Integration of Gamification in Teaching and Students’ Academic Performance: Basis for Action Plan
Bibliographic record
Abstract
Gamification has been noted to be very helpful to learners in increasing their collaboration, motivation, interest, engagement, and teamwork in the classrooms. This is the reason that this work was conducted to determine the extent of the integration of gamification in teaching as a step to help address the alarming performance of the learners in the international and local examinations. Using the descriptive-correlational research design with use of a survey questionnaire as the main data gathering tool, this work was implemented to junior high school science and mathematics teachers in the districts of Guinsiliban, Sagay and Catarman. Appropriate statistical tools were used to analyze the data to answer the inquiries of the study. Results showed that most of the respondents in science and mathematics were females, and majority of them were in Teacher I position, on the age group of 20 to 40, served for at most 10 years, proceeded to graduate studies, and specialized in science education. The respondents had moderately integrated gamification in teaching science and mathematics. While the learners of the respondents had a very satisfactory performance in science and mathematics. Demographic variables like sex, age, educational attainment, number of years of teaching, field of specialization, and teaching position did not influence the extent of integration of gamification in teaching. The study also found a positive, low correlation but significant relationship between the extent of integration of gamification and the academic performance of the learners. Lack of gadgets like laptops and computers, internet connection, lack of access to online game-based platforms, computer literacy of teachers, and time management were the most common challenges in the integration of gamification in the learning sessions of science and mathematics. The researcher recommended that future researchers may replicate this study in schools where gadgets and internet connection are available.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".