ACTIVE LEARNING IN BLENDED INTRODUCTORY PHYSICS COURSE FOR SCIENCE PROGRAMS: INSTRUCTOR’S EXPERIENCE OF NCAT REDESIGN
Bibliographic record
Abstract
This paper describes the instructor’s perspective on course redesign using the National Council for Academic Transformation (NCAT) approach that was adopted in a large public Canadian university. NCAT is an independent, non-profit US-based organization that provides leadership in using educational technologies to redesign learning environments with the goal of producing better learning outcomes for students at a reduced cost to the institution. In 2014 Ryerson University applied for and received funding from the government of Ontario to carry out a pilot course redesign project. Two goals were simultaneously pursued in this project: improving student learning outcomes and improving the institution’s capacity to deliver thus enhanced educational experience efficiently (i.e., productivity improvement). Courses chosen for the pilot represented a broad variety of disciplines and programs. Large-enrollment introductory physics course for science program majors was among the fourteen courses selected for the pilot. The introductory physics course was redesigned following the NCAT guidelines. The goal of this particular course redesign was to turn the course into more active learning blended environment with partially flipped lectures and with a significant online component to extend learning beyond the classroom. The redesigned course was delivered in the Fall semester of 2014. With the increase of the online component of the course the role of the Teaching Assistants shifted from administering and grading toward more tutoring and mentoring roles. The redesigned format improved student active engagement and made students study more regularly. We also observed some improvement in retention and successful completion rate which was achieved without increasing the cost of course delivery. The students achieved better mastery of core physics concepts included in the course syllabus. The benefits beyond studying physics content included fostering time management skills, independence, critical thinking and problem-solving.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".