Control of Competences Formation of Foreign Language Bachelor Students on the Basis of LMS Moodle
Bibliographic record
Abstract
In connection with the transition to a two-tier system of training and educational programs development according to the new Federal State Educational Sandards it is necessary to improve measurement and control tools. The article gives special attention to the development of specific diagnostic tests for monitoring competences formation. The main objective of such tests is that the student, performing suggested tasks, is involved into active thinking and is to make informed decisions when problems occur. Their main aim is not to measure the relevant knowledge but to assess his level of understanding and professional thinking. The research work is relevant due to contradiction between the necessity to control the level of different competences formation and insufficient development of techniques for their assessment. The article presents an algorithm for development of tests in foreign languages, taking into account the provisions of the Common European competences. The paper is concerned with the possibilities the learning management system Moodle in the control of competencies formation of bachelor students of the Faculty of Foreign Languages by means of modern facilities. Built-in test system in Moodle provides a great opportunity for creating on-line tests with the following approaches: direct addition of the test in the course, followed by the supplement of test items, and creating new test based on a bank of questions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".