To Activate English Learning: Listen and Speak in Real Life Context with an AR Featured U-Learning System

Introduction Technological advancements in wireless applications and the popularity of mobile devices have created great advantages and opportunities for learning (Huang & Chiu, 2015; Hsieh et al., 2016). As a natural complement to ubiquitous computing, AR provides an intuitive and interactive interface to a three-dimensional information space embedded within physical reality. The application of Augmented Reality (AR) with context-aware ubiquitous learning (u-learning) systems offers considerable advantages for English as a foreign language (EFL) learning. EFL learners often confront difficulties and challenges when they need to speak English in real life settings. Contextual learning environments have been proven to improve performance in EFL learning (Sandberg et al., 2011). Context-aware ubiquitous learning (u-learning) refers to the computer systems that seamlessly integrate into our everyday life. It is a version of our future computing lifestyle in which providing services and information occurs anywhere and anytime. Researchers have found positive outcomes in the use of mobile devices for English language learners. Kolb (1984) emphasized learning is constructed and contextual which relies on an engagement with social interactions and experience from the real world (De Freitas & Neumann, 2009). Based on Kolb's arguments, we intend to design and develop a U-Learning Instruction System with Augment Reality Features (UL-IAR) to help users to learn EFL in real life contexts. Our UL-IAR allows users to learn EFL in a constructed and contextual environment that involves social interactions and experience. Despite prior studies have considered the technical affordances of u-learning, there is an ongoing need to examine pedagogies that are suitable for u-learning to inform teacher practice, policy makers, curriculum developers, and teacher education (Pegrum et al., 2013). To implement a context-aware u-learning system for EFL learners, we further design learning strategies for different individuals. We believe that individual difference also leads to different learning performance in using u-learning systems. The individual differentiation can be discerned through cognitive styles, learning styles, cognitive control, and intelligence (Jonassen & Grabowski, 1993). The design of learning systems must consider individuals' cognitive styles which would increase the efficacy and satisfaction of the learning experience. However, the performance outcomes of distinct cognitive styles that applied to new learning technologies remain inconclusive (Dillon & Gabbard, 1998). This knowledge gap highlights the need to examine the difference between cognitive styles in u-learning systems with AR. Therefore, our research objective is to empirically examine the learning performance of u-learning system with different learning strategies and cognitive styles. Overall, this paper intends to answer the following research questions. * What kind of learning strategy leads to better EFL learning performance when using UL-IAR? * Do the users with different cognitive styles have different learning performance when using UL-IAR? * Is there best alignment between learning strategies and cognitive styles when using UL-IAR? This paper is organized as follows. We first review the definitions and studies of u-learning, augmented reality, and cognitive styles. We then show our research method which includes system design and development, experimental design and experiment participants. Last, we discuss our research results and findings and conclude theoretical and practical findings, limitations, and future research. Literature review This section reviews the definitions and studies of ubiquitous learning, the applications of augmented reality in education, and cognitive styles. Ubiquitous learning Ubiquitous learning (u-learning) refers to a ubiquitous learning environment that enables individuals to conduct learning activities in the right place and right time. …