An Ensemble-Based Semi-Supervised Approach for Predicting Students’ Performance
| Autor: | Konstantina Drakopoulou, Panagiotis Pintelas, Ioannis E. Livieris, Tassos A. Mikropoulos, Vassilios Tampakas |
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| Rok vydání: | 2018 |
| Předmět: |
Co-training
Academic year Computer science business.industry 02 engineering and technology Semi-supervised learning Machine learning computer.software_genre Educational data mining Popularity Variety (cybernetics) ComputingMethodologies_PATTERNRECOGNITION Work (electrical) 020204 information systems 0202 electrical engineering electronic engineering information engineering Classification methods 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Research on e-Learning and ICT in Education ISBN: 9783319950587 |
| DOI: | 10.1007/978-3-319-95059-4_2 |
| Popis: | Educational data mining has gained popularity due to its ability to provide useful knowledge hidden in data of students’ records for better educational decision-making support. During the last years, a variety of methods have been applied to develop accurate models to monitor students’ behavior and performance, while most of these studies examine the efficiency of supervised classification methods. In this work, we propose a new ensemble-based semi-supervised method for the prognosis of students’ performance in the final examinations at the end of academic year. Our experimental results reveal that our proposed method is proved to be effective and practical for early student progress prediction as compared to some existing semi-supervised learning methods. |
| Databáze: | OpenAIRE |
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