Discourse Analysis of EFL College Learners’ Online Social Interaction and Attitudes towards FACEBOOK
Bibliographic record
Abstract
Computer-Mediated Communication (CMC) refers to communication through a computer between or among people, including synchronous and asynchronous modes. Whether in synchronous or asynchronous modes, a wealth of studies using CMC has reported the benefits of applying this medium to language learning. FACEBOOK is a social networking service, a form of CMC, which has attracted at least 800 million active users (Facebook, 2013). Through FACEBOOK, users could build up their own personal space, exchange messages, and participate in any online social group. From the perspective of teaching and learning, the social group could be used as a virtual classroom in which teachers and learners could exchange ideas after class to promote more independent learning and tighten social ties between teachers and learners and among learners. A large number of studies on language learners’ use of CMC have been carried out; however, there is little research investigating English as a Foreign Language (EFL) learners’ attitudes towards the application of FACEBOOK in a language learning context by using the method of discourse analysis. Thus, in this study, EFL learners’ attitudes towards the application of FACEBOOK in a college language class and their online social interactions were investigated through the quantitative and qualitative methods. The participants, a group of college students (n=20), enrolled in the English Composition class and took part in the study for one semester. Besides descriptive statistics utilized in the study, data from FACEBOOK interactions and post-interviews were analyzed through discourse analysis. The findings showed that the learners had positive attitudes towards FACEBOOK as an online learning community. Based on the findings, some implications would also be proposed to be of help to those who would like to apply FACEBOOK to an EFL context.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".