Research Paper: Where Do We Meet? Understanding Conference Participation in a Department of Engineering Education
Notice bibliographique
Résumé
Abstract This Lessons Learned Paper focuses on understanding the different types of conferences where graduate students and faculty members in a department of engineering education typically present their work. The field of Engineering Education (ENGE) has been growing considerably in the last years, especially with the creation of several engineering education departments around the country. The Engineering Education community has developed several spaces in the United States(i.e. American Society of Engineering Education (ASEE) annual conference, ASEE regional conferences, Frontiers in Education conference (FIE), etc.) and internationally (The Australasian Association for Engineering Education (AAEE) conference, the Canadian Engineering Education Association (CEEA) conference, the European Society for Engineering Education (SEFI), and the Latin American and Caribbean Consortium of Engineering Institutions (LACCEI), among others), to continuously discuss the trends of the field. Nevertheless, engineering educators come from a very broad range of backgrounds, including people from several engineering disciplines, people from different educational backgrounds, people with different social science backgrounds, and even people with many different industry experiences. Hence, researchers have different roots that directly impact the way they conduct their research and the way they share their work. Many of them will present their work beyond the traditional venues created for engineering education, to better adapt to their disciplinary roots, or to develop connections in different fields required to move their research forward. The purpose of this paper is to better understand what type of conferences members of an engineering education department typically attend. Data is being collected quantitatively using a survey that is being distributed to everyone in a well-established engineering education department (40+ graduate students, 25 faculty members). Results will provide a better understanding of where members of the department share their work and where they attend to develop their academic connections. Results will help us to better understand the field and the diversity of backgrounds it entails. Results also will be especially useful for people new to the engineering education field that will be able to track not only the most traditional and recognized conferences in ENGE, but also become familiar with new venues that might be of interest for them based on their disciplinary background. This lessons learned paper will be presented as a lightning talk, however, it will include a portion of the conversation focused on the audience. To do that, we expect the audience to engage during the talk by using real-time feedback (e.g. Polleverywhere) to gather their preferences on attending conferences and see how those preferences contrast to the findings of our study.
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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 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».