Bibliographic record
Abstract
spectrum of terms including remote,distance, blended and massive on line courses (MOOCs)capture the latest trends in teaching/learning from JK tothe Ph.D. level. These pedagogical approaches involvecombinations of face-to-face, asynchronous, andsynchronous delivery of courses to class sizes that cansurpass 300,000 students per offering. MOOCs, typicallyasynchronous, represent an attractive delivery paradigmfor small and large institutions alike as economies of scalehold out visions of significant cashflows, promotion of aSchool’s brand to previously unreachable audiences,democratization of higher education, and enrichment of theplanet’s knowledge capital. MOOCs, however, are notwithout their challenges including: significant commitmentand up-front costs, wading into uncharted territory,defense of academic integrity/brand image, handling ofhands-on laboratory content and poor completion rates.From students’ perspectives, MOOCs present an attractiveand viable alternative permitting study in the convenienceof their home accessing resources from the world’s finestacademic institutions at competitive costs. This paper willexamine the concept of MOOCs with a focus on coursecompletion rates (dependent variable) as a function ofclass size, academic discipline, evaluation methods,delivery platform and course duration (independentvariables). The presented data set (n= 111) is partitionedinto three knowledge domains: engineering, managementand others to quantify completion rate differences acrossthe three identified categories, with emphasis on theengineering discipline. The paper will also present bestpractices for delivering engineering courses/labs based ona MOOCs model. Lessons learned from blended/distancecourse delivery experiences at McMaster University,Bachelor of Technology Program, are extended into theMOOC 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".