Abstract
Suppose you are a teacher, and have to convey a set of object-property pairs (‘lions eat meat’). A good teacher will convey a lot of information, with little effort on the student side. What is the best and most intuitive way to convey this information to the student, without the student being overwhelmed? A related, harder problem is: how can we assign a numerical score to each lesson plan (i.e., way of conveying information)? Here, we give a formal definition of this problem of forming learning units and we provide a metric for comparing different approaches based on information theory. We also design an algorithm, groupNteach, for this problem. Our proposed groupNteach is scalable (near-linear in the dataset size); it is effective, achieving excellent results on real data, both with respect to our proposed metric, but also with respect to encoding length; and it is intuitive, conforming to well-known educational principles. Experiments on real and synthetic datasets demonstrate the effectiveness of groupNteach.
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Acknowledgments
This material is based upon work supported by the National Science Foundation under Grant No. IIS-1247489
Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-09-2-0053.
This work is also partially supported by an IBM Faculty Award and a Google Focused Research Award. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, or other funding parties. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
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Hooi, B., Song, H.A., Papalexakis, E., Agrawal, R., Faloutsos, C. (2016). Matrices, Compression, Learning Curves: Formulation, and the GroupNteach Algorithms. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J., Wang, R. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9652. Springer, Cham. https://doi.org/10.1007/978-3-319-31750-2_30
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DOI: https://doi.org/10.1007/978-3-319-31750-2_30
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