International Collaboration in Curriculum and Laboratory Development
Bibliographic record
Abstract
Abstract ASEE INTERNATIONAL LEARNING FORUM San Antonio, June 9=10, 2012 INTERNATIONAL COLLABORATION IN CURRICULUM AND LABORATORY DEVELOPMENT Prof R Natarajan Former Chairman, All India Council for Technical Education Former Director, Indian Institute of Technology, Madras, India prof.rnatarajan@gmail.comAbstract:International collaboration in Higher and Engineering Education has beenreceiving increasing attention of national governments, international agencies andinstitutions of higher education during the past few decades, particularly since thegeneral acceptance of globalization worldwide. Among the goals of internationalcollaboration is the addition of an international dimension to the course contentsand teaching programs. The formalization of collaboration is essentially through anMoU which sets out the objectives, mechanisms, financial arrangements and IPRissues.India has a long history of international collaboration with several countriesworldwide, also with other Asian countries, such as China, Japan, Korea andMalaysia. Some of the successful collaborations are, for example, through themechanisms of: India-China Eminent Persons Group, Japan Society for Promotionof Science, Association of Commonwealth Universities Conferences and theAnnual Asian University Presidents Conferences. The Indian Society for TechnicalEducation participated in the India-China Dialogue during the GEDC Conferencein Beijing last year, where bilateral faculty and student exchanges were discussedas a means of benefiting from the collaboration. 1The recent initiatives in several countries in Asia to join the Washington Accordhave stimulated interest in Outcomes-Based Teaching-Learning (OBTL), whichinvolves the articulation of Program Objectives and Program Outcomes. Inaddition, Howard Gardner’s Theory of Multiple Intelligences and Edgar Dale’sCone of Experience have been responsible for Curriculum and PedagogyInnovations. There are also significant changes in the objectives and design ofLaboratory Instruction and Practices.There is a number of pre-requisites for achieving success in bilateral internationalcollaboration: commitment at the top, faculty “champions” to undertake andimplement the identified tasks with enthusiasm, trust between the partners, mutualbenefit for both partners, and strategies for ensuring sustainability. 2
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 | 0.044 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.103 | 0.026 |
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".