Needs Analysis on Developing EFL Paragraph Writing Materials at Kalimantan L2 learners
Bibliographic record
Abstract
This study attempts to explore the target needs and the learning needs. This research was conducted at the second semester English department students of Palangka Raya State Islamic Institute of 2017/ 2018 academic years. The respondent was 20 EFL paragraph writing learners. The research findings were as follows: (a) in terms of the target language needs, it revealed that majority of learners (45%) were studying Paragraph Writing course; (b) it was found that majority of the respondents (50%) stated that the skills to develop through the paragraph writing course was the understanding the paragraph development; (c) it was found that majority of respondents said that grammar (45%) and mind mapping (40%) was the students’ difficulty in writing paragraph; (d) in terms of learning style, the learners preferred to get assistance from Internet than other sources, and they preferred to self-learning in the classroom activities. In terms of the learners’ appropriate teaching methods it was found that (a) Internet was preferred dominantly by the respondents (75%) as source to be included as instructional materials; (b) the source of corrective feedback preferred mostly by the respondents (80%) was teacher feedback. Therefore, it is recommended that internet-based materials are preferred for developing EFL writing materials. The materials should be made excellent integration of resources available on the web.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".