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Shujen L. Chang

Bio: Shujen L. Chang is an academic researcher from University of Houston–Clear Lake. The author has contributed to research in topics: Educational technology & Mainland China. The author has an hindex of 2, co-authored 2 publications receiving 675 citations.

Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors investigated the predictions of sleep quality, personality (social inhibition/negative affectivity), and cognitive load (content/computer) toward second language writing (SLW) anxiety and achievement in a computer-based test.
Abstract: Sleep quality, personality, and cognitive load potentially increase second language writing (SLW) anxiety and subsequently affect SLW achievement. This study investigates the predictions of sleep quality, personality (social inhibition/ negative affectivity), and cognitive load (content/ computer) toward SLW anxiety and achievement in a computer-based test. Participants included 172 voluntary undergraduates majoring in English as foreign language. SLW anxiety in a computer-based test, sleep disturbance, personality and cognitive load was assessed with the SLW Anxiety Inventory, Pittsburg Sleep Quality Index, Type-D Personality, and cognitive load questionnaires. A structural equation modeling approach was applied to examine the interdependence among the observed variables. An adequate-fit SLW anxiety model was built (X2 = 6.37, df = 6, p = 0.383, NFI = 0.97, CFI = 1.00, RMSEA = 0.02; R-squared multiple correlations: SLW anxiety in a computer-based test = 0.19, computer-based SLW achievement = 0.07). The structural model showed that sleep disturbance (+0.17), social inhibition personality (+0.31), and computer-induced cognitive load (+0.16) were significant predictors of SLW anxiety in a computer-based test. Subsequently, SLW anxiety in a computer-based test (−0.16) and computer-induced cognitive load (−0.16) were significant negative predictors of computer-based SLW achievement.

Cited by
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Journal Article
TL;DR: In this paper, the authors present an approach for teaching distance learning in Instructional Technology and Distance Learning (ITDL) courses, based on the International Journal of Instructional technology and distance learning (IITDL).
Abstract: International Journal of Instructional Technology and Distance Learning (ITDL), January 2005

4,035 citations

Journal ArticleDOI
TL;DR: The current trends of empirical research in the development of computational thinking through programming is presented and a constructionism-based problem-solving learning environment could be designed to foster computational practices and computational perspectives and suggests possible research and instructional implications.

852 citations

Book ChapterDOI
01 Jan 2014
TL;DR: Research findings about AR in formal and informal learning environments are summarized, with an emphasis on the affordances and limitations associated with AR as it relates to teaching, learning, and instructional design.
Abstract: This literature review focuses on augmented realities (AR) for learning that utilize mobile, context-aware technologies (e.g., smartphones, tablets), which enable participants to interact with digital information embedded within the physical environment. We summarize research findings about AR in formal and informal learning environments (i.e., schools, universities, museums, parks, zoos, etc.), with an emphasis on the affordances and limitations associated with AR as it relates to teaching, learning, and instructional design. As a cognitive tool and pedagogical approach, AR is primarily aligned with situated and constructivist learning theory, as it positions the learner within a real-world physical and social context while guiding, scaffolding and facilitating participatory and metacognitive learning processes such as authentic inquiry, active observation, peer coaching, reciprocal teaching and legitimate peripheral participation with multiple modes of representation.

470 citations

Book
09 Apr 2008
TL;DR: Table of Contents Chapter One: Social Networking as an Educational Tool Chapter Two: Design for a Distributed Environment Chapter Three: Selecting the Media Palette Chapter Four: The Tools in Practice Chapter Five: Constraints on Course Design Chapter Six: Evaluating Course Design and Understanding its Implications References Index
Abstract: @contects:Table of Contents Chapter One: Social Networking as an Educational Tool Chapter Two: Design for a Distributed Environment Chapter Three: Selecting the Media Palette Chapter Four: The Tools in Practice Chapter Five: Constraints on Course Design Chapter Six: Evaluating Course Design and Understanding its Implications References Index

