Research on the Promotion of EAP Teachers' Information Literacy under TPACK Framework in the Era of Digital Intelligence
Bibliographic record
Abstract
Foreign language education is steadily transitioning into the digital teaching era, driven by advancements in digital information technology and artificial intelligence. The integration of digitalization technology into English for Academic Purposes (EAP) instruction imposes increasingly demanding prerequisites on EAP educators' information literacy within the pedagogical landscape. Employing the Technological Pedagogical Content Knowledge (TPACK) framework, this paper conducts a comprehensive examination of the knowledge components and distinctive attributes of EAP instructors in the age of digital intelligence. Through an in-depth assessment of the information literacy of EAP educators at a science and technology university, this study unveils a spectrum of challenges pertinent to information literacy in the domain of academic English instruction. Subsequently, the paper offers a set of recommendations for enhancing the information literacy competencies of academic English instructors, with the overarching objective of shedding light on the professional development of EAP educators.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".