Mobile-Assisted Learning as a Condition for Effective Development of Engineering Students’ Foreign Language Competence
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
In this article we focus on the conditions for effective development of foreign language competence that is technologically oriented methods of teaching a foreign language. The use of computers provides ample opportunities for implementation of activity and student-centered approaches, reorganization of the usual lesson structure, as well as providing the students with more autonomy. Currently, due to the extensive use of wireless devices, there is a new form of e-learning i.e. mobile learning. The experimental results showed that the use of mobile electronic multimedia courses increase the effectiveness of foreign language teaching, as in the absence of a real language environment, creates opportunity for immersion in an authentic foreign language environment, improve language competence in all kinds of speech activity, all of which leads to the development of students’ foreign language competence. These tools allow achieving results with less expenditure of time and effort, which is also an indicator for the efficiency of mobile learning means application in the development of foreign language competence.
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 | 0.001 | 0.005 |
| 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 | 0.000 | 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 it