Jiawei Guo’s research while affiliated with Hangzhou Normal University and other places

In technology-enhanced learning (TEL), students’ positive cognitions and affects towards technology play a crucial role in promoting their adoption of deep approach to learning. This study, based on the construct of students’ attitudes toward technology and the Technology Acceptance Model (TAM), aims to explore the relationship between Chinese middle school students’ cognitive and affective component factors of technology and their adoption of deep approach to learning in environments where information technology is deeply integrated into educational teaching. Participants were selected using a convenience sampling method. A total of 645 questionnaires were distributed and 634 valid questionnaires were recovered, with a valid recovery rate of 98.3%. The hypothetical model linking cognitive and affective factors with the deep approach was constructed and validated using structural equation modeling (SEM). The research findings included the following two parts: (1) Technology knowledge as a cognitive factor is a positive predictor of deep approach, while technology readiness does not have a direct predictive effect on deep approach. (2) Interest and perceived importance as affective factors have varying degrees of mediating effects between cognitive component and deep approach. These results suggest that enhancing students’ technology knowledge and increasing students’ positive experiences in technology learning can increase the use of deep approach in technology-enhanced learning environments (TELEs). Teachers should promote students’ technology knowledge in a variety of ways and create environments that promote positive experiences to guide students to actively use technology for deep learning.

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.

Digital transformation of education has become an inevitable trend. In this context, whether and how technology involvement can facilitate students’ effective learning has become the hot issue of concern in the area of educational technology and learning sciences. This study, using 375 middle school students as samples in northwest China, verified that external support had a certain degree of impact on the learning approaches, and internal motivational factors played mediating roles. The results indicates that perceived support directly and positively predicts deep learning. Perceived classroom mastery goal structure is a mediator in perceived support and deep learning. Computer self-efficacy is a mediator in perceived support and learning approaches. These findings are not only instructive for teachers to guide students to utilize information technology (IT) appropriately for learning, but also provide some suggestions on how teaching practices in IT course can facilitate deep learning so as to enhance learning quality when students are using IT.

Peer relationships play important roles in middle-school students’ individual development. Peer support is indispensable in computer-supported learning contexts. This study is designed to explore the connection between perceived peer support and deeper learning, while examining the mediating role of computer self-efficacy and perceived classroom mastery goal structure. 412 middle school students in northwest China were sampled by constructing the structural equation model (SEM) in this study. The results displayed that perceived peer support had no direct positive predictive effect on deeper learning. Computer self-efficacy completely mediated in perceived peer support and deeper learning. Perceived classroom mastery goal structure completely mediated in perceived peer support and deeper learning. Computer self-efficacy and perceived classroom mastery goal structure played a chain mediating effect in perceived peer support and deeper learning. These findings not only deepen our comprehend of the internal mechanism about peer relationships in promoting deeper learning, but also provide constructive suggestions on how to maintain positive peer relationships among students in computer-supported teaching situations, so as to improve students’ digital literacy and skills from the dimension of satisfying their social emotional needs.