Culturally situated self-regulated learning in statistics in a computer-supported collaborative environment
Bibliographic record
Abstract
This thesis examines the role of context, especially cultural context, in contemporary theoretical models of self-regulated learning. A critical review of prominent models revealed that although current models of self-regulated learning recognize the role of social contexts in forming self-regulatory competency, they assume that, once established, self-regulation functions largely independently of the social context. However, this is not the case in social situations, nor is it the case in Eastern cultures and many non-mainstream Western sub-cultures, in which individuals typically self-regulate in relation to others. To address this issue, a situated discourse model of self-regulated learning was developed to involve both individually oriented and socially oriented regulatory processes. This model was then tested in a context of computer-supported learning in statistics. Participants were 30 Canadian male students and 30 Chinese male students who were enrolled in a major university in Canada. The students were randomly paired to learn analysis of variance for one hour as they solved a data analysis problem by using a computer tutor. Pairs were allowed to learn in a way of their own choice or simply by following the directions prescribed by the researcher. The students had little or no prior knowledge of analysis of variance. The results were consistent with research hypotheses derived from the proposed model. Compared with Chinese pairs, Canadian pairs engaged more with tasks of their own choice as revealed in the computer logs, and favoured more individually oriented actions both in solving their problem and in learning on the computer tutor as shown in their discourse. Moreover, Canadian pairs demonstrated a stronger preference for the employment of individually oriented self-regulatory strategies in the forethought and performance phases of self-regulated learning than did Chinese pairs. Furthermore, there were significant differences between Canadian pai
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".