Psychological interventions of virtual gamification as a motivational basis: A mixed-method systematic review
Bibliographic record
Abstract
BACKGROUNDStudents constantly seek ways to improve productivity within academia. With the advancement of technology in the recent decade, virtual implementations may provide additional support for student productivity, particularly during the COVID-19 pandemic with online learning. One of the virtual realms for motivation include gamification, which has potential as an effective tool to further bolster an individual’s source of intrinsic motivation. METHODUsing a convergent integrated synthesis approach, qualitative and quantitative studies were extracted from APA PsycInfo, ProQuest, and IEEE for relevance to virtual gamification and intrinsic motivation. Studies were reviewed based on a pre-determined and piloted screening tool. Included studies were published between 1990 and 2020 in English within Asia, North America, and/or Europe. Only systematic reviews, randomized control trials (RCTs), metaanalysis, and grey literature were included. Study screening, extraction, and quality appraisals using the Mixed Methods Appraisal Tool (MMAT) were performed independently among two authors. Disagreements following reconciliation between two authors were settled by a third author. Heterogeneity in study designs, outcomes, and measurements precluded meta and statistical analyses; thus, a qualitative analysis of studies was provided. RESULTSBased on the appraised articles, gamification improves intrinsic motivation through badges, social interactions, points, and leaderboards. Experimental studies also displayed a correlation between learning behaviour. CONCLUSIONThe data exhibited an increase in intrinsic motivation due to gamification features, which can be integrated within a virtual context to enhance motivation with potential for application towards online learning 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 | 0.022 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".