Using a Teacher Scheme for Educational Dialogue Analysis to Investigate Student-Student Interaction Patterns for Optimal Group Activities in an Artificial Intelligence Course

Autor/inn/en	Hu, Xiaoyong; He, Wei; Chiu, Thomas K. F.; Zhao, Li
Titel	Using a Teacher Scheme for Educational Dialogue Analysis to Investigate Student-Student Interaction Patterns for Optimal Group Activities in an Artificial Intelligence Course
Quelle	In: Education and Information Technologies, 28 (2023) 7, S.8789-8813 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Zhao, Li)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-022-11556-w
Schlagwörter	Middle School Students; Artificial Intelligence; Group Activities; Group Behavior; Interaction; Experiential Learning; Cognitive Style; Peer Relationship; Novices; Expertise; Discourse Analysis; Behavior Patterns + Suchen Sie Ihr Suchwort? Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; Künstliche Intelligenz; Gruppenaktivität; Gruppenverhalten; Interaktion; Experiental learning; Erfahrungsorientiertes Lernen; Cognitive styles; Kognitiver Stil; Peer-Beziehungen; Expert appraisal; Diskursanalyse
Abstract	Recently, Artificial Intelligence (AI), seen as an engineering domain, has been introduced into school education, but its pedagogy remains unclear. In general, group learning has been applied as a primary form of instruction in hands-on engineering activities. This learning approach is more common in higher education. School students are less mature; therefore, the benefits of adopting group learning as a pedagogical approach remain unclear. Group learning quality can be reflected by student-student interactions and dialogue within a group, and is classified into four types: collective, cooperating-in-parallel, dominant/defensive, and expert/novice. Accordingly, this experimental study involved 37 middle school students, and explored how they interacted within groups when learning AI through hands-on activities in the four group learning types. The Teacher Scheme for Educational Dialogue Analysis (T-SEDA) was used to code student-student interactions and compute their frequencies, and Lag Sequential Analysis was used to analyze the behavioral interaction sequence characteristics of the four group interaction patterns. The results showed that the expert/novice group learning had higher frequency of interaction, and also produced the longest, richest, and most complex sequences. The results suggest that this is the optimal approach to learning for younger students in hands-on AI activities as it encourages group members to interact with each other and reach a consensus. The results contribute to the literature by suggesting effective practices and confirming the use of T-SEDA in a new engineering school subject. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01