IDEAS for Transforming Higher Education: An Overview of Ongoing Trends and Challenges
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.
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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 | 0.025 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.023 | 0.030 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".