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SeHee Jung; Hanwen Wang; Bingyi Su; Lu Lu; Liwei Qing; Xiaolei Fang; Xu Xu – TechTrends: Linking Research and Practice to Improve Learning, 2025
This study presents a mobile application (app) that facilitates undergraduate students to learn data science using their own full-body motion data. The app captures a user's movements through the built-in camera of a mobile device and processes the images for data generation using BlazePose, an open-source computer vision model for real-time pose…
Descriptors: Undergraduate Students, Data Science, Handheld Devices, Open Source Technology
Majdi Beseiso – TechTrends: Linking Research and Practice to Improve Learning, 2025
Predicting students' success is crucial in educational settings to improve academic performance and prevent dropouts. This study aimed to improve student performance prediction by combining advanced machine learning (ML) approaches. Convolutional Neural Networks (CNNs) and attention mechanisms were used for extracting relevant features from…
Descriptors: Prediction, Success, Academic Achievement, Artificial Intelligence
Duncan Culbreth; Rebekah Davis; Cigdem Meral; Florence Martin; Weichao Wang; Sejal Foxx – TechTrends: Linking Research and Practice to Improve Learning, 2025
Monitoring applications (MAs) use digital and online tools to collect and track data on student behavior, and they have become increasingly popular among schools. Empirical research on these complex surveillance platforms is scant, and little is known about the efficacy or impact that they have on students. This study used a multi-method…
Descriptors: High School Students, COVID-19, Pandemics, Progress Monitoring