A Group Intelligence-Based Asynchronous Argumentation Learning-Assistance Platform

Autor/inn/en	Huang, Chenn-Jung; Chang, Shun-Chih; Chen, Heng-Ming; Tseng, Jhe-Hao; Chien, Sheng-Yuan
Titel	A Group Intelligence-Based Asynchronous Argumentation Learning-Assistance Platform
Quelle	In: Interactive Learning Environments, 24 (2016) 7, S.1408-1427 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1049-4820
DOI	10.1080/10494820.2015.1016533
Schlagwörter	Learning Activities; Cooperative Learning; Natural Sciences; Science Education; Grade 8; Junior High School Students; Asynchronous Communication; Electronic Learning; Experimental Groups; Pretests Posttests; Feedback (Response); Computer Assisted Instruction; Group Dynamics; Intelligence; Teaching Methods; Peer Evaluation; Control Groups; Science Instruction; Statistical Analysis; Persuasive Discourse; Concept Mapping; Questionnaires + Suchen Sie Ihr Suchwort? Lernaktivität; Kooperatives Lernen; Naturwissenschaften; Naturwissenschaftliche Bildung; School year 08; 8. Schuljahr; Schuljahr 08; Junior High Schools; Student; Students; Sekundarstufe I; Schüler; Schülerin; Computer based training; Computerunterstützter Unterricht; Gruppendynamik; Intelligenz; Klugheit; Teaching method; Lehrmethode; Unterrichtsmethode; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Statistische Analyse; Persuasion; Persuasive Kommunikation; Concept Map; Fragebogen
Abstract	Structured argumentation support environments have been built and used in scientific discourse in the literature. However, to the best our knowledge, there is no research work in the literature examining whether student's knowledge has grown during learning activities with asynchronous argumentation. In this work, an intelligent computer-supported collaborative argumentation-based learning platform that detects whether the learners address the expected discussion issues is proposed. After each learner presents an argument, a term weighting method is adopted to derive input parameters of a one-class support vector machines classifier which determines if the learners' arguments are related to the discussion topics. Notably, a peer review mechanism is established to improve the quality of the classifier. Besides, a feedback module is used to issue feedback messages to the learners if the learners have gone off on a tangent. The experimental results revealed that the students were benefited by the proposed learning-assistance platform. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01