Reflections on experiences of blended learning among leading countries in this field: Lessons for Iranian Higher Education
Bibliographic record
Abstract
The purpose of this study is to examine the experiences of top universities in the field of blended education in different countries in order to develop and improve the quality of Blended education in Iran’s universities. Blended learning as the third wave of learning environments, afterwards face-to-face and electronic environments in universities and educational organizations to respond to changes in society (including and most importantly, the pandemic of Covid-19 virus) with the aim of enhancing the quality of learning, border on expanding the scope of coverage and cost reduction. In order to reach appropriate solutions in this field, the researcher has used the qualitative research approach and the comparative study method. The selection of top universities (Boston of USA, York of Canada, Manchester of England, Hong Kong education of China, Open University of Malaysia) in the field of blended education based on the official reports of the Times (2021), which considers teaching and learning in the Covid pandemic era and online education, and ranks universities. This approach is aligned with the purpose of the study and made the selection of universities possible for this study. In addition to the ranking of universities, geographical distribution and availability of data were considered in this regard. Count on theoretical foundations, specifically in the realm of the exploratory community, in addition to examining the concept and approach of the university to Blended education, categories such as the structure of the higher education system, the type of university, policies and strategies, management style, educational resources, educational and technological supports, teaching style, the tools and evaluation system were selected and then George Brody's four-stage model was used to analyze the collected data. The findings show that all five universities have officially presented a specific and systematic definition of Blended education and in this regard targeted activities such as the development of technological infrastructure, educational and technological supports, efficient teaching methods according to the nature of Blended education in this course have followed. As a result, a look at the structure and type of the higher education system of these universities illustrate that those who have enjoyed more independence have predicted a more creative planning and strategy in relation to the studied components. The support system in all five universities is prepared according to the cultural and social conditions of the audience. Teaching and face-to-face interactions between students and professors are carried out in regional units, and it is in this regard that students can benefit from professors' guidance in online classes and using software capacities during offline training. In all the studied universities, formative and final evaluation have been used to monitor the quality of the course. According to the results and these three strategies (educational changes, social changes, and cultural changes), and taking into account the existing infrastructure of virtual education in Iran, some lessons have been proposed for Blended education in Iran’s universities.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".