Identifying Transitions Between Self-Regulated Learning Operations During Game-Based Learning
Bibliographic record
Abstract
Self-regulated learning (SRL) is essential while learning with a game-based learning environment (GBLE) to effectively interact with instructional materials, monitor and regulate SRL strategy use, and increase domain knowledge.The field of SRL has had little progress in understanding how learners temporally deploy SRL operations, including Searching, Monitoring, Assembling, Rehearsing, and Translating (SMART; Winne, 2018) , during gamebased learning.This study recruited 56 undergraduate students to play Crystal Island, a GBLE focused on increasing microbiology domain knowledge.Using both log-file and eye-tracking data, learners' SMART operations were captured as they completed the game.Results found that learners engaged in Searching and Assembling/Rehearsing significantly more than any other operations.Transition matrices revealed that while some transition sequences were detrimental to learning, directly monitoring after assembling/rehearsing information were positively related to learning gains.These results have implications for designing GBLEs whose features simultaneously promote and discourage the sequential deployment of SMART operations.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".