Utilizing Game Analytics to Inform and Validate Digital Game-based Assessment with Evidence-centered Game Design: A Case Study
Bibliographic record
Abstract
The purpose of this case study is to demonstrate how to utilize machine learning approaches to analyze student process data for validating and informing digital game-based assessments (DGBAs) with an evidence-centered game design (ECgD). The first analysis was conducted to examine whether students’ mastery of the overall skill required by the game can be well predicted by task-related behavioral features and if the selected key features map onto the evidence model of the ECgD. Specifically, we extracted 27 behavioral features as the indicators of students’ gameplay activities from the evidence trace files and modelled them using a machine learning algorithm—support vector machine with recursive feature elimination—to identify the key features for prediction. The key features were in turn used to predict students’ mastery of the overall skill. Results showed that students’ retry attempts on two assessment tasks were found to be most influential for prediction with a moderate to high training and testing accuracy. The second analysis was conducted to examine whether the number of learning opportunities is sufficient for evaluating students’ mastery of the overall skill as well as determine the optimal number of learning opportunities for evaluation. The approach of long short-term memory networks was used to model students’ time-series behavioral features across multiple learning opportunities for predicting their acquisition of the overall skill. Results suggested that five learning opportunities were a good balance between evaluation accuracy and practical feasibility, and they were sufficient for evaluating students’ mastery of the overall skill given the DGGA tasks.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".