Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning

Autor/inn/en	Rose, Carolyn; Wang, Yi-Chia; Cui, Yue; Arguello, Jaime; Stegmann, Karsten; Weinberger, Armin; Fischer, Frank
Titel	Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning
Quelle	In: International Journal of Computer-Supported Collaborative Learning, 3 (2008) 3, S.237-271 (35 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1556-1607
DOI	10.1007/s11412-007-9034-0
Schlagwörter	Language Patterns; Computational Linguistics; Classification; Learning Processes; Teaching Methods; Cooperative Learning; Computer Assisted Instruction; Interdisciplinary Approach; Coding; Reliability; Validity; Efficiency; Computer Software; Prediction + Suchen Sie Ihr Suchwort? Sprachmodell; Sprachstruktur; Linguistics; Computerlinguistik; Classification system; Klassifikation; Klassifikationssystem; Learning process; Lernprozess; Teaching method; Lehrmethode; Unterrichtsmethode; Kooperatives Lernen; Computer based training; Computerunterstützter Unterricht; Fächerübergreifender Unterricht; Fächerverbindender Unterricht; Interdisziplinarität; Codierung; Programmierung; Reliabilität; Gültigkeit; Effectiveness; Effektivität; Wirkungsgrad; Vorhersage
Abstract	In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners' interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multi-dimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10