How to Create a Student‐Generated Database, in a Large Nutrition Class, to Illustrate the Analysis of Nutrient and Food Intakes
Bibliographic record
Abstract
Abstract The completion of a 3‐d food record, using commonly available nutrient analysis software, is a typical assignment for students in nutrition and food science programs. While these assignments help students evaluate their personal diets, it is insufficient to teach students about surveys of large population cohorts. This paper shows how the Test, Survey, and Pools tool in the learning management system Blackboard™ (Blackboard Inc.) was used to collect the individual food and nutrition intake data from the 3‐d food records of students in a large introductory nutrition class. This student‐generated database was then used to illustrate population level analyses. Examples of the types of analyses include (a) use of the Estimated Average Requirement cut point method to identify nutrients of concern; (b) the use of food intakes to determine the proportion of students consuming the recommended servings of foods from each food group; (c) the analysis of intakes of nutrients that are overconsumed such as salt, saturated fat, and trans fat; and (d) correlations between macronutrients (for example, as fat intake increases, carbohydrate intake decreases). The use of a database, derived from the students own food intakes, connects with student interests, and the analysis of such a database illustrates an authentic task in the nutritional sciences.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.020 |
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".