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Title: Do Speech-Based Collaboration Analytics Generalize Across Task Contexts?
We investigated the generalizability of language-based analytics models across two collaborative problem solving (CPS) tasks: an educational physics game and a block programming challenge. We analyzed a dataset of 95 triads (N=285) who used videoconferencing to collaborate on both tasks for an hour. We trained supervised natural language processing classifiers on automatic speech recognition transcripts to predict the human-coded CPS facets (skills) of constructing shared knowledge, negotiation / coordination, and maintaining team function. We tested three methods for representing collaborative discourse: (1) deep transfer learning (using BERT), (2) n-grams (counts of words/phrases), and (3) word categories (using the Linguistic Inquiry Word Count [LIWC] dictionary). We found that the BERT and LIWC methods generalized across tasks with only a small degradation in performance (Transfer Ratio of .93 with 1 indicating perfect transfer), while the n-grams had limited generalizability (Transfer Ratio of .86), suggesting overfitting to task-specific language. We discuss the implications of our findings for deploying language-based collaboration analytics in authentic educational environments.  more » « less
Award ID(s):
2019805
PAR ID:
10497792
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Association for Computing Machinery
Date Published:
Journal Name:
Proceedings of the 12th International Learning Analytics and Knowledge Conference
ISBN:
9781450395731
Page Range / eLocation ID:
208 to 218
Format(s):
Medium: X
Location:
Online USA
Sponsoring Org:
National Science Foundation
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