A Framework for Considering Exploration, Interpretation, and Confirmation during Data Analysis: Computationally Assisted Analysis of Teacher-Group Interactions
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| Title: | A Framework for Considering Exploration, Interpretation, and Confirmation during Data Analysis: Computationally Assisted Analysis of Teacher-Group Interactions |
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| Language: | English |
| Authors: | Paul Hur, Chris Palaguachi, Nessrine Machaka, Christina Krist, Elizabeth B. Dyer, Cynthia D’Angelo, Nigel Bosch |
| Source: | Journal of Educational Data Mining. 2026 18(1):180-207. |
| Availability: | International Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: https://jedm.educationaldatamining.org/index.php/JEDM |
| Peer Reviewed: | Y |
| Page Count: | 28 |
| Publication Date: | 2026 |
| Sponsoring Agency: | National Science Foundation (NSF), Division of Research on Learning in Formal and Informal Settings (DRL) |
| Contract Number: | 1920796 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | High Schools Secondary Education |
| Descriptors: | Artificial Intelligence, Man Machine Systems, Teacher Student Relationship, High School Teachers, Teacher Behavior, Interaction, Evaluation Methods, Models, Small Group Instruction, Verbal Communication, Nonverbal Communication, Role, Educational Researchers |
| ISSN: | 2157-2100 |
| Abstract: | Education researchers increasingly analyze heterogeneous, multimodal data with computational tools. Yet, reporting rarely makes explicit who (human or computer) leads meaning-making at different points in the analysis. We introduce a framework for analytic agency that distinguishes three stages, exploration, interpretation, and confirmation, and classifies each as primarily human- or computer-led, as considering stage-level leadership can clarify assumptions in analysis. We demonstrate the framework in a multimodal case study of teacher-student group interactions in high school mathematics classrooms. Using 15 classroom videos from three teachers, we selected 21 student groups and developed a pose-based detector that flags interactions. The pipeline aligned group-level audio and word-level transcripts to each detected window and computed acoustic/prosodic features and large-language-model indicators for question-asking, confusion, help-seeking, and math talk. Across the corpus, the detector surfaced 317 interaction events (M = 15.10 per group, SD = 12.42; mean duration = 32.73s). We compared before, during, and after segments using paired tests and mixedeffects models. Naturally, results for mixed-effects models showed significant shifts in keypoints before-toduring and before-to-after for those emphasized in the detection approach, while audio features showed no significant changes. One transcript indicator, confusion, decreased after interactions (β = -0.061, p = 0.049). The pipeline showed preferences for spatial co-presence rather than interaction discourse change, which illustrates how leadership in exploration shaped what became detectable and, consequently, how interpretation proceeded. In the paper's conclusion, we outline hybrid, iterative variants and discuss limitations. Making stage-level agency explicit can help researchers align methodological choices with theoretical aims and produce more transparent, auditable analyses of complex classroom data. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1506628 |
| Database: | ERIC |
| Abstract: | Education researchers increasingly analyze heterogeneous, multimodal data with computational tools. Yet, reporting rarely makes explicit who (human or computer) leads meaning-making at different points in the analysis. We introduce a framework for analytic agency that distinguishes three stages, exploration, interpretation, and confirmation, and classifies each as primarily human- or computer-led, as considering stage-level leadership can clarify assumptions in analysis. We demonstrate the framework in a multimodal case study of teacher-student group interactions in high school mathematics classrooms. Using 15 classroom videos from three teachers, we selected 21 student groups and developed a pose-based detector that flags interactions. The pipeline aligned group-level audio and word-level transcripts to each detected window and computed acoustic/prosodic features and large-language-model indicators for question-asking, confusion, help-seeking, and math talk. Across the corpus, the detector surfaced 317 interaction events (M = 15.10 per group, SD = 12.42; mean duration = 32.73s). We compared before, during, and after segments using paired tests and mixedeffects models. Naturally, results for mixed-effects models showed significant shifts in keypoints before-toduring and before-to-after for those emphasized in the detection approach, while audio features showed no significant changes. One transcript indicator, confusion, decreased after interactions (β = -0.061, p = 0.049). The pipeline showed preferences for spatial co-presence rather than interaction discourse change, which illustrates how leadership in exploration shaped what became detectable and, consequently, how interpretation proceeded. In the paper's conclusion, we outline hybrid, iterative variants and discuss limitations. Making stage-level agency explicit can help researchers align methodological choices with theoretical aims and produce more transparent, auditable analyses of complex classroom data. |
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| ISSN: | 2157-2100 |
