Notice bibliographique
Résumé
To ensure that the graduates of engineering schools in Canada have appropriate background in engineering design, the Canadian Engineering Accreditation Board (CEAB) [2] specifies that every engineering program should provide students with a significant design experience based on the knowledge and skills acquired in earlier course work, and give students an exposure to the concepts of team work and project management. Typically, these “capstone design” courses are inquiry-based learning [3] courses that involve considerable hands-on project work by student teams. As well, many capstone design courses also include classroom instruction on design methodology, design theory, and project management. The open-ended nature of the capstone design course sets it apart from other courses in the engineering curriculum, but also presents a number of challenges to teaching faculty, the foremost being assessment. When designing an assessment tool for any course, it is important to match the assessment tool with the learning objectives for the assessment [1, 5]. In this way, the instructor can determine the extent to which students have achieved the learning objective, and ideally, the learning objective can be reinforced for the student (e.g., via feedback within the assessment). Given the nature of capstone design courses however, the design of student assessments can be a challenge. In particular, capstone design course instructors face the challenge of coordinating multiple student projects: each of which involves a separate student group and faculty “project advisor” or “coach”. The instructor must develop assessment tools that facilitate consistent assessment across multiple projects, multiple teams, and multiple assessors. Additionally, these tools must be appropriate for the typical range of student deliverables for capstone design courses: e.g., reports, presentations, reviews, written assignments, and/or prototypes. Given these challenges one might ask, why assess project work at all? For example, in most capstone design courses the “project advisor” or “coach” and in some cases the project sponsor or “customer” serve as the examiners. Each of these individuals typically has intimate knowledge of the project and the team’s performance and could make a good argument that the team’s result is a foregone conclusion. As Powell notes [6], there are a number of substantial challenges to this line of thinking. In particular, it is tricky being facilitator and judge at the same time. To address this, it is common to use a report or series of reports and design reviews for assessment [4]. The key advantage to this approach (over the “foregone conclusion”) is that the student team has the opportunity to defend their decisions and also learn from their mistakes. The challenge to teaching faculty is to create assessment tools that, (1) have clear requirements, (2) are clearly linked to the course objectives, (3) are flexible, and (4) are fair. In this paper we look at the role of classroom assessment in the context of capstone design courses. Examples are provided from the author’s experience teaching a mechanical and manufacturing engineering capstone design course and recommendations are made based on this experience.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,034 | 0,101 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,008 | 0,003 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,008 |
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 ».