Diagnosing L2 Learners' Language Skills Based on the Use of a Web-Based Assessment Tool Called DIALANG
Bibliographic record
Abstract
DIALANG is an online assessment system used for language learners who want to obtain diagnostic information about their language proficiency (Council of Europe, 2001). The purpose of this study was to examine skill-based self-assessment across different majors. This study was conducted with 68 Iranian students studying at the Alborz Institute of Higher Education. The participants' majors were Teaching English as a Foreign Language (TEFL; n = 23), English Language Literature (ELL; n = 22), and English Language Translation (ELT; n = 23). DIALANG self-assessment scales, consisting of 107 statements with Yes/No responses, were used in this study. Results indicated that ELL students had the highest overall ranking for listening skills, whereas TEFL students received the lowest overall ranking. ELL students had the highest reading skill scores while ELT students demonstrated the lowest scores. ELL students ranked their writing ability the highest, whereas TEFL students rated their writing skill the lowest. Kruskal-Wallis analysis revealed that there was no statistically significant difference in listening and reading skills across the three majors. One-way between-group ANOVA did demonstrate a statistically significant difference in the writing self-assessment statements for the three groups. Implications and directions for future research with DIALANG are provided based on results from the study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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 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".