Mobile microblogging: Using Twitter and mobile devices in an online course to promote learning in authentic contexts
Bibliographic record
Abstract
This research applied a mixed-method design to explore how best to promote learning in authentic contexts in an online graduate course in instructional message design. The students used Twitter apps on their mobile devices to collect, share, and comment on authentic design examples found in their daily lives. The data sources included tweets (i.e., postings on Twitter), students’ perceptions about mobile microblogging activities, and self-reported Twitter usage. Based on the tweet analysis, we found that the students appropriately applied the design principles and design terms in their critique of design examples. While the students were mainly engaged in assignment-relevant activities, they spontaneously generated social tweets as they related peers’ authentic design examples to their own life experiences. Overall, they had positive perceptions toward the mobile microblogging activities. The students also indicated that the design examples shared by peers through mobile microblogging inspired their own message design work. We synthesized instructional design suggestions and challenges for educators interested in incorporating mobile microblogging in their instructional settings.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".