OFFERING INTERDISCPLINARY COURSES: THE WHY, THE HOW AND THE WHAT
Bibliographic record
Abstract
Students in their future workplace will likely face multifacetedchallenges; as such, solutions require integrationand collaboration across disciplines. An interdisciplinaryinstruction benefits students’ learning by exposing themto fundamental topics and perspectives that they wouldnot have been able to obtain easily within their programs.Peer learning and interactions by students from differentbackgrounds provides further learning opportunities. Thepaper is a reflection of the experience of the fourinstructors involved in teaching an interdisciplinarycourse in the area of energy who were from four distinctFaculties over the period of 2010-2012. Planning well inadvance is important to allow instructors from differentbackgrounds with varied traditions in teaching to developa working rapport. Throughout the planning stagesdedicated administrative support must be provided tofacilitate attending to logistics of setting up the coursewithin the university system. The right incentivise for theinstructors should also be provided due to higher thannormal time commitment. What was learnt that there is aneed to provide both foundation material and moreadvanced perspectives simultaneously given the diversebackground of students and topics in an interdisciplinarycourse. Also, it was found that instructors benefitted fromteaching such a course by learning from traditions andmethods in another discipline, and went on to improveother courses in their discipline both in content andteaching style. It was also found that lack of integrationwith “regular” programs, or “official” endorsing candissuade some students from participating. Other lessonsinclude issues around instructor team’s chemistry, coursecontent design, e.g. the need for group projects tointernalize the material, and the use of technology.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".