期刊名称：Revista Brasileira de Informática na Educação
印刷版ISSN：1414-5685
出版年度：2020
卷号：28
页码：796-818
DOI：10.5753/rbie.2020.28.0.796
出版社：Revista Brasileira de Informática na Educação
摘要：Group formation is an important and challenging element for designing successful CSCL scenarios. Despite efforts from the scientific community in developing more effective algorithms to support group formation processes, we still face problems related to learners’ resistance and demotivation towards group work. In this sense, diverse studies highlight the importance of considering learners’ personality traits to form groups, since this factor can influence students’ performance and induce diverse actions and behaviors in group work. Therefore, this paper presents G-FusionPT (Group Formation USIng Ontology and Personality Trait), a group formation algorithm that support new learning roles, denominated Affective Collaborative Learning roles, based on relation between collaborative learning theories and students’ personality traits. The algorithm is based on a collaborative ontology to understand the learning theories (e.g., context, learning activities, group structure), and learners profile to understand learners’ needs (e.g., target/current knowledge/skill). To evaluate the algorithm, we used a 300 student simulated sample with varying group size (three, five, and seven members), and compared G-FusionPT results to other group formation algorithms: G-Fusion (based specifically on collaborative learning theories) and Random (no strategy or criterion). The results demonstrated the effectiveness of G-FusionPT against G-Fusion and Random algorithms, as it generated the highest average percentage of learners in well-formed groups and lowest average percentage of learners in unfit groups.
关键词：CSCL;Group Formation;Algorithm;Personality Trait;Learning Theory
