The Role of Hybrid Learning in Achieving the Sustainable Development Goals
Bibliographic record
Abstract
ABSTRACT Hybrid learning combines digital learning resources with conventional education approaches to expand educational offerings. While this approach has shown promise in addressing limitations of both online and in‐person instruction, significant challenges remain in ensuring equitable access and sustainable implementation. This study examined hybrid learning's relationship with the sustainable development goals (SDGs) framework through a scoping review analyzing evidence from academic literature ( n = 80) and reports from 36 global educational organizations. Our analysis identified 90 potential synergies (54%) and 45 challenges (26%) across social, economic, and environmental dimensions. The findings were analyzed under three main areas: (1) equity promotion through reduced geographical and socioeconomic barriers, (2) crisis response support during disruptions like pandemics and natural disasters, and (3) capacity building opportunities in workforce development. Based on these findings, we propose the SDG‐Hybrid Learning Alignment Framework, including a new SDG Target 4.8 (Digital‐Resilient Education) to guide hybrid learning initiatives. This framework emphasizes infrastructure standards, teaching competencies, equitable resource access, and institutional crisis continuity. Results suggest successful implementation requires integrating digital infrastructure with pedagogical approaches while considering local contexts and institutional capabilities.
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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".