Redesigning for Student Success: Cultivating Communities of Practice in a Higher Education Classroom
Bibliographic record
Abstract
In this paper, I discuss the process of redesigning and teaching a mandatory, academic skill building course for students on academic probation at Mount Saint Vincent University (MSVU) in Atlantic Canada. The rationale for redesigning the course was to offer an alternative, holistic instructional approach for instructors who were teaching a modular-based curriculum. The original course was designed to focus on improving students’ individual self-efficacy and motivation for academic success; however, the social and relational nature of learning was not articulated as an underpinning theory in the curriculum. In the new curriculum, I draw on both Etienne Wenger’s (1998) notions of communities of practice as sites for learning and Howe and Strauss’ (2000; 2007) work on generational analysis as theoretical frameworks. Furthermore, I incorporate Wenger, McDermott, and Snyder’s (2002) principles for cultivating communities of practice as a way of putting theory into practice. Initial data collection led to the main inquiry question: How could a curriculum, centered on building community in the classroom, help students to cultivate meaningful learning experiences that take learning beyond a “fake it ‘til you make it” mentality? This question guided the curricular design process and also my experiences teaching the course at MSVU during the Fall semester of 2012
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".