Power, Voice and Democratization: Feminist Pedagogy and Assessment in CMC
Bibliographic record
Abstract
Academic Technologies for Learning at the University of Alberta regularly conducts a faculty survey related to the use of, and attitudes towards, the use of learning technologies in both face-to-face and distributed learning environments. The 1999-2000 survey revealed some significant differences in the ways that female and male faculty approached the use of te chnology in teaching. Subsequently, the authors pursued these trends through a project of action research in which over 40 female faculty participated. The literature on critical feminist teaching in academia provided a context for this study. Learning design preferences revealed by the female faculty who participated tended to reflect relational values common to the design of learner -centred approaches that place a high value on interactions with students, such as computer-mediated communications (CMC). While many faculty who used CMC grieved the reduction, or loss, of face -to-face contact they also appreciated the increased intimacy of, and democratization inherent in, online conversation. However, this attribute highlighted conflictual feelings about the assessment of these conversations, as the culture of the institution is based on an uneven “power balance” in the classroom. This raised the question of how a feminist teacher understands and accommodates the requirements of learner assessment in envi ronments like CMC, that encourage the construction of knowledge through collaborative conversation. This paper attempts to address this “problem of practice” for female faculty by reviewing and synthesizing the literature on critical pedagogy, feminist teaching, and the assessment of student learning in feminist classrooms. We discuss the assessment approaches used in CMC that are reflected in the stories of seven female faculty. Six issues are identified: necessary coercion, relocating authority, taken-for-granted assumptions, safety, and process as product, and addressing equity.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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 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".