickstream from XuetangX website. The pre-processing stage includes selecting the course with the most participants, normalization, SMOTE, feature selection, and breaking it into three: ensemble, blender, and test data. The BEL model evaluation results obtained an accuracy value of 90.16%, precision of 85.64%, recall of 97.31%, F1-Score of 91.10%, and AUC of 92.83%.http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/24061blending ensemble learningmoocpredictiondropoutsmote. |
| spellingShingle | Muhammad Ricky Perdana Putra Ema Utami A Blending Ensemble Approach to Predicting Student Dropout in Massive Open Online Courses (MOOCs) Jurnal Informatika blending ensemble learning mooc prediction dropout smote. |
| title | A Blending Ensemble Approach to Predicting Student Dropout in Massive Open Online Courses (MOOCs) |
| title_full | A Blending Ensemble Approach to Predicting Student Dropout in Massive Open Online Courses (MOOCs) |
| title_fullStr | A Blending Ensemble Approach to Predicting Student Dropout in Massive Open Online Courses (MOOCs) |
| title_full_unstemmed | A Blending Ensemble Approach to Predicting Student Dropout in Massive Open Online Courses (MOOCs) |
| title_short | A Blending Ensemble Approach to Predicting Student Dropout in Massive Open Online Courses (MOOCs) |
| title_sort | blending ensemble approach to predicting student dropout in massive open online courses moocs |
| topic | blending ensemble learning mooc prediction dropout smote. |
| url | http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/24061 |
| work_keys_str_mv | AT muhammadrickyperdanaputra ablendingensembleapproachtopredictingstudentdropoutinmassiveopenonlinecoursesmoocs AT emautami ablendingensembleapproachtopredictingstudentdropoutinmassiveopenonlinecoursesmoocs AT muhammadrickyperdanaputra blendingensembleapproachtopredictingstudentdropoutinmassiveopenonlinecoursesmoocs AT emautami blendingensembleapproachtopredictingstudentdropoutinmassiveopenonlinecoursesmoocs |