Enhancing Engagement Modeling in Game-Based Learning Environments with Student-Agent Discourse Analysis

Goslen, Alex; Henderson, Nathan; Rowe, Jonathan P.; Zhang, Jiayi; Hutt, Stephen; Ocumpaugh, Jaclyn; Wiebe, Eric N.; Boyer, Kristy Elizabeth; Mott, Bradford W.; Lester, James C.

doi:10.1007/978-3-031-36336-8_105

Cited by 1 publication

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The present study investigated two ITS for high school and remedial college stoichiometry chemistry, which is a domain that, to the best of our knowledge, has not been investigated in terms of moment-by-moment associations between SRL and in-tutor performance. predicted in text classification tasks, for example, by using BERT, which has been fruitfully used for similar prediction tasks in learning analytics (e.g., [21]), or recent large language models. Future work could also investigate if the SRL codes used in this study can be reliably predicted from automated transcriptions of think-aloud, which we used to automate the process of transcribing think-aloud for human labeling, which was still labor-intensive.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Using Think-Aloud Data to Understand Relations between Self-Regulation Cycle Characteristics and Student Performance in Intelligent Tutoring Systems

Borchers,

Zhang,

Baker

et al. 2024

Proceedings of the 14th Learning Analytics and Knowledge Conference

View full text Add to dashboard Cite

Numerous studies demonstrate the importance of self-regulation during learning by problem-solving. Recent work in learning analytics has largely examined students' use of SRL concerning overall learning gains. Limited research has related SRL to in-the-moment performance differences among learners. The present study investigates SRL behaviors in relationship to learners' moment-by-moment performance while working with intelligent tutoring systems for stoichiometry chemistry. We demonstrate the feasibility of labeling SRL behaviors based on AI-generated think-aloud transcripts, identifying the presence or absence of four SRL categories (processing information, planning, enacting, and realizing errors) in each utterance. Using the SRL codes, we conducted regression analyses to examine how the use of SRL in terms of presence, frequency, cyclical characteristics, and recency relate to student performance on subsequent steps in multi-step problems. A model considering students' SRL cycle characteristics outperformed a model only using in-the-moment SRL assessment. In line with theoretical predictions, students' actions during earlier, process-heavy stages of SRL cycles exhibited lower moment-by-moment correctness during problem-solving than later SRL cycle stages. We discuss system re-design opportunities to add SRL support during stages of processing and paths forward for using machine learning to speed research depending on the assessment of SRL based on transcription of think-aloud data.

show abstract

Section: Limitations and Future Workmentioning

confidence: 99%