IDEAS for Transforming Higher Education: An Overview of Ongoing Trends and Challenges
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
The recent unexpected impact of the global pandemic on higher education has caused universities, governments, students, and teachers to reexamine all components of existing systems, including how to become more effective and efficient in using technologies for education. We have seen that moving classes online—either blended or fully online—can be done rapidly, but early reports show huge variations in quality, acceptance, completion, and learning. Thus, it is important to examine the existing research literature on pedagogical innovations and practices that use technologies. To understand this complex situation, the present study examines the current technological, organisational, and pedagogical trends and challenges using an exploratory design carried out in three stages. In stage one, a literature review of the academic and grey literature was conducted, identifying 14 trends of interest. These trends were used in a workshop and interview discussion between leading experts in the higher education field. Stage two focused on identifying 108 initiatives that represent these trends. Finally, 30 of these were selected as cases for further exploration in stage three. Using thematic analysis, the 30 cases were condensed into 12 main themes that represent the innovative practices that led to development of the IDEAS framework as a signpost on the roadmap of next-generation pedagogy for transforming higher education. IDEAS is presented in the discussion alongside examples and ways to apply it in higher education contexts.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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