Use of a Scaffolded Case Study Assignment to Enhance Students’ Scientific Literacy Skills in Undergraduate Nutritional Science Education: Comparison between Traditional Lecture and Distance Education Course Formats
Bibliographic record
Abstract
We investigated whether the implementation of a scaffolded case study assignment could increase student perceptions of their scientific literacy (SL) skills in a third year Nutritional Science course. The change in students’ SL perceptions were assessed by the completion of two surveys (administered at the start and end of the semester) consisting of questions probing a range of SL criteria relevant to undergraduate students. Additionally, we determined if the change in student perceived SL over the semester i) was related to their learning approach (i.e. deep versus surface approaches), as assessed by the Revised Two-Factor Study Process Questionnaire-2 (R-SPQ-2F), and ii) differed between course format, that is, in-class traditional lecture (LEC) and online distance education (DE). The LEC students (n=179) showed improvements in all ten SL outcomes assessed over the course of the semester, whereas the DE students (n=71) showed improvements in only six of the ten parameters assessed, however, the DE course started with a higher assessment of baseline SL capabilities. Additionally, the overall change in perceived SL capabilities was not associated with learning approach (i.e. surface or deep) in either class format. These data demonstrate that case-based learning assignments can promote students perceived SL capabilities in both traditional lecture and distance education course formats.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".