Abstract
This paper contributes to a theory-grounded methodological foundation for automatic collaborative learning process analysis. It does this by illustrating how insights from the social psychology and sociolinguistics of speech style provide a theoretical framework to inform the design of a computational model. The purpose of that model is to detect prevalence of an important group knowledge integration process in raw speech data. Specifically, this paper focuses on assessment of transactivity in dyadic discussions, where a transactive contribution is operationalized as one where reasoning is made explicit, and where that reasoning builds on a prior reasoning statement within the discussion. Transactive contributions can be either self-oriented, where the contribution builds on the speaker’s own prior contribution, or other-oriented, where the contribution builds on a prior contribution of a conversational partner. Other-oriented transacts are particularly central to group knowledge integration processes. An unsupervised Dynamic Bayesian Network model motivated by concepts from Speech Accommodation Theory is presented and then evaluated on the task of estimating prevalence of other-oriented transacts in dyadic discussions. The evaluation demonstrates a significant positive correlation between an automatic measure of speech style accommodation and prevalence of other-oriented transacts (R = .36, p < .05).
Highlights
Applications of machine learning to automatic collaborative-learning process analysis are growing in popularity within the computer-supported collaborative learning (CSCL) community
Where this paper makes its contribution beyond a proof of concept for speech analysis is in illustrating how insights from the social psychology and sociolinguistics of speech style are able to provide a theoretical framework to inform the design of computational models for automated assessment of collaborative-learning processes applied to acoustic data
The purpose of the Dynamic Bayesian Networks (DBNs) described in the previous section is to obtain a measure of speech style accommodation from the raw speech collected in a session to use for testing the hypothesis that speech style accommodation positively correlates with transactivity
Summary
Applications of machine learning to automatic collaborative-learning process analysis are growing in popularity within the computer-supported collaborative learning (CSCL) community. As an example, automated assessment of one specific type of valuable student contribution to group knowledge construction, namely other-oriented transacts described below (Berkowitz & Gibbs, 1983; Berkowitz & Gibbs, 1979), We illustrate how to motivate the design of a data representation and model structure that together yield a positive proof of concept that collaborative processes can be assessed automatically in acoustic data The necessity for this methodology can be argued from a very basic understanding of how machine learning is applied. In many earlier efforts towards automated analysis of transactivity in text based interactions we have achieved higher performance when our feature based representation of the text used for machine learning included a feature that represents language similarity (Rosé et al, 2008; Ai et al, 2010) This confirms that consideration of basic language processes and how they relate to categories of behavior inform the design of effective representations for making a coding scheme learnable. The significance of this finding from a methodological standpoint is that it highlights the importance of considering the theoretical foundation for a construct when setting up a machine learning model to use for automated assessment
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