Exploring Students’ Learning Needs: Expectation and Challenges
Bibliographic record
Abstract
Needs analysis is not new in education or academic circles. Many scholars and educators in different parts of the world see this approach as a valuable tool for program development and review as it is a mechanism that can be used to link the students’ present academic learning with their future needs. This is also true with respect to language programs – the focus of the current study. At the target university, which is located in Indonesia, students are taught English following an ESP approach. Despite the program being in existence for 22 years, it is apparent there are problems with it as upon graduation many students have achieved only minimal English proficiency. To explore why this might be occurring the current study was undertaken using a mixed methods approach, specifically, large-scale, quantitative data was obtained using surveys circulated to 1000 students. This was complemented by qualitative data obtained from focus group discussions. The findings from the analysis of these two components are presented in the current paper. The findings show students at the target university have pragmatic reasons for learning English. Those include international collaboration, better life opportunities, business establishment international employment competitive, better international test outcomes, cultural awareness, and understanding English journals and books.
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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 | 0.024 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".