Academic English Language Needs Assessment: The Case of Undergraduate Engineering Students at Hawassa University
Bibliographic record
Abstract
The main purpose of this study was to identify the language skills and the academic tasks that undergraduate engineering students needed to carry out for their study at HU. To achieve the intended objectives, a cross-sectional survey research design with a mixed method was employed. Two sets of questionnaires were administered to systematic random samples of 284 engineering students and 100 engineering instructors and semi-structured interviews were also carried out with a purposively selected 5 engineering students and 5 engineering instructors to corroborate the results. The analysis of data from different sources showed that engineering students needed the receptive skills followed by the productive skills for their engineering study. With regard to the academic tasks in each skill, the most common and highly required tasks in a descending order in each skill were reading: textbooks, lecture notes, reference books, research papers, and manuals; writing: research reports, internship reports, exam answers, lab reports, and assignments; speaking: presentation of their internships, research reports and assignments, defenses, introductions, asking and answering questions, and expressing opinions; listening to: lecture, questions in class or defense sessions, presentations, discussions, instructions and online resources. Based on these findings implications were made for future research and classroom instruction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".