Comparing the Student Engagement with Two Versions of a Game-based Learning Tool
Bibliographic record
Abstract
Research has shown that game-based learning techniques positively impact students' engagement, motivation, and learning outcomes. We performed a study to explore the differences in student engagement with a game-based learning tool implemented on two different platforms: mobile and web-based. We developed two versions of a peer-quizzing game where the students can create quiz questions related to the learning material, which their peers can attempt to answer. The students can create three types of questions: Multiple Choice Questions, True/ False, and short answers. Students from a first-year introductory programming computer class were recruited to evaluate both versions of the game during one academic term (four months) during the Covid-19 pandemic when classes were entirely online. A bonus participation mark of up to five percent of the course was offered to students who posted at least three questions per week. In addition, we collected data about the students' in-game activities for the study duration. The Mann-Whitney U-test results show no significant difference in the engagement between the web and the game's mobile version. However, students posed more questions in the mobile version than in the Web version of the game. On the contrary, students solved more questions in the web version than in the mobile version. We have learned from the study that both game-based learning platforms effectively engage students. We also collected data about the students' experience with the game in a post-study survey. The responses show that both game versions got similar user experience ratings.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".