Assessing the programming efficacy of teachers through workshop learning combining drones and STEM activities
Notice bibliographique
Résumé
This program focused on work with the DJI (2023b) Tello EDU drone, which is programmable through an app or can be flown with an app or a controller. The DroneBlocks App (DroneBlocks, n.d.) was used for flying through drag-and-drop, block coding, and the DJI (2023a) Tello App was used for flying without programming. Each teacher self-evaluated knowledge and skills, before and after a multi-day workshop. Balogun and Miller (2022) developed, and pilot-tested, a drone club model for out-of-school STEM learning and career pathway exploration. K-12 educators and subject-matter experts provided feedback for revision. Feedback topics ranged from safety to instruction to assessment. Goodnough et al. (2019) collected data regarding teacher pedagogical content knowledge while presenting a unit using drones to study animal habitats. Teacher efficacy was strengthened as they created inquiry-based, classroom environments to engage learners in science. Tsai et al. (2019) developed a computer programming self-efficacy scale. The five subscales included Logical Thinking, Cooperation, Algorithm, Control, and Debug. During summer 2022 and spring 2023, 16 teachers provided survey data for the self-efficacy scale (Tsai et al., 2019) and responded to open-ended questions. The goal was to provide high-quality, teacher professional development to increase knowledge and instructional skills for integrating drones into the elementary, middle, and secondary grades classroom. Measurable objectives included: 1. There will be a statistically significant increase in teachers’ scores on a 16-item, computer programming self-efficacy survey, between administrations of the instrument. 2. There will be a statistically significant increase in teachers’ scores on the five sub-scales of the computer programming self-efficacy survey, between administrations of the instrument. 3. Responses to open-ended questions will be analyzed for trends. Results showed a significant increase in computer programming self-efficacy and significant increases in sub-scale scores. References Balogun, A. O., & Miller, J. (2022). Drone club: Exploring engineering and employability skills outside the classroom. TechTrends, 66, 923-930. DJI. (2023a). Download center. Retrieved February 21, 2023, from https://www.dji.com/downloads/djiapp/tello DJI. (2023b). Tello EDU. Retrieved February 21, 2023, from https://m.dji.com/product/tello-edu DroneBlocks. (n.d.). Download the DroneBlocks Apps. Retrieved February 21, 2023, from https://droneblocks.io/app Goodnough, K., Azam, S., & Wells, P. (2019). Adopting drone technology in STEM (science, technology, engineering, and mathematics): An examination of elementary teachers’ pedagogical content knowledge. Canadian Journal of Science, Mathematics and Technology Education, 19, 398-414. Tsai, M.-J., Wang, C.-Y., & Hsu, P.-F. (2019). Developing the computer programming self-efficacy scale for computer literacy education. Journal of Educational Computing Research, 56(8), 1345-1360.
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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 | 0,001 | 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 | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».