Learning in the Future: Open, Flexible and Distributed Learning——An Interview with Dr. Badrul H. Khan
Bibliographic record
Abstract
Dr. Badrul H. Khan is an international speaker, author, educator and consultant in the field of elearning and educational technology. Dr. Badrul H. Khan is associate professor of Educational Technology Leadership (ETL) at the George Washington University ( GWU). Previously, he served as the founding Director of the ETL graduate cohort program at GWU. He also served as assistant professor of education and the founding Director of Educational Technology graduate program at the University of Texas, and served as instructional developer and evaluation specialist in the School of Medicine at Indiana University. He is famous for his research in open, flexible and distributed learning and has published a number of influential books in this field, including Web-Based Instruction (1997) , Web-Based Training (2001) , E-Learning Strategies (2004) , Managing E-Learning (2005) , and Flexible Learning in an Information Society (in press) , etc, some of which have been published in multiple languages by publishers around the globe. In addition, he is a contributing editor of Educational Technology (USA) , a consulting editor of The International Review of Research in Open and Distance Learning (Canada) , a member of the editorial advisory board of the eLearning Digest (UAE) , a member of the editorial board of Distance Education (Australia) , a member of the editorial board of Review of Education at Distance (Brazil) , a member of editorial advisory board of Media and Technology for Human Resource Development (India) , a member of the scientific committee of Journal of E-learning and Knowledge Society (Italy) , a member of the advisory board of International Journal of Learning Technology (UK), and a member of the advisory board of Indian Journal of Training Development (India). His homepage is at: http://BadrulKhan. com/khan/
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".