Linguistic Knowledge Aspects in Academic Reading: Challenges and Deployed Strategies by English-Major Undergraduates at a Jordanian Institution of Higher Education
Bibliographic record
Abstract
This study aimed to investigate the linguistic knowledge aspect in academic reading, the challenges and the deployed strategies by English major undergraduates at a Jordanian institution of higher education. The importance of the study is attributed to the importance of the academic reading at university which is closely related to the academic achievement across the different academic disciplines. Data were collected by administering a questionnaire among English major students at the Hashemite University in the year 2016. The number of the respondents was 297. The data collected were analysed for its descriptive statistics, Post Hoc Tests, Scheffe Method, and frequency using the SPSS software. Cronbach’s alpha of the reliability coefficient was .93 to the difficulties of reading and .87 was to the strategies deployed by the students, which indicated high internal consistency reliability. Results showed that students faced difficulties related to their insufficient knowledge of text-structure, constructing meaning, and fluent reading. The study revealed that students most employed strategies were the metacognitive followed by the social ones. The cognitive strategies were the least to be used among the students. The study provided some pedagogical implications to be considered.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".