A Theoretical and Empirical Approach in Assessing Motivational Factors: From Serious Games To an ITS
Why this work is in the frame
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Bibliographic record
Abstract
This study investigates Serious Games (SG) to assess motivational factors appropriate to an Intelligent Tutoring System (ITS). An ITS can benefit from SG ’ elements that can highly support learners ’ motivation. Thus, identifying and assessing the effect that these factors may have on learners is a crucial step before attempting to integrate them into an ITS. We designed an experiment using a Serious Game and combined both the theoretical ARCS model of motivation and empirical physiological sensors (heart rate, skin conductance and EEG) to assess the effects of motivational factors on learners. We then identified physiological patterns correlated with one motivational factor in a Serious Game (Alarm triggers) associated with the Attention category of the ARCS model. The best result of three classifiers run on the physiological data has reached an accuracy of 73.8 % in identifying learners ’ attention level as being either above or below average. These results open the door to the possibility for an ITS to discriminate between attentive and inattentive learners.
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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 | 0.000 | 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 | 0.000 | 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