DESIGN OF MASTERY-BASED TUTORIAL IN THE BRIGHTSPACE LEARNING ENVIRONMENT FOR A FIRST YEAR THERMODYNAMICS COURSE
Bibliographic record
Abstract
University level studies can be a daunting experience for students in their first year, especially for students pursuing engineering. Not only are students expected to adapt to a new and intense learning environment, they need to develop a critical thinking approach for their coursework, which is essential in solving engineering problems. The Faculty of Engineering at the University of Manitoba uses the Brightspace Learning Environment by D2L Corporation (UM Learn) as a primary means for delivering course content, communicating with students, and assessing student performance. Of interest are the assessment tools, which can be adapted to enforce good problem solving habits and check students’ learning progress. This paper discusses the design of a mastery-based tutorial for a first year thermodynamics course that provides supplementary formative assessments to ensure students have mastered course content. A survey was distributed to evaluate the online tutorial in which students expressed mixed responses to its usefulness, although many agreed that it supported learning.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".