LewiSpace: an Exploratory Study with a Machine Learning Model in an Educational Game
Bibliographic record
Abstract
The use of educational games as a tool for providing learners with a playful and educational aspect is widespread. In this paper, we present an educational game that we developed to teach a chemistry lesson, namely drawing a Lewis diagram. Our game is a 3D environment known as LewiSpace and aims at balancing between playful and educational contents in order to increase engagement and motivation while learning. The game contains mainly five different missions aim at constructing Lewis diagram molecules which are organized in an ascending order of difficulty. We also conducted an experiment to gather data about learners’ cognitive and emotional states as well as their behaviours through our game by using three types of sensors (electroencephalography, eye tracking, and facial expression recognition with an optical camera) and a self report personality questionnaire (the Big Five). Primary results show that a machine learning model namely logistic regression, can predict with some success whether the learner will success or fail in each mission of our game, and paves the way for an adaptive version of the game. This latter will challenge or assist learners based on some features extracted from our data. Feature extraction integrated into a machine learning model aims mainly at providing learners’ with a real-time adaptation according to their performance and skills while progressing in our game.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".