Teacher-student rapport and gamified learning: Investigating the role of interpersonal variables in classroom integration
Bibliographic record
Abstract
Using the educational setting of Jordan, this research aims to investigate the complex relationship between teacher-student rapport and student involvement in gamified learning experiences. As it relates to the effective use of gamified learning methodologies, this research digs into the far-reaching ramifications of cultivating strong interpersonal interactions between instructors and students. This research uses a strict quantitative technique to investigate the complex relationships between 400 children and 40 teachers. This study's results shed light on an interesting and statistically significant phenomenon: a significant positive connection (r = 0.742, p 0.001) between teacher-student rapport and the amount of engagement seen in gamified learning sessions. The finding, in line with Jordan's educational reforms, highlights the critical role of positive rapport in generating dynamic and significant participation within modern instructional techniques. The practical repercussions highlight the need for teachers to work to improve their interpersonal skills. It becomes clear that this is a crucial factor in enabling effective teaching and learning, especially within the context of contemporary pedagogical approaches. The current research helps us better understand the complex dynamics at play in teacher-student relationships, illuminating their far-reaching consequences for the pursuit of educational excellence in the Jordann setting.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".