Assessing the programming efficacy of teachers through workshop learning combining drones and STEM activities
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".