"Can you hear me, Hanoi?" Compensatory mechanisms employed in synchronous net-based English language learning
Bibliographic record
Abstract
Ahern (2008) writes that the ability to remove the constraints of time and place is a major hallmark of computer-mediated communication (CMC) but that it also supports real-time synchronous forms of interaction.He suggests that "synchronous technologies create a strong network bond because each of the participants must be present at the same time in order to communicate" (Ahern, 2008, p. 99).Kenning (2010, p. 6), expanding on the work of Ciekanski and Chanier (2008, p. 173) would have us view the synchrony and asynchrony as a matter of degree where "face-to-face offers greater simultaneity than audio networks, audio than textchat and text chat than a shared word processor."At Dalarna University, Sweden, we offer modes of communication at many points of Kenning's continuum with a web-based learning platform, including asynchronous document exchange and collaborative writing tools, e-mail, recorded lectures in various formats, live streamed lectures with the possibility of text questions to the lecturer in real time, textchat, and our audiovisual seminars in Marratech™ or Adobe Connect™.Our online students live in many countries around the world and come to our online learning spaces from profoundly different physical realities, so the synchronous seminar is a shared experience that is quite separate from the physical environment in which the students find themselves.Many of our net-based English for Academic Purposes (EAP) students experience that their limited English language proficiency, compounded by technical difficulties and the constraints of the online spaces available, will sometimes cause problems in synchronous seminars.On the other hand, the rich environment of Marratech™, the desktop videoconferencing system used, offers multiple modes of communication (see Figure 1).The aim of this study is to examine the use of the multiple modes available in the seminar tool Marratech to support communication by students and teachers in a synchronous online learning environment.We describe the communication problems experienced in this kind of education and the compensatory strategies "Can you hear me, Hanoi?" Compensatory Mechanisms Employed in Synchronous Net-Based English Language Learning Cunningham, Fägersten, and Holmstenemployed by students and teachers.We will consider situations where communication is disturbed because of• technical problems, such as the system expelling a student, or the purchased Internet time in a public Internet café having expired, or poor connectivity; • students not understanding the teacher because of poor sound conditions, poor perception skills in English, the teacher speaking an unfamiliar variety of English, or a combination of these; • students not understanding fellow students usually because of limited proficiency on one or both parts, possibly in combination with the technical issues mentioned above; • the teacher not understanding the student often because of the student's unintelligible pronunciation, in combination with less than optimal sound conditions. Context of StudyThis paper presents material from two online courses in English for Academic Purposes (EAP) at Dalarna University where students with different linguistic backgrounds meet in our seminars.At Dalarna University, we do not require online language students to attend campus at any stage.However, we do require students to attend timetabled, synchronous seminars.Most students are nonnative speakers of English, with a wide variety of first languages.The conditions under which they study vary tremendously.Many work full-time alongside their studies.Their previous knowledge and proficiency in English are also varied.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".