From active‐in‐behaviour to active‐in‐thinking in learning with technology

Technology has played an increasingly important role in supporting student-centred learning through ubiquitous access to diverse learning contents, immersive interaction with simulated real-world problems and authentic tasks and extensive communication with peers throughout the learning process. With the support of technology, students become active in searching and processing digital contents, performing various problem solving and learning tasks and communicating and collaborating with others. However, while students are often active in technology-mediated learning activities, they are not always directly afforded to be active in thinking or developing meaningful understanding from the learning contents, learning tasks and social communications. Technology-enhanced student-centred learning emphasizes the role of the student in constructing personally relevant meaning (Hannafin & Land, 1997). Nevertheless, without sufficient effort and support, students tend to be more behaviourally than cognitively active in learning with technology (Brookfield, 2009), which may lead to undesirable learning outcomes. Students active in navigating a wide range of online learning resources might be inactive or have difficulties in connecting discrete pieces of knowledge into a coherent whole (Wang, Peng, Cheng, Zhou, & Liu, 2011), which is crucial to meaningful learning (Ausubel, 1963). Students active in online communications might not engage in critical and creative thinking for joint construction of knowledge and new perspectives (Wegerif, 2013). Students engaged in interactions with real-world problems in virtual environments may be inactive or encounter difficulties in making scientific reasoning and systematic analysis (Chen, Wang, Grotzer, & Dede, 2018; Wang, Kirschner, & Bridges, 2016). Effective learning depends not only on active interaction with learning contents, peers and tasks, but more on active thinking in making meaning of the contents and experience and solving real-world problems. The latter concerns mental states and processes (eg, argument and inference), thinking dispositions (eg, open-mindedness and reflectiveness) and more importantly, the application of relevant norms and strategies for effective thinking (McGuinness, 2005; Mercer, 2013; Sun, Wang, & Wegerif, 2019; Tishman & Perkins, 1997; Wegerif, 2013). Technology has a potential to engage students in active thinking and facilitate cognitive processes in complex learning situations, which, however, is not adequately explored in the literature. This special section provides a platform for researchers to present work that offers insights into how active-in-thinking can be strengthened in learning with technology, which is critical to student-centred learning in technology-mediated environments. Inquiry-based learning is a form of active learning. It is also fundamental for the development of higher order thinking skills. In the first article, Dickes, Kamarainen, Metcalf, Gün-Yildiz, Brennan, Grotzer and Dede (2019) from Harvard University present a design framework for incorporating computational modelling and programming tools into blending immersive virtual environments to support the active thinking of primary students in scientific inquiry. The second article by Hsiao, Lee and Klopfer (2019) from MIT demonstrates how teachers can use StarLogo Nova, an agent-based modelling environment to advance mechanistic reasoning among secondary students in explaining complex phenomena. Creative thinking has been more advocated than accomplished in school education. In the third article, Sun, Wang and Wegerif (2019) from the University of Hong Kong and the University of Cambridge propose a computer-based cognitive mapping approach as a divergent thinking tool that can help students to capture the complex process of applying divergent thinking strategies. The approach has shown promising effects on improving high school students' divergent thinking performance when working with creativity-related tasks. Learning through problem solving is widely promoted in educational practice. However, problem solving involves complex cognitive processes that are inaccessible to many learners. In the fourth article, Reilly, Kang, Grotzer, Joyal and Oriol (2019) from Harvard University investigate how the teacher's use of thinking scaffolds can support high school students' thinking and reasoning in a technology-mediated problem-based learning context. The fifth article by Cheng, Hwang and Chen (2019) from National Taiwan University of Science and Technology presents a mobile technology-supported experiential learning system that may help primary students to engage in active thinking and reflection so as to improve problem-solving performance and knowledge achievements. Design thinking, closely related to problem solving, is widely promoted in education. In the sixth article, Wu, Hu and Wang (2019) from East China Normal University and the University of Hong Kong investigate the effects of static scaffolding versus adaptive scaffolding in supporting preservice teachers' development of design-thinking competence in collaborative STEM learning design practice. The seventh article by Chen, Hwang and Chang (2019) from National Taiwan University of Science and Technology examines the effects of promoting reflective thinking on improving graduate students' engagement and project performance in flipped learning with a design project. Concept maps have been widely used to promote high-order thinking. In the eighth article, Cui and Yu (2019) from Beijing Normal University investigate the effects of concept map versus knowledge graph on fostering in-depth thinking and learning among primary students in a flipped classroom. The ninth article presents a related study, where Cai and Gu (2019) from Jiangnan University and East China Normal University explore the effects of diagram-based representation versus text-based representation on improving individual understanding and group discussion among graduate students in a collaborative learning context. It is important to analyse students' thinking so as to improve their thinking and learning performance. In the tenth article, Swiecki, Ruis, Gautam, Rus and Williamson Shaffer (2019) from the University of Wisconsin–Madison and the University of Memphis present an approach to incorporating teacher-created rubrics into the generation of student thinking models by the computer on the basis of student dialogue records. Finally, Hooshyar, Kori, Pedaste and Bardone (2019) from the University of Tartu in Estonia discuss the role of open learner models in promoting active thinking in self-regulated online learning in higher education contexts by a review of relevant literature. This special section brings together a range of approaches to understanding how we can expand from active-in-behaviour to active-in-thinking when learning with technology. Each of them offers an innovative use of learning technologies and innovative design of technology-enabled learning environment and activities. The breadth of the research and international scope is distinctive. We hope this special section will foster further interest in what we believe is an area of great importance.