Adapting Literature Critique Engagement Activities for Online Learning Due to COVID-19: Use of Online Learning Groups to Promote Scientific Literacy Capabilities in Undergraduate Nutrition Education
Bibliographic record
Abstract
Maintaining scientific literacy (SL) skill development in undergraduate science education while transitioning courses from the in-person to online learning environment due to the COVID-19 pandemic requires adaptation of some teaching practices. This study assessed the effectiveness of small online learning groups as the active engagement strategy (replacing in-person breakout groups) to promote SL skill development in fourth year undergraduate nutritional science students in the online learning environment (Fall 2020 semester). As a secondary outcome, SL skill development in the online learning environment (Fall 2020, n=178) was compared to that of the in-person course format (Fall 2019, n=144). Students were surveyed at the start and end of the semester to assess their i) scientific literature comprehension, ii) SL skill perceptions, and iii) practical SL skills. The use of online learning groups contributed to improvements in both literature comprehension and SL skill perceptions (P<0.05), however, practical SL skills remained unchanged (P>0.05). There was no difference in the magnitude of improvement in students’ SL skill perceptions or their practical SL skills between course formats (P>0.05). The ability to think critically about the scientific literature was increased in both course formats, with greater improvements observed in the online course format (P=0.02). Additionally, only students in the online course format had improved comprehension of scientific methods versus the in-person format (P=0.05). Collectively, these data demonstrate that the adaptations of an in-person course to an online learning environment using small online learning groups can similarly promote the development of SL in undergraduate nutrition education.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".