Knowledge transfer or social competence? A comparison of German and Canadian adolescent students on their socio-motivational relationships in school
Bibliographic record
Abstract
This cross-national study investigates the perception of the impact of students’ relationships towards teachers and peers on scholastic motivation in a total sample of 1477 seventh and eighth grade German ( N = 1088) and Canadian ( N = 389) secondary school students. By applying Multigroup Confirmatory Latent Class Analysis in Mplus we confirmed four different motivation types: (1) teacher-dependent; (2) peer-dependent; (3) teacher-and-peer-dependent; (4) teacher-and-peer-independent motivation types in Québec, Canada, as they were found in a preliminary study among German students in the state of Brandenburg ( Raufelder, Jagenow, Drury, & Hoferichter, 2013 ). However, across the two samples, the class sizes varied considerable. The largest group among Canadian students was composed of teacher-and-peer-dependent students, followed by teacher-and-peer-independent students, while the largest group among German students was composed of peer-dependent students, followed by teacher-and-peer-independent students. In both settings the teacher-dependent motivation type constituted the smallest group. These results manifest the different impacts of social environmental variables on the motivation of German and Canadian students, having practical implications for school psychologists and educators in general.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".