Understanding Student Language: An Unsupervised Dialogue Act Classification Approach

Autor/inn/en	Ezen-Can, Aysu; Boyer, Kristy Elizabeth
Titel	Understanding Student Language: An Unsupervised Dialogue Act Classification Approach
Quelle	In: Journal of Educational Data Mining, 7 (2015) 1, S.51-78 (28 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2157-2100
Schlagwörter	Classification; Dialogs (Language); Computational Linguistics; Information Retrieval; Markov Processes; Natural Language Processing; Introductory Courses; Computer Science Education; Data Analysis; Intelligent Tutoring Systems; Cooperative Learning; Classroom Communication; Computer Mediated Communication; Accuracy; Recall (Psychology); Teacher Student Relationship; Higher Education; Qualitative Research; Statistical Analysis + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Dialog; Dialogs; Dialogue; Dialogues; Linguistics; Computerlinguistik; Markowscher Prozess; Natürliche Sprache; Einführungskurs; Computer science lessons; Informatikunterricht; Auswertung; Intelligentes Tutorsystem; Kooperatives Lernen; Klassengespräch; Computerkonferenz; Abberufung; Teacher student relationships; Lehrer-Schüler-Beziehung; Hochschulbildung; Hochschulsystem; Hochschulwesen; Qualitative Forschung; Statistische Analyse
Abstract	Within the landscape of educational data, textual natural language is an increasingly vast source of learning-centered interactions. In natural language dialogue, student contributions hold important information about knowledge and goals. Automatically modeling the dialogue act of these student utterances is crucial for scaling natural language understanding of educational dialogues. Automatic dialogue act modeling has long been addressed with supervised classification techniques that require substantial manual time and effort. Recently, there is emerging interest in unsupervised dialogue act classification, which addresses the challenges related to manually labeling corpora. This paper builds on the growing body of work in unsupervised dialogue act classification and reports on the novel application of an information retrieval technique, the Markov Random Field, for the task of unsupervised dialogue act classification. Evaluation against manually labeled dialogue acts on a tutorial dialogue corpus in the domain of introductory computer science demonstrates that the proposed technique outperforms existing approaches to education-centered unsupervised dialogue act classification. Unsupervised dialogue act classification techniques have broad application in educational data mining in areas such as collaborative learning, online message boards, classroom discourse, and intelligent tutoring systems. (As Provided).
Anmerkungen	International Working Group on Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: http://www.educationaldatamining.org/JEDM/index.php/JEDM/index
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01