Online social media applications for constructivism and observational learning
Bibliographic record
Abstract
Web 2.0 technologies have a range of possibilities for fostering constructivist learning and observational learning. This is due to the available applications which allow for synchronous and asynchronous interaction and the sharing of knowledge between users. Web 2.0 tools include online social media applications which have potential pedagogical benefits. Despite these potential benefits, there is inadequate utilization of online social media applications in learning management systems for pedagogical purposes. Reasons cited for the limited uptake of online social media applications in learning management systems include the lack of consideration regarding the pedagogical benefits of these applications (Christie & Garrote-Jurado, 2009, pp. 273-279). There is limited information regarding experiences of the use of online social media that foster constructivist and observational learning. Using a qualitative meta-ethnographic approach, this article explores the experiences of students and instructors regarding online social media applications for constructivism and observational learning. Constructivist criteria (Baviskar, Hartle, & Whitney, 2009, pp. 543-544) and observational learning, based on Bandura’s (2001, pp. 265-299) social cognitive theory, formed the theoretical grounding for this research. The findings suggest that discussion forums are ideal for the stimulation of constructivism and observational learning in online learning programmes.
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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 | 0.044 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".