Toward Accuracy, Depth and Insight: How Reflective Writing Assignments Can Be Used to Address Multiple Learning Objectives in Small and Large Courses
Bibliographic record
Abstract
Writing-to-learn involves the use of low-stakes informal writing activities intended to help students reflect on concepts or ideas presented in a course. Writing-to-learn can be a flexible and effective tool to help students understand and engage with course concepts, and past research has shown that writing-to-learn activities can substantially improve performance on summative assessments. Not only is coherent writing helpful for learning, it is also a skill that students are expected to acquire during their degree. However, it can be a challenge to provide writing opportunities that are both interesting to students and easy for instructors to implement and grade, particularly in courses with a large number of students. Reflective journaling is one method that can address these learning objectives. The versatility of reflective writing means that it can be adapted to suit a number of different disciplines. In this essay, we will explore reflective writing as a subgenre of writing-to-learn activities, summarizing some of the benefits associated with these assignments that have been described in the pedagogical literature. We will then describe how to tailor the assignments to different kinds of disciplines, including STEM courses, professional programs, and the social sciences and humanities. We will provide some guidance on how to resolve tension around marking and feedback for such an assignment. Finally, we will describe our individual experiences with using this kind of assignment in two courses. As there were a number of contextual differences between the two courses, including size and discipline, our commentary is advanced within the specific context supplied by each.
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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 | 0.046 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".