Quantitative Impacts and Student Perceptions of Offering Multi-Attempt Lockdown Assessment in Two Engineering Core Courses: Dynamics and Thermodynamics
Bibliographic record
Abstract
Instructor-level assessment methodologies specific to engineering core curricula are synergized with institutional-level testing infrastructures to improve outcomes spanning academic integrity, grade accuracy, and elevating students' success.The combination of multiple-attempt testing within a properly proctored testing environment is explored herein.Namely, we compare the students' success rates in two different engineering core courses delivered as hybrid online/live courses during Summer 2022: Dynamics (155 students) and Thermodynamics (282 students).Assessments took place in a novel STEM-focused interwoven testing and remediation infrastructure, referred to as an Evaluation Proficiency Center (EPC), wherein students were permitted three attempts per test while they convened with the Graduate Teaching Assistants (GTAs) after machine scoring of each attempt to engage metacognition and learn from their mistakes before the next attempt.CANVAS was the Learning Management System (LMS) used for these courses, which provided Computer-Based Assessment (CBA) that facilitated the threeattempt testing.The least class average improvement from the first and the third (last) attempt for Thermodynamics was 16% in the third test.A similar comparison for dynamics attained a rate of 41% improvement for the first assessment in the course.A large students' percentage confirmed the method was effective in their learning and assessments.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".