Marrying Digital and Analog with Generation Z: Confronting the Moral Panic of Digital Learning in Late Modern Society
Bibliographic record
Abstract
In some quarters, the implementation of digital technologies continues to be touted as the solution to educational challenges faced by contemporary post-secondary instructors and their students. In this paper, I examine the veracity of the claims made by the purveyors of "edu-tech," particularly in relation to what we know about learning and in light of the characterization of traditional pedagogical strategies as vestigial. The arguments advanced in that context include the ideas that "digital natives" no longer can be taught effectively by "digital immigrants," that instructors must "meet students where they live," and that changes to pedagogy go hand in glove with an understanding of the putative characteristics of today's young learners. I argue that such claims are at best inconsistent with the evidence, that major structural issues have been ignored thereby framing debates far too narrowly, and that the political and economic consequences of neoliberalism must be taken seriously if education is to be of any value, going forward. The paper offers a third, "medium" way which highlights what we know about literacy, what technology can and cannot reasonably offer, and how "analog ways" can contribute to the intellectual and social development of post-secondary students. Finally, I advance the idea that serious evaluation and implementation of such an approach might help to eclipse the "moral panic" characterizing today's educational discourse.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".