A Data-Driven Approach to Engineering Instruction: Exploring Learning Styles, Study Habits, and Machine Learning

A Data-Driven Approach to Engineering Instruction: Exploring Learning Styles, Study Habits, and Machine Learning

This study examined the impact of learning style and study habit alignment on the academic success of engineering students. Over a 16-week semester, 72 students from Process Engineering and Electronic Engineering programs at the Universidad de Los Llanos participated in this study. They completed th...

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Bibliographic Details
Main Authors: Lauren Genith Isaza Dominguez, Antonio Robles-Gomez, Rafael Pastor-Vargas
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Academic performance
engineering instruction
ensemble methods
Felder-Silverman learning styles model (FSLSM)
machine learning (ML)
predictive modeling
Online Access:https://ieeexplore.ieee.org/document/10836232/
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https://ieeexplore.ieee.org/document/10836232/

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