Formative Assessment Procedures and the Second Language Curriculum: Signposts for the Journey
Bibliographic record
Abstract
How teachers conceptualize classroom language learning is the result of many factors including cultural background, personality, teacher training, and experiences as a student.For some, the process is like a game of tennis: a series of exchanges between teacher and learners.For others language learning in the classroom is best described using the image of the traffic controller, with the teacher directing students toward greater proficiency through input and guidance.I find the metaphor that most clearly symbolizes the process of language learning in the classroom is the journey.In this construction the planned curriculum provides the path along which the students travel as independent learners, maintaining a pace determined by their personal goals and motivations.I consider it the role of teacher not to carry the learners through the course, but to ensure that they remain on track to achieve their goals.This conceptualization of the teacher's role as facilitator or guide raises a vital question: how can we adequately monitor each learner's progress while simultaneously performing the many other functions required of a language instructor?The answer lies in the use of planned formative assessment procedures.This article is written with the hope that others will benefit from conceptualizing formative assessment as a tool for signposting the journey of second-language learning.
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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 | 0.092 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".