Formative Assessment as a Component of the Future English Teacher Training
Bibliographic record
Abstract
The article deals with the problem of the initial stage of the future English teacher training and forming basic professional teaching skills by means of the implementation of formative assessment methods into the process of studying. It reveals the urgent necessity of using a modern and reliable system of assessment as a sound foundation of a high quality education and the key role of formative assessment in the process of foreign language teaching and learning. The aim of the article is to reveal the idea of formative assessment methods introduced into the course of English Speech Practice not only as a means of the first-year students’ language competence formation but also as a tool of their pre-service training. It is illustrated how a purely language task can be supplied with a pedagogic component to organize activities as much as possible imitating the atmosphere and surroundings of a classroom with students performing the roles of teachers and pupils. The results of the research presented in the article make it evident that introducing formative assessment into a university English class is multi-purpose: teachers can assess their students’ level of language knowledge and adjust the teaching process, and students can use it in solving professionally oriented practical tasks and assessing their peers and themselves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".