ERIC Number: ED599226
Record Type: Non-Journal
Publication Date: 2019-Jul
Pages: 9
Abstractor: As Provided
ISBN: N/A
ISSN: N/A
EISSN: N/A
Predicting the Quality of Collaborative Problem Solving through Linguistic Analysis of Discourse
Reilly, Joseph M.; Schneider, Bertrand
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019)
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.]
Descriptors: 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
International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF)
Authoring Institution: N/A
Grant or Contract Numbers: 1748093