Including students in the learning experience: How reflective writing assignments can be used to help students engage with course content
Bibliographic record
Abstract
Writing-to-learn involves the use of low-stakes informal writing activities that help students reflect on concepts or ideas presented in a course. Writing-to-learn can be a powerful tool in helping students understand and engage with course concepts, and past research has shown that writing-to-learn activities can substantially improve performance on summative assessments (Nevid, Pastva, McClelland, 2013). Not only is writing helpful for learning, but it is also a skill that students are expected to acquire during their post-secondary degree. However, it can be a challenge to provide writing opportunities that are interesting to students and easy for instructors to implement and grade, particularly in courses with more than 30 students. Reflective journaling is one method that can address these objectives. Reflective writing can take a variety of forms. The versatility of reflective writing means that it can be adapted to suit a number of different disciplines. For example, reflective writing has been implemented in core science courses by having students reflect on how they understand a specific concept from the course, essentially giving them the opportunity to explain these concepts to themselves (Kalman, 2011). More applicable courses have implemented reflective writing by having students evaluate how the course concepts are observable in their own lives (Nevid et al, 2013). Finally, practical courses have used reflective writing as a venue for student reflection on practical experience or mock simulations so that past experiences can help inform future experiences (Sandars, 2009). Participants in this session will first hear about the different forms that reflective writing assignments can take, and will be asked to describe which form of reflective writing might best suit their discipline. We will then describe how we implemented reflective journal assignments in two courses, a mid-sized 3rd year course at the University of Toronto in Psychology, and a small 4th year course at St. Francis Xavier University in Human Kinetics. Participants will have the opportunity to see our assignment description and marking rubric for our low-stakes assignments, and learn how each of us have implemented the assignment, taking advantage of online pedagogical technologies. We will also share our end-of-course reflections on how we might change the assignment to better suit the needs of the students and instructors. Finally, we will solicit suggestions from participants on how to improve this kind of assignment and how to scale the assignment for larger 2nd and 3rd year courses.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".