What Should the Future of Learning Look Like? Looking Back, Looking Forward
Bibliographic record
Abstract
This paper explores a possible and desirable future of technology-enhanced teaching and learning in higher education. It takes a normative lens that defines what ‘ought to be,’ based on considerations grounded in the philosophy of education. In other words, its aim is more prescriptive than predictive. It will suggest we embrace technology only to the extent that it brings us closer to realizing the pedagogical ideals of educability, personalization, and active, experiential learning. This paper examines how these principles prove helpful in prioritizing the technologies worthy of being adopted and how technology can contribute in a meaningful way on all three fronts. In addition to the principles of pedagogical innovation, practical considerations for realizing the future state will be identified. In this context, it is argued that the envisioned future of technology-enhanced teaching and learning in higher education can come to fruition only when education becomes collaborative and course creation builds incrementally on previous educational iterations, made possible through institutional support and collaborative design.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.016 | 0.030 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".