Use of the NutriBiochem Mobile Application in Nutrition & Biochemistry Education
Bibliographic record
Abstract
Mobile technology is an expanding field that allows users to study “anytime, anywhere”. Mobile education targets students who are avid users of technology such as smartphones and tablets. Students may benefit from mobile applications as they serve to conveniently provide instructional materials on familiar devices. The NutriBiochem application (app) was developed at the University of Guelph for use in Nutrition and Biochemistry education at the undergraduate level. The app contains 12 modules related to macro/micronutrients and metabolism, with each module consisting of review cards and multiple choice quizzes. Modules cover a range of topics, from micronutrients to lipid and carbohydrate metabolism. Review cards include figures, pathway diagrams, and key points. Quiz questions are generated from a pool of over 1000 questions, and feedback detailing student proficiency in various areas is provided upon completion of each quiz. NutriBiochem is available at no cost, for any user with an iOS, Android or BlackBerry device or computer interface; at present, there have been over 3500 downloads across these platforms. The pedagogical impact of this app will be demonstrated by analysis of frequency of app use in relation to student performance, and data regarding user characteristics (such as device and feature preferences) will be presented. It is our goal to determine whether this app is a useful pedagogical tool, and to characterize functions and features of mobile applications that students find appealing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.089 | 0.042 |
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".