Insights from an Umbrella Review of Flipped Learning in Higher Education
Bibliographic record
Abstract
There is a noticeable growth in the number of systematic reviews published in open, distance, and digital education (ODDE), with a growing focus on flipped teaching and learning, particularly in higher education, emphasizing the need to consolidate evidence and findings under one comprehensive review. This umbrella review aims to thoroughly understand the current state of flipped learning in higher education and pinpoint research gaps, analyzing 23 systematic reviews published between 2018 and 2022 from three international databases: Web of Science, Education Source, and Scopus. It delves into publication and authorship patterns while synthesizing key insights. The thematic scope of the reviews reveals that many were focused on the effectiveness of flipped learning and teaching interventions, as well as learning design. The review explored theories guiding practice and research, instructional design considerations, and the application of flipped classrooms in various fields of study. It also examined the reported challenges of the flipped classroom model. As there are a scarcity of theoretical frameworks and a lack of detailed information on the pedagogical challenges of this model, recommendations are presented to enhance research and practice of flipped teaching and learning. The results of this umbrella review provide valuable insights to guide research in future and improve the quality of systematic reviews in the field of ODDE in general and flipped teaching and learning in particular.
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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 | 0.082 | 0.236 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.027 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| 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".