A Case Study of English as Foreign Language Chinese Teachers' Use of Computer-Based Technology
Bibliographic record
Abstract
The purpose of this research was to explore the experiences of four Chinese university teachers of English as a Foreign Language (EFL) on the effectiveness of implementing computer-based technologies in their classes. Specifically, this case study sought to document the participants’ views on 1) the types of computer-based technology used in their classes; 2) the role of computer-based educational technology in teaching EFL pedagogy; 3) the potential benefits in using computer-based instructional technologies in EFL; 4) the challenges and/or barriers to the effective use of computer-based instructional technologies in EFL instruction. Using both within case and cross-case analyses, the findings reveal a complex interwoven set of perceptions and experiences computer-based technologies and English language teaching. Seven important themes emerged: 1) the school strongly encourages the use of auxiliary educational platforms; 2) the school supports teachers with many resources; 3) computer-based technologies have impacted student learning; 4) computer-based technologies have impacted the way teachers instruct; 5) computer-based technology enhance teaching effectiveness and efficiency; 6) technical difficulties associated with computer-based technologies are challenging; and 7) the COVID- 19 pandemic forced more rapid adoption of computer-based technologies. This research is especially significant as it includes a unique set of educators in a unique educational setting, implementing emerging educational technologies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".