Developing Gamification to Improve Mobile Learning in Web Design Course during the COVID-19 Pandemic
Bibliographic record
Abstract
Thailand is currently facing a widespread third wave of COVID-19 outbreaks in the second quarter of 2021. The government has encouraged social distancing compliance to work from home and study at home policy to mitigate the risks of the pandemic. As a result, educational institutions at all levels must temporarily close their services within the premises. The students need to lean towards the online system independently. Hence, this research aims to develop gamification in the Web Design course to increase the perception and achievement of Information Technology students through mobile learning. The research results showed that the students who registered for the academic year of 2020, the majority’s learning performance improved after adopting the gamification approach via developed mobile application. According to the statistical test (t-Test) results, a significant difference was discovered between pre-test and post-test scores at a significance level (α) of 0.05. The learners also rated the effectiveness of the developed game at the highest level and accepted the developed game with high consensus. In conclusion, the research findings indicate that games can be used as effective instruction media for undergraduate students and online classrooms amidst the situation of the COVID-19 pandemic.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".