Exploring the use of text and instant messaging in higher education classrooms
Bibliographic record
Abstract
This article examined how higher education students used text and instant messaging for academic purposes with their peers and faculty. Specifically, comfort level, frequency of use, usefulness, reasons for messaging and differences between peer-to-peer and peer-to-instructor interactions were examined. Students noted that they were very comfortable with using both text and instant messaging. Text messaging was used weekly with instructors and daily with peers. Instant messaging was used rarely with instructors but weekly with peers. Students rated text messaging as very useful and instant messaging as moderately useful for academic purposes. Key reasons cited for using both text and instant messaging included saving time, resolving administrative issues, convenience and ease of use. Text messaging appears to be the preferred mode of communication for students with respect to communicating with both peers and instructors. It is concluded that both text and instant messaging are useful and viable tools for augmenting student's communication among peers and faculty in higher education.Keywords: text messaging; instant messaging; student–faculty interaction; peer-to-peer interaction(Published: 3 September 2013)Citation: Research in Learning Technology 2013, 21: 19061 - http://dx.doi.org/10.3402/rlt.v21i0.19061
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".