Proposal for Need Analysis in an Exam Preparation Course: A Descriptive Study
Bibliographic record
Abstract
Need analysis is an essential element in the process of designing any language course as it seeks to cater for what learners need in their lessons. This study proposes a framework to analyse learners’ needs for exam preparation courses. The proposed framework adopts the works of Macalister, Nation, and Brindley to address different linguistic and non-linguistic needs. To the best of the researcher’s knowledge, no framework has ever been provided for teachers to carry out need analysis in the context of preparing for international exams. In this study, therefore, the framework was applied to find learners’ needs in an exam preparation course for an A2 English level international exam. The participants were 10 learners aged 10-12 enrolled on a course in a private language centre. The data were collected through a combination of quantitative and qualitative tools, that is to say, by questionnaires, tests, and classroom observations. Results revealed that the framework herein proposed gives a detailed understanding of the learners’ needs prior to the course showing that learners from this study have difficulties in the skills of reading, writing, and listening. Findings also revealed learners’ preference for a variety of classroom activities, online games, and art-crafts.
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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 | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".