Research on self-determination theory emphasizes the importance of the internalization of motivation as a crucial factor for determining the quality of motivation. Hence, intrinsic motivation is deemed as an important predictor of learning. Research on epistemic beliefs, on the other hand, focuses on the nature of knowledge, and learning with more sophisticated epistemic beliefs associated with more adaptive outcomes. While learning and achievement are multiply determined, a more comprehensive theoretical model that takes into account both motivational quality and epistemic beliefs (...) is needed. Hence, this study aims to examine the role of intrinsic and instrumental motivation alongside epistemic beliefs in predicting students’ achievement in science. Data were drawn from the PISA 2015 survey. We focused on four of the top-performing societies. Two were Eastern societies – Singapore and Hong Kong, and the other two were Western societies: Canada and Finland. We found both common and specific patterns among the four societies. Regarding the common patterns, we found that intrinsic motivation and epistemic beliefs had direct positive effects on science achievement. As for the regionally-specific findings, instrumental motivation positively predicted achievement only in Western societies, but not in Eastern societies. The interaction effect between motivation and epistemic beliefs also demonstrated different patterns across the four societies. Implications for the role of motivation and epistemic beliefs in optimizing student learning and achievement are discussed. (shrink)

This study provided a content analysis of studies aiming to disclose how artificial intelligence has been applied to the education sector and explore the potential research trends and challenges of AI in education. A total of 100 papers including 63 empirical papers and 37 analytic papers were selected from the education and educational research category of Social Sciences Citation Index database from 2010 to 2020. The content analysis showed that the research questions could be classified into development layer, application layer, (...) and integration layer. Moreover, four research trends, including Internet of Things, swarm intelligence, deep learning, and neuroscience, as well as an assessment of AI in education, were suggested for further investigation. However, we also proposed the challenges in education may be caused by AI with regard to inappropriate use of AI techniques, changing roles of teachers and students, as well as social and ethical issues. The results provide insights into an overview of the AI used for education domain, which helps to strengthen the theoretical foundation of AI in education and provides a promising channel for educators and AI engineers to carry out further collaborative research. (shrink)