Latent class analysis of 651 Filipino students identifies four AI dependency profiles, with the most dependent group showing the weakest academic competencies in critical thinking, writing, research, and engagement.
1974 A New Look at the Statistical Model Identifi cation
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WSINDYc-MPC identifies governing dynamics more robustly than benchmarks under high noise, enabling longer prediction horizons and lower tracking errors in fusion, drone, chaos, and aircraft control tasks.
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Profiles of AI Dependency: A Latent Class Analysis of Filipino Students' Academic Competencies
Latent class analysis of 651 Filipino students identifies four AI dependency profiles, with the most dependent group showing the weakest academic competencies in critical thinking, writing, research, and engagement.
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WSINDy for Model Predictive Control with Applications to Fusion, Drones, and Chaos
WSINDYc-MPC identifies governing dynamics more robustly than benchmarks under high noise, enabling longer prediction horizons and lower tracking errors in fusion, drone, chaos, and aircraft control tasks.