Recognizing and measuring self-regulated learning in a mobile learning environment

Abstract

With the realization that more research is needed to explore external factors (e.g., pedagogy, parental involvement in the context of K-12 learning) and internal factors (e.g., prior knowledge, motivation) underlying student-centered mobile learning, the present study conceptually and empirically explores how the theories and methodologies of self-regulated learning (SRL) can help us analyze and understand the processes of mobile learning. The empirical data collected from two elementary science classes in Singapore indicates that the analytical SRL model of mobile learning proposed in this study can illuminate the relationships between three aspects of mobile learning: students’ self-reports of psychological processes, patterns of online learning behavior in the mobile learning environment (MLE), and learning achievement. Statistical analyses produce three main findings. First, student motivation in this case can account for whether and to what degree the students can actively engage in mobile learning activities metacognitively, motivationally, and behaviorally. Second, the effect of students’ self-reported motivation on their learning achievement is mediated by their behavioral engagement in a pre-designed activity in the MLE. Third, students’ perception of parental autonomy support is not only associated with their motivation in school learning, but also associated with their actual behaviors in self-regulating their learning.

Introduction

Mobile technologies forge ubiquitous learning spaces and experiences across different scenarios or contexts (Sharples et al., 2005, Sharples et al., 2007). They have led us into a new phase in the evolution of technology-enhanced learning (TEL). Ubiquity, the most significant feature of wireless and mobile technologies, creates possibilities for learners to learn the right thing at the right time at the right place (Peng, Su, Chou, & Tsai, 2009). In their words, ubiquity “refers not to the idea of ‘anytime, anywhere’ but to ‘widespread’, ‘just-in-time’, and ‘when-needed’ computing power for learners” (p. 175). It is increasingly believed that this feature enables educators to facilitate and scaffold student-centered learning activities that encompass both formal and informal settings (Frohberg et al., 2009, Looi et al., 2011, Zhang et al., 2010).

Although mobile learning is becoming popular in educational research and practices, there is under-theorization about the nature, process, and outcome of mobile learning (Sharples et al., 2005, Sharples et al., 2007, Wali et al., 2008). This is one of the main challenges facing mobile learning research. Many existing studies (Liaw et al., 2010, Wali et al., 2008, Waycott et al., 2005, Zurita and Nussbaum, 2007) grounded and conceptualized the application of mobile technologies to learning in the framework of activity theory. Although those studies attached importance to the role of the learner in effective mobile learning, further research is still needed to unpack the roles of learner characteristics such as motivation in understanding and analyzing the mechanisms and processes of mobile learning. Specifically, more research is needed to explore external factors (e.g., pedagogy, parental involvement in the context of K-12 learning) and internal factors (e.g., prior knowledge, learning goals, and strategies) that underlie student-centered mobile learning. Student-centered learning logically assumes that students are the agents (masters) of their own learning in some manner. Mobile learning environments provide a means by which students can exercise agency to manage their own learning. This assumes that handheld computers can not only be used as cognitive tools (Chen, Tan, Looi, Zhang, & Seow, 2008), but also as metacognitive tools.

The intent of the present study is to exemplify how the theories and methodology of self-regulated learning (SRL), an active area in contemporary educational psychology (cf. Zimmerman and Schunk, 2001a, Zimmerman and Schunk, 2011), can help to explicate and understand the mechanism and processes of mobile learning. Approaching this aim will lead to significant implications for addressing these challenges in the theorizing of mobile learning.

From a broader perspective, Azevedo (2005) explored how self-regulated learning can be used as a guiding theoretical framework to examine learning with advanced computer technologies that presumably include mobile technologies. According to Azevedo, our knowledge of the mechanisms underlying students’ learning with technologies is lacking when compared with the technological advances which have made such technologies prevalent in homes, school, and at work. Thus, more research is needed to use multiple theoretical lens and multiple methods for better understanding the complex nature of learning with technology-advanced learning environments, in particular, mobile learning environments. These views are aligned with and support the current attempt to investigate mobile learning processes from the perspective of SRL.

In the following sections, the notion of self-regulated learning (SRL), its main components, and a phase model of SRL will be briefly reviewed. Based on this, an analytic SRL model of mobile learning for theoretically interpreting and analyzing mobile learning processes will be introduced. This model will be empirically substantiated by the measurement and statistical analyses of students’ cognitive, metacognitive, and motivational processes in the context of a mobile learning intervention involving two elementary science classes in Singapore. Finally, the empirical findings, and theoretical and practical inferences for further research in both mobile learning and SRL will be interpreted and discussed.