A Systematic Review on Deep Learning in Education: Concepts, Factors, Models and Measurements

Abstract

Deep learning, a cognitive process involving goal alignment, advanced problem-solving, and rigorous knowledge application, lacks a unified framework despite its wide application in education. This gap poses challenges for researchers, educators, and policymakers. A meticulous review of ten pertinent articles published between 2019 and 2023, aimed to define the concept, identify influencing factors, examine research models, and scrutinize measurement methods. Results show that while "deep learning" is commonly used, a clear, unified definition is absent. Influencing factors are proposed based on diverse theoretical frameworks, including individual and environmental elements. Commonly referenced models are Biggs' 3P model and Bloom's taxonomy. Data collection predominantly employs surveys, interviews, and experiments, with a bias towards quantitative analysis, supplemented by qualitative methods. It's worth mentioning that there is a growing trend in the use of mixed methods. This study provides valuable insights for the effective implementation of empirical deep learning research. It provides practical references bridging theory and practice for education researchers.