Clustering Research on Collaborative Knowledge Building Characteristics of Team Games Based on Interaction Analysis Model

作者：Jiaying Li, Hua Jin, Yifeng Fan, Qianhui Ji, Tianyi Ou, Shuai Ren, Jin Zhang, Xiaoyang Li · 发表于：Frontiers in artificial intelligence and applications · 年份：2024 · DOI：10.3233/faia240094 · 研究领域：Innovative Educational Techniques

To enable educators to make informed choices based on the collaborative knowledge building characteristics of team games, this paper will systematically extract the collaborative knowledge building characteristics of multiple team games. First, the behavior of collaborative knowledge building in team games is quantified based on the five behavioral phases of the Interaction Analysis Model (IAM) to determine the five-dimensional data set of collaborative knowledge building eigenvalues of 112 team games. A comprehensive examination is conducted on the overall profile and data similarity relationships, and 8 team games with highly ambiguous similarity relationships are removed to ensure the accuracy of clustering. It is found that the collaborative knowledge building characteristics of team games exhibit fuzzy similarity relationships. Subsequently, based on this, the Fuzzy C-Means (FCM) algorithm is employed to identify 8 collaborative knowledge building characteristics within 104 team games. Based on this, selected and retained a total of 36 team games with a high level of credibility within their respective categories. Finally, the collaborative knowledge building characteristic of each category is represented in the form of five-dimensional radar charts to analyze their distribution characteristics and establish the corresponding relationship between the characteristics and team games.