AN ANALYSIS OF THE TEACHING AND LEARNING OF ACADEMIC LITERACIES IN ONLINE BOOK CLUBS
Bibliographic record
Abstract
English for academic purposes (EAP) students require opportunities to practice and develop their English computer mediated communication to interact effectively in university contexts where English is the medium of instruction (EMI). In 2014 a literacy activity that used Facebook Groups to host student online book clubs was introduced into the curriculum of a 24 week EAP program at a major Canadian university. Informed by sociocultural theory this thesis presents insights gained through a multi-method qualitative study of the book clubs and their participants over the course of the program. The Community of Inquiry framework (Garrison, Anderson Archer, 2000) was employed to conduct a content analysis of five Facebook book club groups. The content analysis data were then analyzed with the data collected from multiple interviews with three case study students who participated in the five book clubs, a student questionnaire (n=58), and interviews with four book club instructors. The COI framework was used to record and interpret how instructor choices, actions and interventions affected social and cognitive presence of the studentsâ participation and practices in the book clubs. The interview and questionnaire data enabled me to elucidate the content analysis data and gain insights into how instructor online behaviour mediated student perceptions about their learning and participation in the activity. The thesis findings indicate that instructor book selection, scaffolding, modelling, and manner significantly mediated student perceptions regarding their engagement, participation, and interaction in the activity, specifically, whether students thought that they: (1) read extensively, and practiced essential academic reading strategies; (2) demonstrated high cognitive presence in their written comments; (3) and interacted and scaffolded each otherâ s learning. This thesis contributes to the L2 online learning literature in significant ways. The multi-method approach over 24 weeks provides an in-depth understanding of teaching and learning processes in an EAP online learning context that is currently absent in the literature. The study also draws attention to a need to better understand the influence and impact of politeness and pragmatics in interactive online activities such as book clubs, both between students, and between instructors and students.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".