Bibliographic record
Abstract
As teaching moved on-line we had to rethink and readjust what approaches to use in order to reach the outcomes. Adjustments had to be made to the designed activities especially when groups had to meet in breakout rooms. We will present the various aspects that came under scrutiny, as for example, peripheral participation, the development of mini-communities of practice, cooperation, collaboration and mediation. We analyzed instructor’s journal notes and students’ products. There were 53 students in the classes concerned. The main research question is what was effective in making participants improve learning and how did the implementation increase their understanding of working together virtually. The method used is qualitative (Creswell, & Poth, 2018). The instructor took observational notes of processes and actions during planned activities. These notes were analyzed to uncover insights. In addition, student ‘products’ of group work were analyzed for the triangulation of results. Results show that the effort put into creating more engagement in the module brought about a number of interesting results that increased student understanding. Overall, findings show that participants reiterate expectations and summarize them, the repetition allowed a better grasp and this could also be due to the fact that during the reconversion, participants had to make sure they really understood the contents, i.e. ensuring that the meanings were clear which, in turn led to a better intake of specific features. As regards working together, several issues were identified, yet overall, all students were highly successful, due mostly to a supportive approach as regards feedback or a ‘feed-through’ approach. The theoretical underpinnings came from research on learning and pointed to the requirement of additional insights on the part of instructors especially when teaching has to take into account equity, diversity, inclusion and indigenization (EDII). Instruction had to be more connected to students’ lives. Bransford et al (2000) assert that “to develop competence in an area of inquiry, students must: a) have a deep foundation of factual knowledge, b) understand facts and ideas in the context of a conceptual framework, and c) organize knowledge in ways that facilitate retrieval and application” (p. 16).
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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 | 0.028 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".