Instant Messaging for Creating Interactive and Collaborative m-Learning Environments
Bibliographic record
Abstract
'Instant Messaging' (IM) and 'Presence,' which is essentially the ability of being able to detect if other users are logged in on the network and send them messages in real time, has become one of the most popular applications of the Internet, causing people to want to stay connected to the Internet for inordinate amounts of time, a phenomena that also fosters a sense of "online community," that perhaps no other application has done previously (Alvestrand, 2002). This research looks at the use of mobile devices to send instant messages that can carry much more information than the short message service (SMS) messages, but would be free to use, notwithstanding the price of getting online. We present a prototype IM system that can be used as a viable means of communicating and learning in higher education establishments. There is some evidence to show that learning using mobile devices reduces the formality of the learning experience, and helps engage reluctant learners and raise their self-confidence. In order for the learning process to be successful in online distance learning, unlike in the traditional face-to-face learning, attention must be paid to developing the participants' sense of community within their particular group. Instant messaging – or IM – is a natural medium for online community building and asynchronous/ synchronous peer discussions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".