Building a case for self-regulating as a socially constructed phenomenon
Bibliographic record
Abstract
This dissertation extends work of contemporary psychologists, such as Bruner and Cole, who are struggling to investigate cognition and learning from socio-cultural perspectives. The field of cognitive psychology does not have a strong line of theories or methodologies that explain social processes and cognition in relation to each other. For the most part, cognitive psychologists have attempted to explain human thought and action in ontological isolation from the context and culture within which they occur (Martin & Sugarman, 1996). I agree with Martin and Sugarman's position that socio-cultural theory affords opportunities to develop a psychology of human learning that accounts for the emergent nature of memory and imagination as conscious practices of the agentic mind which are dynamically and reflexively1 shaped by experiences in socio-cultural settings. Throughout this dissertation I examine how self-regulating and social processes co-evolve in the context of a computer-supported learning environment. Forty-one first year undergraduate students participated in First Class Client computer conferences in groups of four throughout a semester-long course. The on-line “space” was used for discussing issues, completing and submitting assignments, and receiving instructor feedback. The design of this computer-supported learning environment was grounded in theories of self-regulating (Winne & Hadwin, 1998) and the notion that social and situational factors contribute to “in the head” cognition (Salomon, 1993). Within this course, technology played an integral role in both the teaching and learning processes. It also provided the primary means of data collection for this dissertation by capturing all discussions and assignments on-line. Throughout this dissertation, I focus on a set of strategic learning assignments that were completed individually by 41 students and submitted to on-line conferencing groups of four students. I examined processes and outcomes of self-regulating in this learning context using three contrasting theoretical lenses and grain sizes of analysis: individual constructivist, social constructionist, and symbolic interactionist. Through this multi-methodological examination of self-regulating, I illustrate a means for developing more sophisticated understandings of classroom orchestration and empirical examination of self-regulating as a reflexive and situated social process. 1Throughout the dissertation I use the term reflexive to mean reciprocal influence. That is, the individual is shaped by and in turn shapes the socio-cultural sphere.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.122 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".