Assessing the Impact of Transitioning Introductory Design Instruction to an Online Environment
Notice bibliographique
Résumé
Abstract In their first academic term (1A), students in the Mechatronics Engineering (MTE) program at the University of Waterloo are required to take 5 courses including programming in C++ (MTE 121 – Digital Computation), and a course which introduces them to the profession (MTE 100 – Mechatronics Engineering). For the last 5 years, these courses have included both a multi-week, integrative, open-ended robotics project with deliverables in both courses, and a 2-day long curricular hackathon dubbed “Tron Days” which further integrates the 1A courses together by having students solve an ill-structured design task. In previous publications on these activities, there has been evidence of growth in student self-efficacy (as described by Bandura) in the skills relating to these projects (viz. programming, design, and teamwork), as well as growth in student epistemological development (as described by Perry). As these courses take place in students’ first academic term, this personal growth is crucial to maintain, even as we transitioned to online instruction for fall 2020. Both courses under study (in both 2019 and 2020) have implemented Felder and Brent’s suggested instructional conditions to facilitate intellectual growth, albeit with different emphases; and so epistemological development is expected in both years. This paper described the adaptations to MTE 100 and MTE 121 to transition them to an online teaching mode, including the revised Tron Days and course project, and investigated what impact, if any, this transition had on student self-efficacy and epistemological development. Since 2013, students in first year MTE have been invited to complete a start of term and end of term survey to capture the impacts of the semester’s instruction on individual students. Statistical analyses of these data showed students’ self-efficacy beliefs in programming and design improved from start to end of term in both 2019 and 2020, and student self-efficacy beliefs in teamwork improved from start to end of term in 2020. Regression analyses predicting end of term self-efficacy beliefs showed there was no statistically significant impact on programming or design beliefs based on year, however year was a statistically significant coefficient for the regression model predicting teamwork self-efficacy with students in 2020 rating higher. Analyses of data on personal epistemology was inconclusive due to poor validity and reliability. Overall, students showed improved self-efficacy beliefs after participating in the remote teaching environment of 2020, similar to the students who were taught in-person in 2019.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».