Exploring the categories of students’ interest and their relationships with deep learning in technology supported environments

Interest is not only the starting point to begin a wonderful learning journey for students, but also an important driver for deep learning and continuous progress. This study used latent profile analysis (LPA), multiple logistic regression analysis, and multivariate analysis of variance (MANOVA) to analyze the self-reported questionnaires of 634 junior high school students in China, with the aim of exploring the co-existing categories of situational interest and individual interest in technology-supported learning environments, the associated factors, and their impact on the four elements of deep learning (enjoyment of learning, cognitive commitment, relating ideas, understanding). The study found that the co-existing categories of situational interest and individual interest in technology-supported learning environments included "Medium situational interest-Low individual interest group", "Medium situational interest-Medium individual interest group", "High situational interest-Medium individual interest group", "High situational interest-High individual interest group"; grade level was correlated with the deepening and stabilizing phases of interest; all four interest categories were correlated with the four elements of deep learning; and the deepening and stabilizing phases of interest were more correlated with the four elements. The results of the study validate that there is heterogeneity in the effects of situational interest and individual interest on deep learning in technology-supported learning environments, and that "high situational interest-high individual interest" is an important factor in the occurrence of deep learning.