DEVELOPING A SELF-PACED ONLINE COURSE FOR GENDER-BASED VIOLENCE PREVENTION EDUCATION: PROCESS AND LESSONS LEARNED
Bibliographic record
Abstract
Research in universities shows that experiences of gender-based violence (GBV) are common among the student population. STATSCAN data reports 1 in 5 female students will experience gender-based violence before they leave university or college. Furthermore, in a recent climate survey of post-secondary institutions it was found that 71% of students at Canadian postsecondary schools either witnessed or experienced unwanted sexualized behaviours in a postsecondary setting,47% of students at Canadian post-secondary institutions witnessed or experienced discrimination on the basis of gender, gender identity or sexual orientation in the past year; 41% of all reported incidents of sexual assault were reported by students and 4 out of 5 undergraduate students surveyed at Canadian universities reported experiencing dating violence. Intervention initiatives in the form of online courses are sparse but show promising capabilities to reach and engage with large student communities. This paper describes the process and lessons learned in the development of a self-paced online course as one of many initiatives taken to prevent gender-based violence incidents on a Canadian university campus. Initially, the general framework for GBV education and prevention will be explained, and how it led to the creation of a micro e-learning collection. Then, the development methodology will be explained, using the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model to organize all decisions made, emphasizing on the analysis and design phases. Elements considered in the analysis were: (1) the requirements for the qualifications of students and student leaders, (2) reasoning for self-paced online approach, and (3) technical infrastructure and requirements. In terms of design, considered topics were: (1) content and curriculum, (2) learning approach, and (3)collaboration and networks. The evaluation plan is a long-term, phased approach that includes evaluation of: (1) the learning environment, design, and accessibility; (2) student satisfaction and engagement, (3) student learning and learning outcomes, and (4) relevance and impact. Finally, some lessons learned, and preliminary results of implementation will be discussed.
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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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".