Benefits of Establishing Accurate Student Learning Time Estimates in Two Second-Year Integrated Engineering and Math Courses
Bibliographic record
Abstract
At the Department of Electrical and Computer Engineering and the School of Biomedical Engineering at the University of British Columbia (UBC) a system of providing and refining time estimates for student completion of homework assignments has been introduced in two integrated second-year Engineering and Math courses. Particular attention has been given to individual homework questions. In this paper findings to date are presented after two offerings of the courses in which this system was implemented. By using student feedback from the first offering to adjust instructor time estimates, instructors were able to obtain time estimates accurate to within 5 minutes of student reported averages for 77% of ELEC211 and 64% of BMEG 220 questions. Student perception of the usefulness of time estimates was generally positive, ranging from 35% to 64% of students reporting the initiative to be either ‘useful’ or ‘very useful’ over 3 years of data. Examples drawn from both courses will be discussed to demonstrate how the collected data is being used to identify areas of further improvement to assignment questions and course structure.
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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 | 0.010 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".