The Effects of Pre-service English Language Teachers’ Making Vocabulary Learning Materials in Web-Supported Situated Learning Environment on Their Vocabulary Learning
Bibliographic record
Abstract
The aim of this study was not only to help pre-service English language teachers (PS-ELTs) to design vocabulary learning materials for a web-supported situated learning (SL) environment but also to have them learn the vocabulary they used to prepare those materials in the web-supported SL environment. Also, the effects of this process on the PS-ELTs’ academic achievement, self-efficacy beliefs in designing situated learning environments (SEB-SLE), and technological pedagogical content knowledge self-confidence (TPACK-SC) were revealed. One of the advanced mixed-method designs, intervention design, was employed, and 56 PS-ELTs participated in the study. The data were collected via quantitative measurements (two scales, a vocabulary achievement test) and qualitative measurements (student diary, online messaging logs, open-ended interview form, and focus group interview records). For the quantitative data, independent samples t-test, related-samples t-test, and 2X3 repeated measures ANOVA test were used; for the qualitative data, content analysis. The results showed that there was a significant difference between the within-group gain scores and retention test scores in terms of the vocabulary achievement test and the SEB-SLE scale. However, despite a significant difference in the within-group gain scores in terms of the TPACK-SC scale, no significant difference was found between the post-test and retention test scores. Moreover, although both groups revealed no significant differences in the scores of the vocabulary achievement test and the TPACK-SC scale, the scores of the SEB-SLE scale showed a significant difference in favor of the treatment group. Related to the procedure, PS-ELTs highlighted that preparing vocabulary learning materials according to the web-supported SL model had a considerable effect on their vocabulary learning. Besides, the application process supported permanent learning and vocabulary knowledge development. What is more, the procedure helped them gain critical thinking, problem-solving, synthesis, and research skills as well as improving their TPACK.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| 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".