Korean efl inservice teachers' experiences with native-speaking teachers of efl using two computer-mediated communication modes: a qualitative case study
Bibliographic record
Abstract
This qualitative case study was designed to explore the CMC experience of Korean nonnative English speaking inservice teachers who corresponded with native English speaking teachers as they used asynchronous and synchronous electronic communication. The study examined (a) the benefits of CMC that Korean participants might experience as both teachers and learners through online communication with native speakers, (b) the difficulties that the Korean participants faced when corresponding with their native English speaking partners, and (c) suggestions for the combined pedagogical use of two modes of CIVIC that Korean participants might give their students and other Korean EFL teachers. In this study the participants, three Korean English teachers and three native English speaking teachers, shared their views about their experiences. I examined them and focused on the three research questions through data collection and analysis, which I accomplished through one-to-one interviews, informal group interviews, participants' online dialogues, direct observation of the process of the participants' online communication, and my reflective journal entries, to explore the Korean participants' experiences. The data analysis was conducted inductively along with continuous data collection from the first stage. The findings include (a) the difficulties that they encountered in CIVIC with native-speaking teachers as language, emotional, cultural, technological, environmental, and time issues; (b) the benefits as language learners and language teachers through a collaborative learning environment that facilitated both inter- and intrapersonal interactions as a powerful way of learning that enables learners to co-construct meaning in conversation; and (c) the participants' suggestions to maximize the benefits of CMC for language learners. Based on the findings from this study, I believe that EFL teachers need to have a positive attitude toward exploring new ways of teaching and learning English, which are continually changing because of the fast development of technology. I also believe that CMC has strong potential in foreign language learning: It enables language learners, especially in an EFL context, to communicate with native speakers and to expose themselves to authentic target-language use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".