Supporting Collaborative Learning and E-Discussions Using Artificial Intelligence Techniques

Autor/inn/en	McLaren, Bruce M.; Scheuer, Oliver; Miksatko, Jan
Titel	Supporting Collaborative Learning and E-Discussions Using Artificial Intelligence Techniques
Quelle	In: International Journal of Artificial Intelligence in Education, 20 (2010) 1, S.1-46 (46 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1560-4292
DOI	10.3233/JAI-2010-0001
Schlagwörter	Artificial Intelligence; Cooperative Learning; Computer Mediated Communication; Discussion (Teaching Technique); Persuasive Discourse; Teaching Methods; Classification; Computer Uses in Education; Educational Technology; Foreign Countries; High School Students; College Students; Israel; United Kingdom + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Kooperatives Lernen; Computerkonferenz; Persuasion; Persuasive Kommunikation; Teaching method; Lehrmethode; Unterrichtsmethode; Classification system; Klassifikation; Klassifikationssystem; Computernutzung; Unterrichtsmedien; Ausland; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Collegestudent; Großbritannien
Abstract	An emerging trend in classrooms is the use of networked visual argumentation tools that allow students to discuss, debate, and argue with one another in a synchronous fashion about topics presented by a teacher. These tools are aimed at teaching students how to discuss and argue, important skills not often taught in traditional classrooms. But how do teachers support students during these e-discussions, which happen at a rapid pace, with possibly many groups of students working simultaneously? Our approach is to pinpoint and summarize important aspects of the discussions (e.g., Are students staying on topic? Are students making reasoned claims and arguments that respond to the claims and arguments of their peers?) and alert the teachers who are moderating the discussions. The key research question raised in this work: Is it possible to automate the identification of salient contributions and patterns in student e-discussions? We present the systematic approach we have taken, based on artificial intelligence (AI) techniques and empirical evaluation, to grapple with this question. Our approach started with the generation of machine-learned classifiers of individual e-discussion contributions, moved to the creation of machine-learned classifiers of pairs of contributions, and, finally, led to the development of a novel AI-based graph-matching algorithm that classifies arbitrarily sized clusters of contributions. At each of these levels, we have run systematic empirical evaluations of the resultant classifiers using actual classroom data. Our evaluations have uncovered satisfactory or better results for many of the classifiers and have eliminated others. This work contributes to the fields of computer-supported collaborative learning and artificial intelligence in education by introducing sophisticated and empirically evaluated automated analysis techniques that combine structural, textual, and temporal data. (Contains 16 footnotes, 5 tables, and 13 figures.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10