Student use and pedagogical impact of a mobile learning application
Bibliographic record
Abstract
Mobile learning (m-learning) is a relevant innovation in teaching and learning in higher education. A mobile app called NutriBiochem was developed for use in biochemistry and nutrition education for students in a second year Biochemistry and Metabolism course. NutriBiochem was accessed through smartphones, tablets, or computers. Students were surveyed upon completion of the final exam (n = 88). Survey questions assessed frequency of use, motivations for use, and perceptions of app usefulness. The pedagogical impact of NutriBiochem was evaluated by measuring the relationship between frequency of use and final course grade. Just over half of the students used the app, and ∼80% of users accessed the app moderately or infrequently. Smartphones were the most common device and the preferred device on which to access the app. There were no statistical differences in mean final grade between users and nonusers. Students with higher comfort levels with technology accessed the app more broadly than those with lower level of comfort with technology. Over 75% of students agreed that NutriBiochem was a useful learning tool, but fewer (∼45%) felt it helped them perform better in the course. The findings of this study are important, as they suggest that NutriBiochem is an effective study tool for students who are comfortable with technology, and access it regularly. Overall, the use of mobile applications in science education has been shown to be: 1) effective in enhancing students' learning experience; 2) relevant and important as an emergent method of learning given modern pressures facing higher education; and, 3) met with positive student attitudes and perceptions in terms of adopting and using such technology for educational purposes.
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 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".