Developing A Conceptual Framework for Sustainable Development Education Through Digital Tools: Qualitative Insights from Southwest China
Bibliographic record
Abstract
This research delves into the intricate utilization of digital technology in higher education, aiming to construct a nuanced conceptual framework to interpret the digitalization of teaching sustainable development. A qualitative research design was employed, utilizing semi-structured in-depth interviews with 25 higher education teachers representing diverse experiences, backgrounds, and expertise from southwest China. The study also incorporated word frequency analysis and sentiment analysis to provide a comprehensive understanding of the emphasized themes and emotional tones in the discussions. NVivo 12.0 software facilitated meticulous coding and analytical abstraction, allowing the identification of core concepts and relationships among them. The results unveiled prominent themes such as Institutional Support and Policy Integration, Faculty Competence and Training, and Integration of Sustainable Development in Curricula. The sentiment analysis depicted a predominantly positive outlook on the integration of sustainability and digital tools in education, highlighting the perceived advancements and benefits in the field. The word frequency analysis further reinforced the focus on education, sustainability, and digital tools within the research discourse. The study contributes significantly to the academic discourse on digital sustainability education, providing intricate insights and a conceptual framework that can guide future research and practice in the field. It paves the way for refined educational methodologies, curriculum designs, and institutional policies, emphasizing the critical role of digital technology in education for sustainable development.
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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 | 0.018 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".