Recommend computer studies courses for students taken based on supported mobile learning modes
Bibliographic record
Abstract
Mobile learning is not a concept anymore, learning and getting education has transcended the traditional physical barriers. Many mobile learning applications are developed based on different theories of learning. Both learner and teachers are excited to adopt this promising technology. Mobile phones and other hand held technologies improved significantly, emerging and evolving rapidly. Modern mobile devices are equipped with advanced features, multiple interfaces and synchronization capabilities. However the technology growth may not necessary complies with what the educational sector needs as it is not the core focus of mobile technology. On other hand new courses and disciplines are also not fully compatible with the mobile functionalities and features. This research is intended to review the existing computer studies Ontario Curriculum for High School students and the mobile functions that devices in the market have to find the relations between learning topics and hand held devices' functionalities. The results analysed and the mobile learning modes found in this research as well as the proposed recommender system, hence, can provide not only users but also course authors great help when the users make decisions of taking courses and purchasing new devices in order to do further learning as well as when the authors take different learning methods into consideration in designing learning activities.
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 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".