Bibliographic record
Abstract
Serious games have become a popular alternative learning tool for computer programming education. Research has shown that serious games provide benefits including the development of problem solving skills and increased engagement in the learning process. Despite the benefits, a major challenge of developing serious games is their ability to accommodate students with different educational backgrounds and levels of competency. Learners with a high-level of competence may find a serious games to be too easy or boring, while learners with low-level competence may be frequently frustrated or find it difficult to progress through the game. One solution to this challenge is to use automated adaptation that can alter game content and adjust game tasks to a level appropriate for the learner. The use of adaptation has been successfully utilized in educational domains outside of Software Engineering, but has not been applied to serious programming games. This paper presents GidgetML, an adaptive version of the Gidget programming game, that uses machine learning to modify game tasks based on assessing and predicting learners' competencies. To assess the benefits of adaptation, we have conducted a study involving 100 students in a first-year university programming course. Our study compared the use of Gidget (non-adaptive) with GidgetML (adaptive) and found that students who played Gidget during lab sessions varied significantly in their performance while this variance was significantly reduced for students who played GidgetML.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.071 | 0.023 |
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".