Abstract
Suggesting tasks and learning resources of appropriate difficulty to learners is challenging. Neither should they be too difficult and nor too easy. Well-chosen tasks would enable a quick assessment of the learner, well-chosen learning resources would speed up the learning curve most. We connect active learning to classical pedagogical theory and propose the uncertainty sampling framework as a means to the challenge of selecting optimal tasks and learning resources to learners. To assess the efficiency of this strategy, we compared different exercise selection strategies and evaluated their effect on different datasets. We consistently find that uncertainty sampling significantly outperforms several alternative exercise selection approaches and thus leads to a faster convergence to the true assessment. These findings demonstrate that active (machine) learning is consistent with classic learning theory. It is a valuable instrument for choosing appropriate exercises as well as learning resources both from a teacher’s and from a learner’s perspective.
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References
Golbandi, N., Koren, Y., Lempel, R.: Adaptive bootstr. of recommender systems using decision trees. In: Proc. of the 4th ACM Intl. Conf. on Web Search and Data Mining (2011)
Lewis, D., Catlett, J.: Heterogeneous uncertainty sampling for supervised learning. In: Proceedings of the International Conference on Machine Learning, ICML (1994)
Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., Koper, R.: Recommender systems in technology enhanced learning. In: Recommender Systems Handbook: A Complete Guide for Research Scientists & Practitioners (2010)
Murray, T., Arroyo, I.: Toward measuring and maintaining the zone of proximal development in adaptive instructional systems. In: Cerri, S.A., Gouardéres, G., Paraguaçu, F. (eds.) ITS 2002. LNCS, vol. 2363, pp. 749–758. Springer, Heidelberg (2002)
Rish, I., Tesauro, G.: Active Collaborative Prediction with Maximum Margin Matrix Factorization. In: Inform. Theory and App. Workshop (2007)
Thai-Nghe, N., Drumond, L., Horváth, T., Krohn-Grimberghe, A., Nanopoulos, A., Schmidt-Thieme, L.: Factorization techniques for predicting student performance. In: Educational Recommender Systems and Technologies: Practices and Challenges (2011)
Vygotsky, L.S.: Mind in Society: The Development of Higher Psychological Processes. Harvard University Press, Cambridge (1978)
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© 2011 Springer-Verlag Berlin Heidelberg
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Krohn-Grimberghe, A., Busche, A., Nanopoulos, A., Schmidt-Thieme, L. (2011). Active Learning for Technology Enhanced Learning. In: Kloos, C.D., Gillet, D., Crespo GarcÃa, R.M., Wild, F., Wolpers, M. (eds) Towards Ubiquitous Learning. EC-TEL 2011. Lecture Notes in Computer Science, vol 6964. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23985-4_48
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DOI: https://doi.org/10.1007/978-3-642-23985-4_48
Publisher Name: Springer, Berlin, Heidelberg
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