Junior High Teachers' Perception on Consistent Implementing Technology-Based Gamification
Bibliographic record
Abstract
The problem investigated was many junior high teachers do not consistently implement technology-based gamification (TBG) within class activities despite evidence showing it as a useful learning tool for student engagement. This study aimed to explore why junior high teachers do not consistently implement TBG and identify the barriers and potential solutions from teachers' perspectives. A modified technology acceptance model and diffusion of innovation theory were combined with cultural and local aspects to generate a comprehensive gamification acceptance model. A basic qualitative approach was suitable for the study. The research questions sought junior high teachers' perceptions about consistently implementing TBG, the obstacles, and the potential solutions. Seventeen teachers of four neighboring schools in western Canada formed the purposive sample for semistructured personal interviews. The method of data analysis was interpretive thematic coding. Study outcomes supported TBG's usefulness as a learning object and an engagement tool that offers students' sense of community. The data aiding TBG's ease of use indicated that teachers' experience, required preparation time, and technical support altered the TBG adoption rate. Also, internal and student-related pressures for teachers defined perceived social pressures and altered the TBG adoption rate. Teachers identified insufficient training as the main barrier and suggested that TBG standardization is the leading solution to inconsistent implementation of TBG. Principals may use the outcomes to remove the barriers for teachers. Districts directors can standardize TBG and measure teachers' practice with TBG. Such data may positively impact social change by supporting teachers to make informed decisions about removing barriers and improving the TBG adoption rate.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".