Analysis of Machine Learning Classification Approaches for Predicting Students’ Programming Aptitude
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- Schneider, Kerstin & Berens, Johannes & Oster, Simon & Burghoff, Julian, 2018.
"Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods,"
VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy
181544, Verein für Socialpolitik / German Economic Association.
- Johannes Berens & Kerstin Schneider & Simon Görtz & Simon Oster & Julian Burghoff, 2018. "Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," CESifo Working Paper Series 7259, CESifo.
- Johannes Berens & Simon Oster & Kerstin Schneider & Julian Burghoff, 2018. "Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," Schumpeter Discussion Papers sdp18006, Universitätsbibliothek Wuppertal, University Library.
- Wala Bagunaid & Naveen Chilamkurti & Prakash Veeraraghavan, 2022. "AISAR: Artificial Intelligence-Based Student Assessment and Recommendation System for E-Learning in Big Data," Sustainability, MDPI, vol. 14(17), pages 1-22, August.
- Silvia Bacci & Bruno Bertaccini, 2022. "A Mixture Hidden Markov Model to Mine Students’ University Curricula," Data, MDPI, vol. 7(2), pages 1-19, February.
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