Forbidden fruit : identity, power and investment issues in learning a second language through computer mediated communication
Bibliographic record
Abstract
In this inquiry, I use ethnographic research methods to uncover the tensions that a selected group of military officers and students from Central and Eastern Europe and Asia experienced learning English in Canada and in Europe. In both settings, I use a Participatory Action Research (PAR) approach to the inquiry to critically explore with the participants their experiences using computers for second language learning. We negotiate changes to their current perceptions of computer-assisted language learning (CALL) through the use of computer-mediated communication (CMC). This communication involved writing-based exchanges at the Canadian site and using state-of-the-art audio video transferring technology, in a multi-site videoconferencing setting with Europe. The study took place between 2001 and 2004. During the four phases of the study, I collected data through observations of online interchanges, collaborative dialogic interviews and participants' written texts in the form of journals and e-mails. Other important data sources included videotapes and field notes taken at the Canadian site and during three field trips to the European sites. I draw on Vygotsky's socio-cultural approach to language, Bakhtin's concept of learning as dialogic and Weedon's notion of identity as dynamic, constructed and contested through Discourses. The work of these three theorists helps to frame my understanding of the historical, political, cultural, pedagogical and personal influences on this multicultural group of English language learners as they negotiated their learning in a unique setting. The participants' stories suggest that video-based computer technology not only supported some of their investment in using their second language orally but also enabled them to construct more powerful subjectivities. The identity construction that took place in English online is an important consideration for these individuals from evolving democracies that are struggling for international connection and recognition. I argue that more stories need to be told so that SL researchers can re-examine their understanding and theories of language learning and communicative practices to include computer technology. I suggest that stories such as these also have important implications for learners, educators and policy makers as they consider their teaching and learning practices with computers in their second language learning contexts.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.028 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".