Predicting the Quality of Collaborative Problem Solving through Linguistic Analysis of Discourse [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019).

Autor/inn/en	Reilly, Joseph M.; Schneider, Bertrand
Titel	Predicting the Quality of Collaborative Problem Solving through Linguistic Analysis of Discourse [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019).
Quelle	(2019), (9 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Problem Solving; Discourse Analysis; Cooperative Learning; Computer Assisted Instruction; Nonverbal Communication; Natural Language Processing; Computational Linguistics; Achievement Gains; Programming Languages; Data Analysis; Academic Achievement; Connected Discourse; Eye Movements; College Students; Computer Science Education; Correlation; Intervention; Teaching Methods; Task Analysis; Prediction + Suchen Sie Ihr Suchwort? Problemlösen; Diskursanalyse; Kooperatives Lernen; Computer based training; Computerunterstützter Unterricht; Non-verbal communication; Nonverbale Kommunikation; Natürliche Sprache; Linguistics; Computerlinguistik; Achievement gain; Leistungssteigerung; Auswertung; Schulleistung; Augenbewegung; Collegestudent; Computer science lessons; Informatikunterricht; Korrelation; Teaching method; Lehrmethode; Unterrichtsmethode; Aufgabenanalyse; Vorhersage
Abstract	Collaborative problem solving in computer-supported environments is of critical importance to the modern workforce. Coworkers or collaborators must be able to co-create and navigate a shared problem space using discourse and non-verbal cues. Analyzing this discourse can give insights into how consensus is reached and can estimate the depth of their understanding of the problem. This study uses Coh-Metrix, a natural language processing tool that measures cohesion, to analyze participant discourse from a recent multi-modal learning analytics study where novice programmers collaborated to use a block-based programming language to instruct a robot on how to solve a series of mazes. We significantly correlated thirty-five Coh-Metrix indices from the transcripts of dyads' discourse with collaboration, learning gains, and multimodal sensor values. We then fit a variety of machine learning classifiers to predict collaboration using the indices generated by Coh-Metrix as features. This study paves the way for real-time detection of (un)productive interactions from multimodal data and could lead to real-time interventions to support collaborative learning. [For the full proceedings, see ED599096.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01