Researching Game-Based Learning: A Brief Synthesis Project
Bibliographic record
Abstract
The purpose of this research synthesis project is to survey existing digital game-based learning (DGBL) research in order to generate preliminary categories that articulate analytically distinguishable cognitive competencies. These include orientations, attitudes, interactions, and dispositions that enable cognitive development through playing games. We compile an initial literature scan, limiting the language to English, then search via keyword “game-based learning” through the following educational research databases: Eric, Education Source, Communication & Mass Media Complete, Education Index Retrospective, and Teach Reference Center. This returned over 1,500 results, which we refined by filtering out papers focused on gamification, those researching populations outside of educational contexts (e.g., private business and healthcare), systematic and scoping reviews, and papers published before 2010. This focused the results closer to 1,300 papers, which we divided into two categories, research focused on learning ecologies, and research using “motivation” in its key words and/or abstract. One clear and unexpected result was the way in which DGBL research inconsistently discusses motivation, and how it mainly seems to be used as a catch-all for measuring GBL outcomes. This brief synthesis reveals that much more attention needs to be paid to whether and how potentially pat constructs like motivation are being deployed in GBL studies.
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 | 0.040 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".