Fundamental: A Teacher Professional Development Program in Engineering Research with Entrepreneurship and Industry Experiences
Notice bibliographique
Résumé
Abstract Today, technology is pervasive and it is reshaping every aspect of our lived experience. Unfortunately, similar to the vast majority of adults in our society, K-12 students generally lack an understanding of the engineering foundation of the tech-gadgets that have become an integral part of their lifestyles. As technology undergoes accelerating and converging advances, it is paramount that K-12 students receive high quality STEM education so that they have the potential to join the future engineering workforce as contributors to our innovation-driven economy. In recent years, hands-on, problem- and project-based learning has been gathering momentum in K-12 education. Such an approach can benefit students in gaining a greater understanding of subject material and allow teachers to support students of diverse learning styles. Even as hands-on STEM learning aims to incorporate real-world applications, many implementations of learning activities and practices are restricted to the classroom environment and constrained by the scale and resource availability. Teachers and students seldom get opportunities to explore authentic, real-world challenges, primarily due to the lack of teacher knowledge and experiences with modern technologies. These dynamics continue to reinforce students’ misperception that science and math are activities that students do in the classroom, disconnected from the real-world. Thus, to develop a technically literate workforce, educators must not only teach STEM knowledge but also address the students’ question, “Why do I need to know this?” Guided by the internship efficacy research, we posit that through exposure to hands-on engineering and industry experiences, teachers can become better equipped to inform students about how classroom science and math connects to real-world career opportunities. This paper documents activities and outcomes of a teacher professional development (PD) program, which allows participants to have authentic experiences in engineering, technology, entrepreneurship, and industry. An engineering department at a higher education institute hosted nine teachers for a six-weeklong summer PD, beginning with a two-week hands-on, structured learning followed by a four-week collaborative research and periodic industry interaction experiences. During the structured learning, teachers performed numerous hands-on experimental activities to learn and understand scientific and mathematical foundations of mechatronics and robotics. Moreover, through experiential activities, including visit to a technology startup incubator, they learned fundamental concepts of entrepreneurship, such as business model canvas, minimum viable product, intellectual property, raising funding, etc. In the four-week research phase, to experience the process and challenge of conducting engineering research, the teachers worked in teams to collaborate in and contribute to ongoing projects involving graduate researchers, undergraduate and high-school students, and faculty mentors. Moreover, talks from industry professionals, including a two-day workshop by an industry partner, and visits to startup and factory sites provided ample industry exposure, giving teachers a unique opportunity to develop their innovation, entrepreneurship, and networking skills as well as to gain a real-world understanding of engineering workplace and careers. Teachers created lesson plans to share educational, technical, entrepreneurial, and industry aspects of their summer experience, to provide a foundation for college-level education to their students, and better inform their students about engineering career opportunities. Through follow-up sessions over the academic year, teachers continue to receive additional support for implementing authentic, engineering-based lesson plans. The participants made significant contributions to research projects in four engineering labs. Illustrative research includes the design of a wirelessly controlled robot to study marine environments, development of an affordable game-based telerehabilitation solution for stroke victims, etc. Based on pre- and post- technical quizzes conducted during the PD, the teacher’s understanding of scientific and mathematical foundations of the concepts improved from 49% to 64%. Final submission of this paper will provide a detailed overview of PD curriculum, activities, research projects, and teacher outcomes (e.g., technical quiz, self-efficacy, and external evaluation).
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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 | 0,008 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,003 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,002 | 0,013 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,034 | 0,011 |
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 ».