Abstract
Vocational further education typically builds upon prior knowledge. For learners who lack this prior knowledge, preparatory e-learnings may help. Therefore, we wish to identify students who would profit from such an e-learning. We consider the example of a math e-learning for the Bachelor Professional of Chemical Production and Management (CCI). To estimate whether the e-learning would help, we employ a predictive model. Developing such a model in a real-world scenario confronted us with a range of challenges, such as small sample sizes, overfitting, or implausible model parameters. We describe how we addressed these challenges such that other practitioners can learn from our case study of employing data mining in vocational training.
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Funding by the German Ministry for Education and Research (BMBF) under grant number 21INVI1403 (project KIPerWeb) is gratefully acknowledged.
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Paaßen, B., Dywel, M., Fleckenstein, M., Pinkwart, N. (2022). Interpretable Knowledge Gain Prediction for Vocational Preparatory E-Learnings. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_23
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