Participation, positioning, and power: Opportunities to learn in a university Kinesiology Classroom
Bibliographic record
Abstract
Past studies in science education research have documented that people of colour and women continue to be underrepresented in science-related university programmes and careers. To better understand issues of equity and student achievement, providing equitable opportunities to learn (OTL) in diverse, content-focused classrooms has been recognized as an important aim in educational research. This critical microethnographic study examines a Canadian, undergraduate, Kinesiology classroom and how the professor’s framing of science learning made space for a range of OTL. Then I explore how students’ navigation of scientific discussions shaped their access to particular OTL. Drawing from cultural-historical activity theory and positioning theory, this dissertation brings into focus the sociohistorical and discursive practices of students and teachers in a science classroom. The research draws data from classroom videotapes, interviews, and stimulated recalls, wherein two participants were the foci of analysis. The analyses consider students’ verbal and non-verbal acts of positioning, highlighting how these positions can facilitate and/or constrained their access to OTL. In one group, a focal student appeared to take up the position of a facilitator, and in another, there appeared to be no expert or facilitator. The second student adopted a powerful role in one activity, then shifted from an initially more passive role to a mediator position in another group. My findings show how a common task led to differential contexts for learning. In particular, conflicts and student differential uptake of positions in small-group settings are examined for their ability to potentially foster unique OTL. My study’s theoretical contributions include a synthesis of sociocultural theories of learning, as well as how these theories can be strengthened through sustained attention to multi- levels of inquiry. The different grains of video analysis afford a broader look at how students learn together in scientific discussions; what resources they draw upon to make meaning; how they negotiate the multiple, interactional demands of the task; and how power and social relations shape available OTL. Implications for science education research are also raised, including suggestions for a greater analytic focus on student-led spaces, and bringing together conversations of learning, identity, and power.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".