Distance Learners’ Use of Handheld Technologies
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
This study investigates how and where distance learners use handheld devices and the impact this has on learning habits, access to learning content and quality of work. It analyses the spatial dimension of anytime-anywhere learning and, with a focus on anywhere learning, it explores students’ ongoing negotiation of the flow between and across study locations. The study concludes by proposing two new concepts: the flow of places and place of space. These should help direct the framing of future studies into the places, spaces, and mobility of formal and informal seamless learning. A dataset comprising 446 responses from undergraduate students enrolled at the UK’s largest distance learning university was analysed in respect to three research questions. All age groups, study levels, and disciplines were represented. Five key findings are: most students now use handheld devices for study-related learning; the distribution of study-related learning tasks was similar in all seven study places; there is a strong, statistically-significant correlation between the number of study places in which handheld devices are used and the number of study task types performed; two fifths of students using a handheld device for learning have noticed a change in study habit and benefit to learning; and multiple regression analysis shows three variables (number of study places, number of study tasks, and change in study habits) are predictors of finding it easier to access learning materials and improved quality of learners’ work.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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