Novel heterogeneous grouping method based on magic square

Chun Cheng Peng, Cheng Jung Tsai, Ting Yi Chang, Jen Yuan Yeh, Meng Chu Lee

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)


Grouping students appropriately to increase learning achievement is important in learning and teaching. Traditional grouping methods include both homogeneous and heterogeneous grouping; heterogeneous grouping has been claimed to improve students' learning achievement and learning process in both cooperative and collaborative learning. Recently, machine–learning-based grouping approaches have been proposed to produce better heterogeneous groups. One main drawback of these machine-learning-based methods is that they are highly affected by parameter settings; setting the appropriate parameters is difficult for general users. Consequently, the most adopted heterogeneous grouping methods currently are s-shape placement, random assignment, and self-grouping, as the three methods do not require additional parameter settings. Herein, a new heterogeneous grouping algorithm named MASA (magic square-based heterogeneous grouping algorithm) is proposed. As in the s-shape placement method, the only parameter required in MASA is the number of groups. Experimental analysis on 92 datasets indicated that MASA was superior to the s-shape placement, random assignment, and self-grouping methods for generating better heterogeneous groups. Additionally, MASA is an adaptive method that can generate several grouping results simultaneously, and users can select the preferred solution.

頁(從 - 到)340-360
期刊Information sciences
出版狀態Published - 2020 五月

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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