E-Learning: Students Input for Using Mobile Devices in Science Instructional Settings
Bibliographic record
Abstract
<p class="jel-maintext">A variety of e-learning theories, models, and strategy have been developed to support educational settings. There are many factors for designing good instructional settings. This study set out to determine functionality of mobile devices, students who already have, and the student needs and views in relation to e-learning settings. The study participants are undergraduate students who are enrolled department of science education in faculty of education and electrical and electronics engineering department in faculty of engineering. Prepared questionnaire form is used to collect data. This form consists of three parts. First part of questionnaire related to mobile devices, second part related to user preferences and third part contains open ended question to get students ideas about usage of self-phones in science educational settings. Countable data are analyzed with descriptive techniques. And content analysis technique is used for written data. Findings show that mobile phones should be selected as required equipment for usage of mobile devices in e-learning setting. Other important findings that students suggest that mobile phone can be used face to face educational setting in classroom and outside of classroom without and face to face interaction to teacher or students.</p>
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.011 |
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".