A Reading-Writing Assignment Based on Popular Literature To Enhance Learning about Microbiology
Bibliographic record
Abstract
In order to stimulate engagement in microbiology, a reading-writing assignment based on a narrative popular science book was created for a one-semester introductory microbiology course. In order to encourage critical thinking, students were required to formulate a question related to the book to research and report on. Active learning was supported by guidance and feedback at each stage of the assignment. The assignment components were graded according to a rubric based on the learning outcomes: reading comprehension, question formulation, literature research, synthesis, and written communication. Median scores for the assignment components indicated that students successfully demonstrated the learning outcomes. A question was included on the final examination, asking students to summarize their most important learning from the assignment. Qualitative analysis of the exam answers revealed a wide variety of lessons learned about the practical applications of microbiology. On average, students scored better on the assignment and the assignment-related exam question than on the final exam. There was no significant correlation between a student's performance on the final exam and their performance on either of the assignment-related assessments, suggesting that the assignment benefited students regardless of their exam-taking capability. According to surveys administered at the end of the introductory microbiology course and again when students were enrolled in a senior microbiology course, a strong majority of students found the reading-writing assignment to be engaging and informative. This assignment may be modified in various ways in order to suit the needs of other courses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".