Assessing Reading Strategies of Engineering Students: Think Aloud Approach
Bibliographic record
Abstract
Literacy in reading and understanding printed words is significant for all undergraduate students to succeed in their academic career. Developments in digital technology improved the quality of academic texts in terms of design, format and layout. Further, availability of academic related English language resources in electronic versions gave readers to access and read the content on computer screens or download a text to make a hard copy. These developments on the printed pages of English course books helped the readers to identify letters of English alphabet precisely. However, vocabulary and sentence structures presented in the text remained difficult to the ESL students. A study was conducted to assess the reading strategies of engineering students across the state of Andhra Pradesh, India. Subjects of the study were 52 students pursuing engineering education in the engineering colleges affiliated to Jawaharlal Nehru Technological University, India. The primary source of data was obtained through think-aloud verbal probe. Findings of the study reveal that engineering students feel disappointed and frustrated when the content presented in the course is beyond their comprehension. This research paper presents qualitative data obtained through think-aloud verbal probe. Insights from the verbal reports on reading strategies presented in this paper give new directions to the teaching-learning of English in ESL undergraduate classrooms. Findings presented from the study are significant for English teachers, researchers and curriculum designers to develop quality reading materials for ESL classrooms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".