Use of Moodle LMS-Based Tests for Enhancing Linguistic Competence of Students Majoring in Foreign Language Philology
Bibliographic record
Abstract
The purpose of the study is to identify how the speech production component of the linguistic competence, which seems to be a deficiency of using the Moodle LMS in the language training, can be enhanced through the use of the voice-administered language tests. Ten Moodle-based voice-administered language tests were designed, piloted, and employed to influence the stages of speech production component and English language proficiency level both seen as an approach to enhancing the linguistic competence of students majoring in foreign language Philology. The tests assessed the components of linguistic competence such as cohesion and coherence, vocabulary (lexical resource), along with grammar, and accuracy. The course satisfaction survey was used to study the experimental group students’ perceptions towards the use of voice-administered language tests to develop linguistic competence. It was found that the designed Moodle-based voice-administered language tests could be considered a feasible and reliable option to enhance the linguistic competence in the students majoring in Foreign Language Philology. The tests had influenced the stages of the speech production component and English language proficiency level, in general. They also improve students’ cohesion and coherence, vocabulary (lexical resource), grammar, and accuracy that are seen as components of linguistic competence. Due to the intervention, there was a considerable shift in the EG students’ English vocabulary profile. There was a movement to higher levels of proficiency in the use of vocabulary compared to the CG students whose growth was marginal, mostly in the B1 level. It was also found that the EG students were generally satisfied with the intervention, the design and content of the tests, and the effectiveness of the tests in developing linguistic competence.
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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 | 0.002 |
| 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 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".