Technologies of Text: Reflections on Teaching, Learning, andWriting with/in Digital Environments
Bibliographic record
Abstract
Four years ago I was invited to develop and teach a course addressing some aspect of literacy and technology within the context of a Master of Educational Technology program. I came to the project with experience in web development and an established research program in digital literacy, but with little first-hand experience of e-learning from an instructor’s perspective. In developing my materials for online delivery, I teamed with an exceptional instructional designer, Jeff Miller, and the course was launched in the fall of 2002. Teaching it since in two online offerings and one face-toface offering has afforded me an interesting perspective on curriculum and pedagogy for digital literacy, as well as on the nature of writing in new media environments. I offer this paper, then, primarily as an instructor’s reflection on the value of this journey. The paper consists of three parts: 1) a discussion of the premise of the course, 2) an overview of the context and curriculum, and 3) remarks upon a collaborative community-building assignment that revealed for both the students and myself some interesting features of hypermedia in terms of the ways in which it both promotes and confounds certain notions of writing in online environments. Toward a broader understanding of technology The premise of the course in question, entitled “Text technologies: The changing nature of reading and writing, ” has to do with broadening perceptions 123
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".