Students’ and Instructors’ Perceptions of the Benefits and Drawbacks of Using a Twitter-based Assignment in an Undergraduate Public Health Nutrition Course
Bibliographic record
Abstract
The integration of social media in higher education may support improved communication among students and instructors and facilitate collaborative learning. Within public health education, the use of social media may enable students to critically reflect on relevant everyday experiences, while the ability to effectively communicate via social media is increasingly viewed as an important public health competency. Although the existing literature suggests benefits of the use of social media in higher education, a lack of attention to potential drawbacks has been raised. The objective of this study was to examine student experiences, including perceived benefits and challenges, of a Twitter-based assessment in a public health nutrition university course. Data consisted of students’ (n=115) written reflections, complemented by transcripts from semi-structured interviews conducted with the instructor and three teaching assistants. Three themes identified by inductive thematic analysis included engaging students with course content and one another, practicing communication skills, and navigating learning curves. Most students noted the assessment provided opportunities to apply course concepts and connect with peers. However, some did not find the assignment’s purpose to be intuitive and some resistance to the use of Twitter was noted, particularly with respect to the constraints associated with tight character limits. Other students noted minimal impact on their learning due to the superficial nature of tweets. The results underscore the importance of tying social media-based assessments to clear and realistic learning goals with appropriate student supports, as well as balancing potential benefits of experimentation with social media with the potential drawbacks.
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".