The Impact and Acceptance of Gamification by Learners in a Digital Literacy Course at the Undergraduate Level: Randomized Controlled Trial
Bibliographic record
Abstract
Background: In recent years, the integration of technology in education has revolutionized traditional learning paradigms. Digital literacy, a crucial skill in the 21st century, has become a vital aspect of modern education, enabling students to navigate, critically assess, and effectively use digital tools. As educators strive to boost engagement and learning outcomes, gamification has appeared as an auspicious pedagogical approach. By applying game mechanics to nongame contexts, gamification seeks to create a more immersive and digital learning experience. Objective: This research paper aims to investigate the impact and acceptance of gamification by learners in a digital literacy course at the undergraduate level. Methods: In a pre-post intervention study, 168 undergraduate students were randomly assigned either to the experimental group (gamification based) or control group (conventional) learning condition. Both groups of participants learned the same topics in digital literacy. Results: Empirical findings showed that participants from the experimental group had better academic performance in digital literacy than those who were not exposed to the game-based learning environment. The participants' prior experience with gamification was not found to be a significant predictor of their acceptance of gamification in a digital literacy course. Conclusions: The study provides evidence supporting the potential benefits of gamification in enhancing digital literacy education and opens the door for further exploration and implementation of gamified learning approaches in higher education settings.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".