A Framework for Identifying Working Memory Capacity from the Log Information of Learning Systems
Bibliographic record
Abstract
Working memory capacity (WMC) is a cognitive trait that affects students’ learning behaviors to perform complex cognitive tasks such as reading comprehension, problem solving, and making decision. Considering students’ WMC when providing them with course materials and activities helps in avoiding cognitive overload and therefore positively affects students’ learning. However, in order to consider students’ WMC in the learning process, an approach is needed to identify students’ WMC. To address this problem, we introduce a general framework to automatically identify WMC from students’ behavior in a learning system. Our approach is generic and designed to work with different learning systems. It connects to the learning systems’ database and extracts students’ behavior data to analyze them for indications about their WMC. The proposed approach has been implemented as an extension to a tool for detecting learning styles, enabling this tool to additionally identify students’ WMC. By knowing students’ WMC, teachers can provide meaningful recommendations to support students with low and high WMC. Furthermore, such information is the basis for designing adaptive systems that can automatically provide students with individualized support based on their WMC.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".