ONE-TO-ONE TECHNOLOGY-ENHANCED LEARNING: AN OPPORTUNITY FOR GLOBAL RESEARCH COLLABORATION
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Over the next 10 years, we anticipate that personal, portable, wirelessly-networked tech- nologies will become ubiquitous in the lives of learners — indeed, in many countries, this is already a reality. We see that ready-to-hand access creates the potential for a new phase in the evolution of technology-enhanced learning (TEL), characterized by “seamless learning spaces” and marked by continuity of the learning experience across different scenarios (or environments), and emerging from the availability of one device or more per student (“one-to-one”). One-to-one TEL has the potential to “cross the chasm” from early adopters conducting isolated design studies to adoption-based research and widespread implementation, with the help of research and evaluation that gives attention to the digital divide and other potentially negative consequences of pervasive computing. We describe technology-enhanced learning and the affordances of one-to-one computing and outline a research agenda, including the risks and challenges of reaching scale. We reflect upon how this compares with prior patterns of technology innovation and dif- fusion. We also introduce a community, called “G1:1,” that brings together leaders of major research laboratories and one-to-one TEL projects. We share a vision of global research, inviting other research groups to collaborate in ongoing activities.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it