Delivery of Content for an Online Graduate Course on “The Management of Innovation for Engineers”
Bibliographic record
Abstract
This paper will discuss the pedagogical impact and elaborate on the development and implementation of the first online graduate course at the Faculty of Applied Science and Engineering at the University of Toronto. The graduate course entitled "Management of Innovation in Engineering" (APS 1012H) provides students with the core concepts of innovation including; types of innovation, innovation diffusion, strategic and systems thinking, transformational change management, innovative enterprise design & development, and sustaining a culture of innovation. The key course objectives are to expose engineers to real world strategic management challenges and facilitate them to think and write critically through experiential learning. This seminar style course was considered an ideal candidate for the initial pilot for the Faculty online delivery initiative. Both the course content and the course delivery methods were new initiatives at the university. This course incorporates both academic readings to provide the broad theory of innovation and in depth lectures and discussions based on the instructors many years of hands on practical experience in innovation in a variety of industry sectors. A team-based approach was taken to produce final project reports on a variety of topical innovation subjects. The development of a 100% online delivery was an iterative process spanning three academic terms. The course was first delivered to 100% in class students in the winter term 2010. In the fall term 2010 a hybrid approach was adopted were the course was delivered simultaneously to both in class and online students. The content from the inclass lectures was captured during live lectures using a sophisticated multimedia lecture capture system and delivered asynchronously to the online students. Great effort was made to ensure, the course objectives, content, evaluation, assessment, and academic rigour was equivalent for both sections. This first iteration provided valuable insight and revealed complexities of the online delivery specific to the course syllabus. The lessons learned were then used to refine and improve the online delivery and successfully teach a 100% online Engineering graduate course for the winter 2011 term. In summary, the challenge was to teach the management of innovation using a new innovative teaching process while maintaining the experiential learning objectives in a virtual environment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.187 | 0.063 |
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".