Team Teaching Of Thermodynamics: Rapid Instructional Development In Young Academics
Bibliographic record
Abstract
Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 3675 Team Teaching of Thermodynamics: Rapid Instructional Development in Young Academics Alan E. Nelson and Suzanne M. Kresta University of Alberta Department of Chemical and Materials Engineering Edmonton, Alberta T6G 2G6 Canada Introduction A large undergraduate teaching service course is often viewed as a teaching ghetto, where young academics learn how to teach by doing without any substantial guidance or mentoring. New faculty are often assigned to such courses during the first term of appointment and are expected to perform in the classroom without having formal teaching training or education. These new faculty are immediately confronted with the many content based aspects of university education, including lecture preparation, development of assignments, and writing of examinations. However, the successful educator must devote a similar effort to developing effective and efficient teaching practices based on student learning styles, learning objectives, and cognitive levels appropriate to the level of the course. The time needed to recognize and address diverse student learning styles, be an effective communicator, develop classroom enthusiasm through active learning and effective interaction, and to be flexible in student learning outcomes is often lost to the more urgent need to develop the course content. Consequently, student evaluations of the new instructor’s effectiveness and overall course performance are often less than anticipated, and the initial enthusiasm and energy for undergraduate teaching are lost. In most instances, this is not the result of a lack of effort by the young academic, but rather the constraints of other professional expectations. The time demands placed on these young faculty typically exceed the number of hours in a day and they quickly search for areas to recoup time. With the expectation to “publish or perish” many reduce the effort placed on course development in order to develop their research programs. While many successful educators advocate spending no more than 2 hours preparing for each new lecture, inexperience with preparing a comprehensive lecture coupled with re-learning much of the material frequently means that new instructors exceed this recommended time allotment. The time required outside of the classroom to address student questions for a large service course further reduces the time Proceedings of the 2004 American Society for Engineering Education Annual Conference & Exposition Copyright © 2004, American Society for Engineering Education
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".