M-Learning as a Convenient Support to the Learning Process in Computer Science
Bibliographic record
Abstract
Many studies undertaken in the field of education have revealed that m-learning is emerging more and more as an effective learning method with the use of smartphones. Always turned on and easily transported, smartphones can be used anywhere, at any given time and in any context. Considering the potential of m-learning, we seek to understand this novelty as a support in the learning process, especially in computer science education. To reach this end, we asked a group of students their opinion in the form of a survey, in order to know the most beneficial aspects of a mobile application in education. The outcome of this survey helped us to develop a mobile application from which students could access course news, work timing, frequently asked questions and results. After using this application, a second survey highlights students' interest. We thus show that, especially for courses such as programming courses, m-learning plays a full role as it offers accessibility to valuable information which comes in a continuous flow to support the learning process and as it offers an environment where interaction with the learning tool can be adapted and adjusted to the learner's style and needs.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".