THE INTERPLAY BETWEEN ADOLESCENTS’ PERCEPTIONS OF TEACHER-STUDENT RELATIONSHIPS AND THEIR ACADEMIC SELF-REGULATION: DOES LIKING A SPECIFIC TEACHER MATTER?
Bibliographic record
Abstract
Although positive teacher-student relationships are known to aid students’ academic self-regulation, the emotional aspects of teacher liking are often neglected within research. The present study used a large sample of seventh- and eighth-grade students (N = 1,088; MAge = 13.7) in secondary schools in Germany to investigate whether the motivation students gain from specific well-liked teachers (i.e., that students identify) can moderate the relation between their perception of teacher-student relationships overall and academic self-regulation (intrinsic motivation, identified, introjected, and external regulation). By means of latent moderated structural equations, students’ motivation based on liking one specific teacher was found to moderate the association between teacher-student relationships and intrinsic motivation. The present study makes a contribution to the existing research on teacher-student relationships and academic self-regulation by investigating the role of students’ motivation related to the liking of a specific teacher. Results indicate that when early adolescent students can identify a well-liked teacher, they tend to have higher levels of academic motivation. Hence, students’ motivation based on liking a single teacher compensates for generally low-quality teacher-student relationships and their respective impact on students’ intrinsic motivation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".