Making It Matter: Increasing Student-Perceived Value of Microbiology through Reflective and Critical News Story Analysis
Bibliographic record
Abstract
Even before coverage and updates on COVID-19 became a daily event in mainstream news, mass media was already full of science-focused current events stories. While relevant to our everyday lives, many popular press science articles overstate conclusions, misstate details or, at worst, purposefully spread disinformation. This iterative news analysis and writing intervention was designed to increase the visibility of real-world applications of microbiology in current events (including and beyond the 2019 coronavirus disease [COVID-19] pandemic), thereby engaging students and cultivating motivation through a positive perception of course content in accordance with expectancy-value theory. This intervention can be scaled and has been successfully used in both large- and small-enrollment microbiology classes as an active learning strategy. Students engage in science literacy at multiple levels, starting with identifying credible sources, then summarizing news articles, relating them to course content, conveying the main ideas to lay audiences, identifying in turn misleading or omitted ideas, and finally writing potential exam questions on the topic. This multifaceted analysis allows students to interact with material at many different levels in a self-directed manner as students seek out and choose articles to share with their peers. To date, anecdotal evidence suggests positive gains in student interest and perceived value of studying science.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".