Search

In recent years, flipping classrooms has become a popular topic of discussion. However, few previous studies have focused on the effect of the flow experience on programming self-efficacy. To address this gap, the present researchers developed a model that included six research hypotheses. The study applied a flipped classroom model to a programming course in order to examine the relationship between self-efficacy, flow experience, and learning performance in a flipped programming course. The research model was empirically examined using a survey to collect data from 46 college students in Taiwan. Programming courses were conducted for 180 minutes once a week, with six weeks of total teaching time. The findings of this study indicate that there is a positive relationship between programming self-efficacy and enjoyment, engagement, and flow control. The study also revealed that certain important implications and factors to improve the design and delivery of such courses. [ABSTRACT FROM AUTHOR]

Amidst the rapid advancement in the application of artificial intelligence learning, questions regarding the evaluation of students' learning status and how students without relevant learning foundation on this subject can be trained to familiarize themselves in the field of artificial intelligence are important research topics. This study employed the use of a self-built AI platform (Ladder) for students to systematically learn and apply AI learning model established by the partial least squares (PLS) method to investigate the influence between variables (learning attitudes, self-regulated learning, AI anxiety, individual impact, computational thinking abilities, cognitive styles). This study was particularly conducted in the Department of Computer Science and Information Engineering of a top national university in Southern Taiwan. The valid data were collected from 65 students (55 male students; 10 female students). Furthermore, this study demonstrated the relationship between cognitive style, self-regulated learning and computational thinking. For the first time, it explored the impact of AI anxiety and completed existing research on it. The results of this study show that interest in learning positively affects learning attitudes. In addition, learning attitudes have a positive influence on each individual's performance. Based on multiple theories and the artificial intelligence learning platform, the model proposed in this study effectively understood students' learning status. [ABSTRACT FROM AUTHOR]

Current empirical and theoretical understanding of the relation between age and crime is based almost entirely on data from the United States and a few prototypical Western societies for which age-specific crime information across offense types is available. By using Western databases, Hirschi and Gottfredson (1983) projected that the age distribution of crime is always and everywhere robustly right-skewed (i.e., sharp adolescent peak)-a thesis that is both contested and widely accepted in criminology and social science writings. In the study described here, we tested this age-crime invariance thesis by comparing age-crime patterns in Taiwan (a non-Western Chinese society) with those in the United States. In light of Taiwan's collectivist culture versus the U.S. individualist gestalt, we anticipated more divergence than homogeneity in their age-crime schedules. Our findings show robust divergence in Taiwan's age-crime patterns compared with U.S. patterns and the reverted J-shaped norm projected by Hirschi and Gottfredson. Implications for research and theory on the age-crime relation and for studying human development or life-course topics more broadly are discussed. [ABSTRACT FROM AUTHOR]