Using Assessments to Improve Student Outcomes in Engineering Dynamics
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,058 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».