Team: 10
School: Taos High
Area of Science: Computer Science/Statistics
Interim: Team Number: 10
School Name: Taos High School
Area of Science: Computer Science
Project Title: Datacasting, an Opportunity in Educational Equity
Problem Definition:
Our project consists of analyzing the consequences of SARS-COV-19 on New Mexican education and the effectiveness of Datacasting within remote learning. Due to economic and residential inequities, many students cannot access broadband internet. Students have an unfair disadvantage when compared to others with unlimited access to broadband. Datacasting enables educational content to be delivered over established television signals.
Problem Solution:
The solution to the problem will be drawn from collected data, training models, and testing against a variety of scenarios. Examples of scenarios that can be tested against include: economic status, access to broadband internet, and access to Datacasting. With these scenarios and educational success metrics, such as SAT/ACT scores, state testing proficiency, graduation rate, we can feed the data into a linear regression model. The mode can later be upgraded to a non-linear model for greater accuracy.
Progress to Date:
Our team has produced visualizations of processes related to Datacasting, but primarily the transmission process. The visualizations are made in Alice and NetLogo. Our team also aided Dr. Gladys for the pilot Datacasting program in Taos county. On January 19th, 2022 we handed out receivers and antennas to the community in order to begin the pilot program. We also researched the data pertaining to educational success metrics from the NMPED and found that no data has been published for the 2020-2021 educational year.
Expected Results:
Due to the lack of data provided by state sources (NMPED, 2021) and the necessary variables stated in our Meet the Scientist interview, we cannot expect a realistic and applicable model. However, theoretical data can be fitted to satisfy the lack of educational success metrics. This theoretical data and TensorFlow regression models (TensorFlow, 2022) can lead to a model capable of “predicting†future data with our theoretical data. The expected results should be the same regardless of data, and should equal a model capable of presenting the educational success of New Mexican students over time and with differing scenarios.
Team Members: Athanasios Bertin, Carlos Miller, Gracie Goler, Lakai Tucker, Lola Shropshire
Mentors: Pedro Escobar-Escoto
Sponsoring Teacher: Tracy Galligan
Works Cited:
Buono, M., MacPherson, K., Martin, J., & Rydout, G. (2021). (rep.). Digital Sovereignty.
Miller, C., Goler, G., Tucker, L., Bertin, A., Shropshire, L., Martin, J., Buono, M., & MacPherson, K. (2021, November 11). Datacasting Introduction. personal.
Basic regression: Predict fuel efficiency. TensorFlow. (n.d.). Retrieved January 25, 2022, from https://www.tensorflow.org/tutorials/keras/regression#split_features_from_labels
NMPED. (2021, November 22). Achievement Data. New Mexico Public Education Department. Retrieved January 25, 2022, from https://webnew.ped.state.nm.us/bureaus/accountability/achievement-data/
Miller, C., Goler, G., Tucker, L., Bertin, A., Shropshire, L., & Robey, T. (2021, November 8). Meet the Scientist. Personal.
Community, N. P. (2022, January 14). NumPy User Manual. NUMFocus Inc.
https://www.youtube.com/watch?v=V59bYfIomVk (Simple linear regression definition)
Team Members:
gracie goler
Lola Shropshire
Carlos Miller
Lakai Tucker
Athanasios Bertin
Sponsoring Teacher: Tracy Galligan