| Presentation | 2018-07-14 Pair formation optimization algorithm based on learning performance, attendance rate, and pair history of students Satoshi V. Suzuki, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Skills of collaboration with various people is increasingly required in the future society. The author focus on peer learning as one of the forms of collaborative learning to enhance such kind of skills. However, the methods to form pairs in the classroom has much room for improvement. Randomly forming pairs causes the variation of pair activity in wrong way and spends unnecessary time for lesson. Moreover, deciding the pair formation in advance for the class with many students who have a poor attendance record causes students with peer absent from the lesson. The author developed the pair formation optimization method using combinatorial optimization with genetic algorithm and particle swarm optimization. This method explores better pair formation as a solution based on learning performance and attendance rate of the students. Additionally, this method considers history of past pair formation to enable the students to learn with the different peer for each lesson. In this paper, the author compared this method with different combinatorial optimization algorithm and discuss research perspective for development of collaborative learning environment in the future. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | peer learning / pair formation / combinatorial optimization / genetic algorithm / particle swarm optimization |
| Paper # | ET2018-20 |
| Date of Issue | 2018-07-07 (ET) |
| Conference Information | |
| Committee | ET |
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| Conference Date | 2018/7/14(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | National Institute of Technology, Hakodate College |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Learning Analytics and Learning Data, etc. |
| Chair | Yozo Miyadera(Tokyo Gakugei Univ.) |
| Vice Chair | Ryo Takaoka(Yamaguchi Univ.) |
| Secretary | Ryo Takaoka(Open Univ. of Japan) |
| Assistant | Megumi Kurayama(National Inst. of Tech., Hakodate College) / Masaru Okamoto(Hiroshima City Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Educational Technology |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Pair formation optimization algorithm based on learning performance, attendance rate, and pair history of students |
| Sub Title (in English) | An Application of hybrid of genetic algorithm and particle swarm optimization |
| Keyword(1) | peer learning |
| Keyword(2) | pair formation |
| Keyword(3) | combinatorial optimization |
| Keyword(4) | genetic algorithm |
| Keyword(5) | particle swarm optimization |
| 1st Author's Name | Satoshi V. Suzuki |
| 1st Author's Affiliation | Osaka University of Economics and Law(Osaka Univ of Econ. and Law) |
| Date | 2018-07-14 |
| Paper # | ET2018-20 |
| Volume (vol) | vol.118 |
| Number (no) | ET-131 |
| Page | pp.pp.7-12(ET), |
| #Pages | 6 |
| Date of Issue | 2018-07-07 (ET) |