Application of Prompt Learning Models in Identifying the Collaborative Problem Solving Skills in an Online Task
Mengxiao Zhu, Xin Wang, Xiantao Wang, Zihang Chen, Wei Huang

TL;DR
This paper introduces a prompt-based learning pre-trained model that effectively identifies collaborative problem solving skills from behavioral data, outperforming traditional models especially with limited training data.
Contribution
It presents a novel prompt-based learning approach that achieves high accuracy in CPS skill coding with minimal training data, advancing automatic behavioral analysis.
Findings
Highest accuracy, macro F1, and kappa on large datasets
Best performance on small training sets
Potential to replace manual coding in CPS assessment
Abstract
Collaborative problem solving (CPS) competence is considered one of the essential 21st-century skills. To facilitate the assessment and learning of CPS competence, researchers have proposed a series of frameworks to conceptualize CPS and explored ways to make sense of the complex processes involved in collaborative problem solving. However, encoding explicit behaviors into subskills within the frameworks of CPS skills is still a challenging task. Traditional studies have relied on manual coding to decipher behavioral data for CPS, but such coding methods can be very time-consuming and cannot support real-time analyses. Scholars have begun to explore approaches for constructing automatic coding models. Nevertheless, the existing models built using machine learning or deep learning techniques depend on a large amount of training data and have relatively low accuracy. To address these…
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Taxonomy
TopicsInnovative Teaching and Learning Methods · Education and Critical Thinking Development · Technology-Enhanced Education Studies
