Assessing Motivational Strategies in Serious Games Using Hidden Markov Models.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent research has extended tutor strategies to model not just interventions to offer information and activities, but also interventions to support learners ’ wills and motivation. It is important to investigate new ways, intertwined with learners ’ performance (successful completion of tasks) and judgement (self-report questionnaires), for evaluating tutor intervention strategies. One promising way is the use of physiological sensors. Within this paper, we study some motivational strategies that were implemented in a serious game called HeapMotiv to support learners ’ performance and motivation. We build several hidden Markov models which use Keller’s ARCS model of motivation and electrophysiological data (heart rate HR, skin conductance SC and EEG) and are able to identify physiological patterns correlated with different motivational strategies.
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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 | 0.001 | 0.004 |
| 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