Exploring Interactions Between Computational and Critical Thinking in Model-Eliciting Activities Through Epistemic Network Analysis

Carmona, Guadalupe; Galarza-Tohen, Beatriz; Martinez-Medina, Gonzalo

doi:10.1007/978-3-030-93859-8_23

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Similar evaluation studies are scarce. While prior work investigated collaborative learning using epistemic network analysis [5],…”

Section: Quantitative Ethnography Methods In Learning Analyticsmentioning

confidence: 99%

“…(3) Means rotation (MR) parameter: To have the first dimension maximizing the difference between two student groups, we used the grouping variable indicating low and high learning rates to be an MR parameter. (4) Codes: To compare model performance and fitting based on different combinations of modalities, we included different numbers of codes for the two T/ONA models with and without out-of-tutor interactions (5).…”

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confidence: 99%

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Revealing Networks: Understanding Effective Teacher Practices in AI-Supported Classrooms using Transmodal Ordered Network Analysis

Borchers,

Wang,

Karumbaiah

et al. 2024

Proceedings of the 14th Learning Analytics and Knowledge Conference

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Learning analytics research increasingly studies classroom learning with AI-based systems through rich contextual data from outside these systems, especially student-teacher interactions. One key challenge in leveraging such data is generating meaningful insights into effective teacher practices. Quantitative ethnography bears the potential to close this gap by combining multimodal data streams into networks of co-occurring behavior that drive insight into favorable learning conditions. The present study uses transmodal ordered network analysis to understand effective teacher practices in relationship to traditional metrics of in-system learning in a mathematics classroom working with AI tutors. Incorporating teacher practices captured by position tracking and human observation codes into modeling significantly improved the inference of how efficiently students improved in the AI tutor beyond a model with tutor log data features only. Comparing teacher practices by student learning rates, we find that students with low learning rates exhibited more hint use after monitoring. However, after an extended visit, students with low learning rates showed learning behavior similar to their high learning rate peers, achieving repeated correct attempts in the tutor. Observation notes suggest conceptual and procedural support differences can help explain visit effectiveness. Taken together, offering early conceptual support to students with low learning rates could make classroom practice with AI tutors more effective. This study advances the scientific understanding of effective teacher practice in classrooms learning with AI tutors and methodologies to make such practices visible.

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“…Similar evaluation studies are scarce. While prior work investigated collaborative learning using epistemic network analysis [5],…”

Section: Quantitative Ethnography Methods In Learning Analyticsmentioning

confidence: 99%

mentioning

confidence: 99%