Literaturnachweis - Detailanzeige
| Autor/inn/en | Agrawal, Rakesh; Nandanwar, Sharad; Murty, M. N. |
|---|---|
| Titel | Grouping Students for Maximizing Learning from Peers [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (10th, Wuhan, China, Jun 25-28, 2017). |
| Quelle | (2017), (6 Seiten)
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| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Grouping (Instructional Purposes); Peer Relationship; Interaction; Ability Grouping; Academic Achievement; Scores; Outcomes of Education; Computation; Equations (Mathematics); Educational Strategies; Statistical Distributions |
| Abstract | We study the problem of partitioning a class of N students into k groups of n students each (N = k × n), such that their learning from peer interactions is maximized. In our formalization of the problem, any student is able to increase his score in the subject the class is studying up to the score of the student who is at p-percentile among his higher ability peers. In contrast, the past work presumed that only students with score below the group mean may increase their score. We give a partitioning algorithm that maximizes total gain summed over all the students for any value of p such that 100/(100-p) is integer valued. The time complexity of the proposed algorithm is only O(N log N). We also present experimental results using real-life data that show the superiority of the proposed algorithm over current strategies. [For the full proceedings, see ED596512.] (As Provided). |
| Anmerkungen | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2020/1/01 |
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