Lessons Learned from Using Competency Based Assessment (CBA) in a First Year Engineering Statics Course
Notice bibliographique
Résumé
During the Fall term of 2020, the first year Statics course in the College of Engineering at theUniversity of Saskatchewan was taught remotely, and synchronously, using a competency-based assessment (CBA) implementation. CBA is commonly used in other professions, especially medical education and teacher education, but it has yet to see widespread use in engineering education.CBA can involve a number of key differences from conventional assessment practices. In this pilot, it involved the following key differences. Whereas previous versions of the course had involved assignments, labs, a midterm, and a final exam, each worth a certain weight in the overallcourse grade, the 2020 CBA version was broken into three modules, each with a “module test”. The module tests were superficially similar to a midterm, and there was no cumulative final exam. Open book assignments, quizzes, labs, and module tests consisted of questions and exercises that addressed a variety of learning outcomes (LOs) within the modules. The LOs were assigned weights in the overall course grade, as opposed to assigning weights to assessments themselves as in a conventional assessment system. Students could therefore overcome poor performances in early assessments of LOs, as better later results on the same LOs would replace the earlier results.A key feature of this approach was that students had at least two and typically three or more opportunities to exhibit competence with respect to the course’s LOs. Another key aspect of this CBA implementation was the division of course material into three levels or “types”. Type A materials were the most basic building blocks of the course i.e. basic definitions, calculations, and concepts. Type B materials were basic integrative problems e.g. solving a basic 2D or 3D particle or rigid body equilibrium question, or solving a basic truss. Type C materials were advanced or “tricky” integrative problems that probed deeper understanding and required more adaptive problem solving. Students were required to meet competency thresholds for Type A and B materials i.e. theyneeded to exhibit a minimal level of competency in the LOs in order to pass the course.Overall, the class excelled in this assessment format and anecdotal evidence suggests that the students enjoyed it. A summary description of the complete system will be presented in this paper, including how grades were determined, how assessments were conducted and evaluated, how LOs were determined, and how the three levels of material were arranged. As well, basic statisticalresults from the class’ performance will be presented, along with a number of observations made by the instructors and some anecdotal impressions conveyed to them by students. The observed outcomes will be compared with the CBA literature for related STEM contexts, although the remote learning/COVID context did obscure the causes and origins of some of the observed outcomes. Changes that will be made in next year’s implementation of the course will also be discussed.
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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 | 0,016 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,005 | 0,006 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,007 |
| 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 ».