Predicting Cognitive Load Using Sensor Data in a Literacy Game
Bibliographic record
Abstract
Educational games are being increasingly used to support self-paced learning. However, educators and system designers often face challenges in monitoring student affect and cognitive load. Existing assessments in game-based learning environments (GBLEs) tend to focus more on outcomes rather than processes, potentially overlooking key aspects of the learning journey that include learner affect and cognitive load. To address this issue, we collected data and trained a model to track learner cognitive load while they used an online literacy game for English. We collected affect-related physiological data and pupil data during gameplay to enable the development of models that identify these latent characteristics of learner processes. Our model indicates the feasibility of using these data to track cognitive load in GBLEs. Our multimodal model distinguished different levels of cognitive load, achieving the highest Kappa (.417) and accuracy (70%). Our model reveals the importance of including affect-related features (i.e., EDA and heart rate) when predicting cognitive load and extends recent findings suggesting the benefit of using multiple channels when modeling latent aspects of learner processes. Findings also suggest that cognitive load tracking could now be used to facilitate the creation of personalized learning experiences.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".