Neuroscience Perspectives for Science and Mathematics Learning in Technology-Enhanced Learning Environments

Abstract

Recently, neuroscience perspectives on human learning have drawn increasing interest among researchers in education (Schrag, 2011). Many researchers, particularly in science and mathematics education, have highlighted the utility of integrating a neuroscience or cognitive–science perspective into science andmathematics learning (e.g. Anderson, 1983, 1997, 2009; Anderson & Contino, 2013; Duncan & Rivet, 2013; Kwon & Lawson, 2000; Lawson, 1986, 2003, 2004; Longo, Anderson & Wicht, 2002). At the same time, another mainstream of productive research has focused on using technology to enhance science and mathematics learning based on research studies and evidence-based improvement of classroom practices derived from the research. In these approaches, multimedia representations such as text, graphic, video, animation, and simulation are usually designed to include concrete cognitive representations, or improved visualizations of abstract and conceptual ideas, as a way of enhancing learning (Chang & Linn, 2013). Recently, with the rapid development of information technology, some highly innovative approaches have been developed. These include complex digital learning environments and other interactive media, such as web-based learning, mobile learning, game-based learning, and computer-supported collaborative learning. These newer innovations have been critically analyzed and examined in research studies on science and mathematics learning (e.g. Lee, Tsai, Wu, Tsai, Liu, Huang, Lai et al., 2011). Although researchers in different domains have committed substantial efforts in designing innovative learning environments and examining their impacts on students’ learning, many challenges remain, such as fully understanding how students perceive and process a variety of representations embedded in different complex learning environments. Neuroscience methodologies have been recently linked to research in the educational technology field. For example, eye movement methodology has been applied to multimedia learning and the results have been discussed and summarized in some published special issues (e.g. Van Gog & Scheiter, 2010). Lai, Tsai, Yang, Hsu, Liu, Lee, Lee et al. (2013) also reviewed eye-tracking studies related to learning and pointed out