It’s All Fun and Games until Someone Learns Something: Assessing the Learning Outcomes of Two Educational Games
Bibliographic record
Abstract
Objective – To determine whether educational games can be designed that are both fun and effective in improving information seeking skills. Methods – Two skills that are known to be particularly difficult for students taking a required information literacy test were identified. These skills are the ability to identify citations and the ability to search databases with keywords. Educational games were designed to address these two skills. The first game, Citation Tic Tac Toe, placed commonly used bibliographic citations into a tick tac toe style grid. Students were required to play the Tic Tac Toe game and subsequently given citation identification exercises. The second game arranged key concepts related to search phrases in a Magnetic Keyword interface. Students were observed searching databases before and after playing the Magnetic Keyword game and their pre- and post-play searches were analyzed. Results – Students who played the Tic Tac Toe game improved more from pretest to posttest than students who only took an online tutorial. In addition, students who played the Magnetic Keyword game demonstrated quicker database searching for their topics and expressed increased satisfaction with their results. Conclusions – Games can be created which have measurable educational outcomes and are fun. It is important, however, to establish the educational objective prior to beginning game design.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".