Adaptive serious games for computer science education
Bibliographic record
Abstract
Serious games have the potential to effectively engage students to learn, however, these games tend to struggle accommodating learners with diverse abilities and needs. Furthermore, customizing a serious game to the individual learner has historically required a great deal of effort on the part of subject matter experts, and is not always feasible for increasingly complex games. This thesis proposes the use of automatic methods to adapt serious programming games to learners' abilities. To understand the context of the problem, a survey was conducted of the serious programming game literature, which found that while many games exist, there has been very little consideration for the use of adaptation. Given the breadth of the existing serious programming game literature, a methodology was developed to support adaptation of existing games. To demonstrate the efficacy of this adaptive methodology in serious programming games, two case studies were conducted: 1) a study comparing adaptive and non-adaptive gameplay in the Gidget game, and 2) a study assessing non-adaptive gameplay, adaptive gameplay, and adaptive hints in the RoboBug game. The results from both case studies provide evidence to the need for adaptation in serious programming games, and illustrate how the adaptive methodology can be utilized to positively affect the engagement of learners and their ability to achieve learning outcomes.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".