Facilitating Scientific Literacy Through Writing: A Write-to-Learn Assignment for Large Introductory Undergraduate Biology Courses
Bibliographic record
Abstract
Write-to-learn (WTL) assignments have been used in a variety of disciplines to encourage conceptual learning and critical thinking in undergraduate education. These assignments focus on facilitating rather than assessing learning. Conversely, write-to-communicate (WTC) assignments (e.g., lab reports and exams), often with the goal of assessing learning, are more commonly employed in foundation STEM courses. We developed a WTL assignment that focuses on promoting curiosity driven learning, critical thinking, and metacognition; skills that promote students’ scientific literacy through writing. We integrated theoretical frameworks for scientific literacy, that include the sub-constructs of third space, authenticity, and multiple discourse as well as science as a human endeavour, and metacognition and self-direction (1, 2) to develop this 3-part WTL assignment. In this assignment, students first select a topic of interest and write freely on their current understanding of the topic (Part 1). They then develop a research question based on their writing and seek answers to their question from published literature (Part 2). Finally, they reflect on their overall experience with the WTL process and propose further avenues of investigation for their research topic (Part 3). Student feedback suggests that they enjoyed the WTL process and their overall satisfaction with the structure of the assignment was high. As we continue to evolve the assignment based on student feedback, we are gratified that students reported high self-efficacy with regard to future writing as a result of participating in this assignment. We recommend use of this type of WTL assignment in large, introductory STEM courses, so as to facilitate rather than simply assess students’ learning. Primary Image: Scientific literacy through writing. Schematic depicting a write-to-learn assignment format implemented in an introductory undergraduate biology course, along with corresponding science literacy constructs.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".