Mobile-Assisted Seamless Learning Activities in Higher Distance Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Among online learning factors stated in the research literature, it is argued that online activities is the strongest factor which contributes to online learning. This article illuminates mobile-assisted seamless learning activities by using laptops, tablets, or smart phones. Two conditions are compared, a) face-to-face (F2F) online webinars (web-based seminars or conferencing), b) the elements of part a, but complemented by teacher-recorded flipped classroom-videos (pre-lectures) before the F2F online webinars. Data collection consists of observations of 22 recorded F2F online webinars among 40 vocational student teachers divided into groups of 18 and 22 participants, and 12 interviews (six from each group, including both women and men). The study is theoretically within the research concept of mobile-assisted seamless learning: mediated learning anytime, anywhere, and in different contexts. The results raise some challenges and implications presented by using mobile digital devices to expand participation and motivation across different contexts for creation of ubiquitous knowledge access.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it