Person-Oriented Approaches to Profiling Learners in Technology-Rich Learning Environments for Ecological Learner Modeling
Bibliographic record
Abstract
Technology-rich learning environments (TREs) provide opportunities for learners to engage in complex interactions involving a multitude of cognitive, metacognitive, and affective states. Understanding learners’ distinct learning progressions in TREs demand inquiry approaches that employ well-conceived theoretical accounts of these multiple facets. The present study investigated learners’ interactions with BioWorld, a TRE developed to guide students’ clinical reasoning through diagnoses of simulated patients. We applied person-oriented analytic methods to multimodal data including verbal protocols, questionnaires, and computer logs from 78 task solutions. Latent class analysis, clustering methods, and latent profile analysis followed by logistic regression analyses revealed that students’ clinical diagnosis ability was positively correlated with advanced self-regulated learning behaviors, high confidence and cognitive strategy use, critical attention to experts’ feedback, and their positive emotional responses to feedback. The study results have the potential to contribute to a theory-guided approach to designing TREs with a data-driven assessment of multidimensional growth. Building on the study results, we introduce and discuss an ecological learner model for assessing multidimensional learner traits which can be used to design a TRE for adaptive scaffolding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".