Teaching Practicum: Does It Impact to Pre-service EFL Teachers’ Language Assessment Literacy?
Bibliographic record
Abstract
The current study aimed to dig into two issues on the development of language assessment literacy among pre-service EFL teachers after they accomplished a six-week teaching practicum. Forty-two (42) pre-service EFL teachers from the English department took part in this study on a voluntary basis. The participants consisted of sixth semester students who were enrolled in a 6-week teaching practicum program at state and private senior high schools in Semarang, Indonesia. By utilizing mixed-method design, this present study used a closed-questionnaire, focused-group interview, and semi-structured individual interview to collect the data. The findings of this study reveal that pre-service EFL teachers possess an insufficient level of language assessment literacy prior to their teaching practicum. Upon completion of their six-week teaching practicum, their language assessment literacy level is elevated to a moderate level. Additionally, this study emphasizes the significant impact of a six-week teaching practicum on the pre-service EFL teachers’ development of language assessment literacy. This includes enhancing their knowledge, skills, and fundamental principles in the field of language assessment. The teaching practicum bridges the gap between student teaching and full-fledged teacher certification by exposing pre-service EFL teachers to classroom management and allowing them to develop into their roles as teachers and assessors.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".