A dynamic multimodal approach for assessing learners' interaction experience
Bibliographic record
Abstract
In this paper we seek to model the users' experience within an interactive learning environment. More precisely, we are interested in assessing three extreme trends in the interaction experience, namely flow (a perfect immersion within the task), stuck (a difficulty to maintain focused attention) and off-task (a drop out from the task). We propose a hierarchical probabilistic framework using a dynamic Bayesian network to simultaneously assess the probability of experiencing each trend, as well as the emotional responses occurring subsequently. The framework combines three-modality diagnostic variables that sense the learner's experience including physiology, behavior and performance, predictive variables that represent the current context and the learner's profile, and a dynamic structure that tracks the temporal evolution of the learner's experience. We describe the experimental study conducted to validate our approach. A protocol was established to elicit the three target trends as 44 participants interacted with three learning environments involving different cognitive tasks. Physiological activities (electroencephalography, skin conductance and blood volume pulse), patterns of the interaction, and performance during the task were recorded. We demonstrate that the proposed framework outperforms conventional non-dynamic modeling approaches such as static Bayesian networks, as well as three non-hierarchical formalisms including naive Bayes classifiers, decision trees and support vector machines.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".