Hybrid learning environments in higher education: A systematic review of emerging learning and teaching modalities
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
Since the onset of the pandemic, hybrid learning environments have seen significant expansion to cater to diverse learning preferences across different domains. As educators become more familiar with these settings, they encounter challenges and opportunities. Hybrid learning must meet the needs of both in-person and remote students, ensuring fair access to educational outcomes. Our review reveals varied experiences among teachers adapting to these technologies and methods. From students’ perspective, hybrid learning enhances autonomy and self-directed learning as they navigate in-person and online modalities. It suggests that personal dispositions significantly impact student engagement more than peer interactions. Educators also need additional academic development opportunities to adapt curricula for hybrid delivery. Future research should continue to explore how to better support educators and students in these settings, focusing on optimising design principles for hybrid learning, enhancing digital tool integration, and developing personalised learning paths to accommodate diverse students and improve learning outcomes.
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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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