Designing TPACK-English Textbook for Economic Faculty Students
Bibliographic record
Abstract
This study thoroughly investigates the educational needs of Economics students in English for Specific Purposes (ESP) and Technological Pedagogical Content Knowledge (TPACK), based on a survey of 292 students and 12 alumni from the Economics faculty. The study reveals nuanced linguistic preferences across six fundamental components: task, activity, language use, technology, pedagogy, and topic or content relevancy. The findings show that listening skills, particularly for information acquisition, were highly valued (mean score: 3.01), whereas speaking abilities such as explaining and knowledge elicitation were universally deemed critical (mean scores > 3.00), whereas activities such as making suggestions were deemed less important (mean score: 1.99). The study also identified important areas in economics education, highlighting significant topics such as Economics, Microeconomics, and Islamic Economics (mean scores > 3.00) and the impact of English proficiency levels in maximizing learning experiences. The findings demonstrate a variety of activity priorities, with a focus on collaborative instructional tactics and specific linguistic demands in economic communication. Furthermore, this study explored students’ educational needs and the design of English instructional material for economics faculty students. This design integrates technology, language skills, and economic theory to improve student learning and skill acquisition.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".