Grand Challenges as Educational Innovations in Higher Education: A Scoping Review of the Literature
Bibliographic record
Abstract
Grand challenges are complex problems that are common to much of society, affect large populations, and may have several possible solutions. Incorporation of grand challenges into higher education courses can facilitate the development of collaborative problem-solving skills while providing relevant and practical opportunities to experience the dynamics involved in real-world work. Although grand challenges are becoming more commonly used in higher education, to date, there has been no synthesis of how grand challenges are incorporated and the learning outcomes of engaging in grand challenge work. In this scoping review, we examined and mapped the state of evidence for the use of grand challenges in higher education. We conducted the review according to the Johanna Briggs Institute methodology for scoping reviews and considered quantitative, qualitative, and mixed-methods studies as well as literature reviews, program descriptions, and opinion papers published in English without limitations on year of publication. We used a data extraction tool to synthesize and present our findings in a tabular form with accompanying narrative summaries. The results reveal a growing global interest in the use of grand challenges in higher education while highlighting a lack of rigorous empirical evidence on the impact on student learning.
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 | 0.017 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".