Using "assessment for learning" practices with pre-university level students of English as a Second Language: a mixed methods study of teacher and student performance and beliefs
Bibliographic record
Abstract
The use of assessment to foster learning has become established in classroom settings in recent years, where it has drawn considerable research interest, as learners have come to take more responsibility for their learning. The Language Testing (LT) community has recently called for more research into advances in alternative assessment practices (Brookhart, 2005; Fox 2009; Harlen & Winter, 2004; McNamara 2001a, 2001b; Pellegrino et al., 2001; Poehner and Lantolf 2005; Rea-Dickins 2004; Shohamy, 2004; Turner, 2009). The present research reports on an exploratory study incorporating treatment and control groups, in which assessment for learning (AFL) principles were applied in two pre-university English for academic purposes (EAP) classes. The study focussed on student learning of a grammatical feature (the use of would and will in contingent use contexts) as a vehicle for investigating AFL. The study has sought to (a) interpret AFL by developing AFL procedures appropriate to a second language (L2) classroom, (b) apply these AFL procedures in an L2 classroom setting, and (c) investigate their effect on learning, and in addition, to investigate for evidence of the assessment bridge (AB), the area of classroom practice linking assessment, teaching, and learning. An AFL methodology for L2 settings was developed for the study in the form of teacher training. The AFL pedagogical materials included computer-assisted language learning (CALL), an online individual, group and teacher-class concept mapping exercises. The data collection instruments included the concept maps produced, classroom observation field notes, transcribed group and class discourse, teacher and student survey questionnaires, and pre- and post-treatment tests to indicate trends. The data were analyzed by mixed methods and the results triangulated. The results found evidence of several instances of the AB and suggest that the application of AFL procedures may have enhanced student learning of the modal usage in question. This study reporting concludes with a call for a research agenda in the LT community for further study of applications of an AFL approach in EAP classroom settings.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".