Emotional issues in collaborative learning: Bibliometrics with topic modeling

BACKGROUND: Collaborative learning has been widely researched in education. The roles of motivation and emotions have been found to be central to collaborative learning. Research on emotional issues in collaborative learning has increasingly become an active field. However, no attempt has been made to evaluate the literature concerning emotional issues in collaborative learning. This study is thus conducted to fill this gap by summarizing its status and development trend.
METHODS: This paper presents a quantitative overview of the research concerning emotional issues in collaborative learning. The study is based on articles published in the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) databases of the Web of Science between 2010 and 2019. In total, 824 articles are identified and quantitatively analyzed by using topic modeling and bibliometric analysis. Specifically, this study identifies the major research topics and their developmental trends, leading countries/regions and institutions around the world that have conducted relevant research on emotional issues in collaborative learning. Additionally, this study also identifies the journals that include the most articles, and the scientific collaborations between top countries/regions and institutions.
RESULTS: There is a generally increasing trend of annual articles and citations in the research concerning emotional issues in collaborative learning. Results from topic modeling provide important insights into current research hotspots as well as future research directions. For example, learners’ technology acceptance and perception in collaborative learning are worth highlighting. Second, emotional issues such as learners’ motivation, perceptions, and self-regulation are increasingly concerned by scholars conducting research concerning flipped or blended learningbased collaborative learning. Third, the anxiety of learners with special educational needs is increasingly concerned by scholars conducting research on collaborative learning. Furthermore, researchers should keep up with issues concerning learners’ collaboration learning anxiety, learning perceptions and satisfaction, perceived usefulness of wikis when conducting studies concerning wiki-based collaborative language learning. Additionally, from a methodological perspective, in addition to statistical methods such as learning analytics and structural equation modeling, artificial intelligence techniques such as machine learning and sentiment mining and analysis have shown an increase in applications in the research field.
CONCLUSIONS: It is worth highlighting the potentially informative and valuable implications of this study, which may help scholars, policymakers, and practitioners understand the past, present, and future scientific structure of research concerning emotional issues in collaborative learning. Findings concerning influential institutions, countries/regions, and journals help scholars in identifying influential actors in the research field from whom they may learn and explore potential scientific collaborations. Copyright © 2020 The Education University of Hong Kong (EdUHK).