An Approach for Detecting Students' Working Memory Capacity from Their Behavior in Learning Systems
Why this work is in the frame
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Bibliographic record
Abstract
Working memory capacity (WMC) is a cognitive trait that affects students' learning behaviors while performing complex cognitive tasks. Knowing students' WMC can positively enhance students' learning in many ways, for example, by providing them with adaptive content and activities to suit their individual WMC. This paper presents an approach for identifying students' WMC from their learning behaviors in learning systems. The approach as well as its implementation into an existing detection tool are introduced in this paper. The following six learning behaviors, extracted from the literature, are modeled to infer students' WMC: linear navigation, constant reverse navigation, performing simultaneous tasks, recalling learned material, revisiting passed learning objects, and corresponding learning styles.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it