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B

Baker et al., 2015

Baker, R. S., Lindrum, D., Lindrum, M. J., & Perkowski, D. (2015). Analyzing Early At-Risk Factors in Higher Education E-Learning Courses. International Conference on Educational Data Mining (EDM), Madrid. https://eric.ed.gov/?id=ED560553

Bañeres et al., 2020

Bañeres, D., Rodríguez, M. E., Guerrero-Roldán, A. E., & Karadeniz, A. (2020). An Early Warning System to Detect At-Risk Students in Online Higher Education. Applied Sciences, 10(13), Article 13. https://doi.org/10.3390/app10134427

Barbour & Plough, 2009

Barbour, M., & Plough, C. (2009). Social Networking in Cyberschooling: Helping to Make Online Learning Less Isolating. TechTrends, 53(4), 56–60. https://doi.org/10.1007/s11528-009-0307-5

Berens et al., 2019

Berens, J., Schneider, K., Gortz, S., Oster, S., & Burghoff, J. (2019). Early Detection of Students at Risk—Predicting Student Dropouts Using Administrative Student Data from German Universities and Machine Learning Methods. Journal of Educational Data Mining, 11(3), 1–41. https://doi.org/10.5281/zenodo.3594771

Bildungsbericht 2024

Autor:innengruppe Bildungsberichterstattung (Hrsg.). (2024). Bildung in Deutschland 2024: Ein indikatorengestützter Bericht mit einer Analyse zu beruflicher Bildung. wbv Media. https://doi.org/10.3278/6001820iw