Bibliographic record
Abstract
Narrative case research has been widely utilized in educational inquiry to investigate different and changing positions and perspectives on questions of identity, curriculum and classroom practice. Despite the fact that case-study research of this kind is well suited to the investigation of changing technologies and their interpretation in different classroom settings, narrative methods have been little utilized in e-learning research. This article addresses this situation first of all by presenting psychologist Jerome Bruner's understanding of narrative as both a pervasive mode of cognition and a formal mode of inquiry – a dual emphasis that is central to understanding narrative as a research method. It then describes the elicitation of an individual teacher's narrative in an ‘active interview’ context, and presents her account of the adaptation of blog technology in a writing class. The article examines the ways in which teacher and technology are presented as agents of change in this narrative, and compares this to other, more common but less explicitly ‘narrative’ accounts in e-learning research. In doing so, the article makes significant reference to Jean-Francois Lyotard's notion of ‘meta-narratives’, arguing that that the overarching meta-narrative of technological progress still informs a great deal of research in e-learning. It concludes by making the case that the influence of this particular meta-narrative should be balanced by attention to multiple ‘micro-narratives’, which tend to tell rather different stories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.060 |
| Scholarly communication | 0.021 | 0.032 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".