Blended Learning in an Upper Year Engineering Course: The Relationship between Students’ Program Year, Interactions with Online Material, and Academic Performance
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Notice bibliographique
Résumé
At a comprehensive, public university in Western Canada, a fourth-year course in risk and safety management was recently made a requirement for all engineering students; depending on their program, students may take this course in their second, third, fourth, or fifth year of their program. As a result of increasing class sizes, this course was shifted from traditional to blended instruction. Since blending and opening this course to students with varying years of undergraduate engineering experience, instructors noted a difference in students’ maturity (e.g., a change in quantity and quality of in-class discussion, questions, participation, student-teacher interactions, and problem solving capabilities) and questioned whether this impacted their interactions with online material. Research examining the impact of blended learning in Engineering has primarily focused on large first-year undergraduate courses; research about blended learning in upper-year engineering courses is sparse. Studies investigating courses with students of varying years of experience in the program are virtually non-existent. Therefore, to better understand students’ interactions with online material during blended learning as connected to years in their program, we examined the relationship between levels of interaction and performance of students by year in program. This study analyzed approximately 2000 students’ interactions with online material and performance across five sections of a risk-management course in engineering. We found that students who had completed more years of their program interacted less with online material than students earlier in their undergraduate careers. Academic performance, on the other hand, was higher for students who had interacted more with online material and slightly higher for students who had completed more years in their program. These results suggest that the delivery of instructional materials may need to be tailored to students’ year in their program. Further implications and areas of future study are discussed.
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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 | 0,012 | 0,005 |
| 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 | 0,005 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
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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 découle