Using Assessments to Improve Student Outcomes in Engineering Dynamics
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Engineering Dynamics has historically been one of the most challenging courses in the engineering curriculum. At this institution, Dynamics is taken by approximately 400 students annually and the failure rate has hovered around 15-20% for the past 10 years. This rate has serious implications on program length and student retention. Numerous studies have been conducted that are aimed at improving these common statistics in Dynamics. These studies provide invaluable guidance on improving teaching techniques to address the diverse needs of learners in and outside of the lecture halls. The focal point of this study is on student assessments and their use to promote content mastery in Engineering Dynamics. Using classroom assessments in highly effective ways to improve student learning is not a new idea. However, they are often used by instructors as tools solely to rank the students rather than for an opportunity to help students learn. Using assessments as sources of information to guide and provide corrective instruction are steps that have been taken at the University of Calgary towards improving student outcomes. To further exploit the ability of assessments to be used to help students learn, the effect of giving students an opportunity to reassess on course outcomes is examined. Although often met with controversy, proponents of second chance exams believe that when done properly, they have a significant positive impact on student learning and retention. This may particularly be the case for engineering dynamics, where students are lost in rigid body dynamics if they have not fully understood the foundational first part of the course, particle dynamics. Over the past few years, the assessments in Engineering Dynamics have consisted of 8 quizzes, a midterm, and a final exam. Student’s comments on the course evaluations have strongly suggested that quizzes are a great opportunity for them to keep up to date with the course material. Due to the heavy load of almost weekly quizzes, of the 8 quizzes, the two on which the lowest marks were obtained were not considered in the calculation of the student’s final grade. Although this is common practice when multiple quizzes are taken in a course, it does not give students the opportunity to learn from their mistakes. This is also true for the case of the midterm, where some students are left with a low mark, and therefore a poor understanding of the foundational material. In order to improve student learning, two significant changes have been implemented in the Fall, 2019 dynamics class. Firstly, students can rewrite any one quiz before the midterm, and any one of the later quizzes before the final exam. Secondly, with constraints, students can rewrite the midterm two weeks after the original date. The details of the assessments, rules and constraints surrounding the reassessments, and a comprehensive evaluation of the effect of the reassessments on student learning outcomes and student experience will be detailed in this work.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it