Learning with Web 2.0: Social Technology and Discursive Psychology
Bibliographic record
Abstract
Recent years have seen the rise of Internet technologies which facilitate activities that are, above all, social and participatory, allowing children and adults to create and share their own content, and to communicate in a wide range of forums. Correspondingly, there has been great popular and expert interest in the potential of Web 2.0 communication technologies for education. The discursive ‘spaces' enabled by Web 2.0 differ from conventional face-to-face and online educational environments in that communication largely occurs in the written form, and is informal and abbreviated. To understand the potential of these new ‘conversational’ communicative practices and technologies for formal education calls for a new research approach: one that focuses on learning through text-based, informal communication. Such a research approach has been proposed by discursive psychology, a social psychological paradigm that emerged in the 1990s which combines the insights of phenomenology, ethnomethodology and conversational analysis. The concern of this approach and of its theoretical precursors with ‘sense-making’ has been observed by educational technologists to make it clearly suitable to a study of instructional practice. This article provides an account of this discursive approach in terms of its relevance to education and applicability for new technologies. With these two key factors in mind, the article suggests how discursive psychology can be adapted in the study of Web 2.0 technologies in educational 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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.074 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| 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".