An investigation of attitudes of students and teachers about participating in a context‐aware ubiquitous learning activity
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
Abstract In recent years, digital learning has been converting from e‐learning to m‐learning because of the significant growth of wireless and mobile computing technologies. Students can learn any time and any where with mobile devices. Consequently, context‐aware ubiquitous learning (u‐learning) is emerging as a new research area. It integrates wireless, mobile and context awareness technologies in order to detect the situation of the learners and provide more seamless adaptive support in the learning process. In this paper, a context‐aware u‐learning environment is developed for learning about campus vegetation in elementary schools based on an innovative approach by employing repertory grid method in designing learning content. In addition, we probe the feasibility of context‐aware u‐learning in courses by soliciting feedback from the students and teachers through interviews and questionnaires. The findings reveal that the environment is capable of enhancing students’ motivation and learning effectiveness. Moreover, it is also capable of reducing the teaching load while enabling better control of class order.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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