Improving Reading Comprehension with University Students: An EAP based study case in the Angolan context
Bibliographic record
Abstract
The technical and scientific development of the world, especially in the western English speaking countries brought together a significant impact of the English language in people’s lives worldwide. Today, scientific information is largely published in English. Therefore, being able to read in English has become a must, mainly for students. Given this fact, EFL teaching in Angola deserves the attention of the educational system in general, particularly in higher education. Public and private universities and many the polytechnic institutes around the country have English in their curricula. After ten years teaching EFL at ISCED (Instituto Superior de Ciências da Educação); the author recognised that students systematically showed serious difficulties in reading comprehension; a reality that most of the teachers of EFL at that institution also acknowledged. In general, however, the current practice of English teaching does not sufficiently help students to cope with such difficulties. In order to address this problem, the researcher decided to carry out a project to improve this situation. The present work concentrates on the second year students of Geography at Instituto Superior de Ciências da Educação in Benguela and Huambo, to enable them cope successfully with information in Geography; published in English. To achieve this aim, the subject teachers were interviewed and a questionnaire was administered to the target students, to get enough input about the matter under study. Subsequently, reading comprehension worksheets with topics on Geography were introduced in the reading lessons. Afterwards, the teachers of English involved in the lessons were interviewed, via e-mail, to get their perception about the students’ reaction during the lessons, and their point of view on this innovative change. The results revealed that the current practice of teaching reading needs to be changed and improvements introduced to make students develop sufficient reading comprehension competence. Using authentic texts on the students’ area of specialization is a helpful starting point towards the development of EAP teaching materials, taking into account the results from the needs analysis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".