437 citations

Book Chapter
01 Mar 2009
TL;DR: The Framework for the Rational Analysis of Mobile Education (FRAME) model describes mobile learning as a process resulting from the convergence of mobile technologies, human learning capacities, and social interaction and addresses contemporary pedagogical issues of information overload, knowledge navigation, and collaboration in learning.
Abstract: The Framework for the Rational Analysis of Mobile Education (FRAME) model describes mobile learning as a process resulting from the convergence of mobile technologies, human learning capacities, and social interaction. It addresses contemporary pedagogical issues of information overload, knowledge navigation, and collaboration in learning. This model is useful for guiding the development of future mobile devices, the development of learning materials, and the design of teaching and learning strategies for mobile education. Introduction Research in the fi eld of mobile learning is on the rise. Visionaries believe mobile learning offers learners greater access to relevant information, reduced cognitive load, and increased access to other people and systems. It may be argued that wireless, networked mobile devices can help shape culturally sensitive learning experiences and the means to cope with the growing amount 066897_Book.indb 25 3/10/09 9:02:46 AM 26 Marguerite L. Koole of information in the world. Consider, for a moment, an individual who is learning English. There is a myriad of available resources on grammar, vocabulary, and idioms; some resources are accurate and useful; others less so. Equipped with a mobile device, the learner can choose to consult a web page, access audio or video tutorials, send a query via text message to a friend, or phone an expert for practice or guidance. She may use one or several of these techniques. But, how can such a learner take full advantage of the mobile experience? How can practitioners design materials and activities appropriate for mobile access? How can mobile learning be effectively implemented in both formal and informal learning? The Framework for the Rational Analysis of Mobile Education (FRAME) model offers some insights into these issues. The FRAME model takes into consideration the technical characteristics of mobile devices as well as social and personal aspects of learning (Koole 2006). This model refers to concepts similar to those as found in psychological theories such as Activity Theory (Kaptelinin and Nardi 2006) – especially pertaining to Vygotsky’s (1978) work on mediation and the zone of proximal development. However, the FRAME model highlights the role of technology beyond simply an artefact of “cultural-historic” development. In this model, the mobile device is an active component in equal footing to learning and social processes. This model also places more emphasis on constructivism: the word rational refers to the “belief that reason is the primary source of knowledge and that reality is constructed rather than discovered” (Smith and Ragan 1999, 15). The FRAME model describes a mode of learning in which learners may move within different physical and virtual locations and thereby participate and interact with other people, information, or systems – anywhere, anytime. The FRAME Model In the FRAME model, mobile learning experiences are viewed as existing within a context of information. Collectively and individually, learners consume and create information. The interaction with information is mediated through technology. It is through the complexities of this kind of interaction that information becomes meaningful and useful. Within this context of information, the FRAME model is represented by a Venn diagram in which three aspects intersect (Figure 1). 2 2. The nomenclature used in the Venn diagram has been altered from previous publications. Previously the device aspect was called the device usability aspect, the device usability intersection was called the learner context intersection, and the social technology intersection was called the social computing intersection. 066897_Book.indb 26 3/10/09 9:02:47 AM A Model for Framing Mobile Learning 27 The three circles represent the device (D), learner (L), and social (S) aspects. The intersections where two circles overlap contain attributes that belong to both aspects. The attributes of the device usability (DL) and social technology (DS) intersections describe the affordances of mobile technology (Norman 1999). The intersection labelled interaction learning (LS) contains instructional and learning theories with an emphasis on social constructivism. All three aspects overlap at the primary intersection (DLS) in the centre of the Venn diagram. Hypothetically, the primary intersection, a convergence of all three aspects, defi nes an ideal mobile learning situation. By assessing the degree to which all the areas of the FRAME model are utilized within a mobile learning situation, practitioners may use the model to design more effective mobile learning experiences. (DLS) Mobile Learning (DS) Social Technology (LS) Interaction Learning (D) Device Aspect (DL) Device Usability (L) Learner Aspect (S) Social Aspect Information Context FIGURE 1 The FRAME Model 066897_Book.indb 27 3/10/09 9:02:47 AM 28 Marguerite L. Koole Aspects Device Aspect (D) The device aspect (D) refers to the physical, technical, and functional characteristics of a mobile device (Table 1). The physical characteristics include input and output capabilities as well as processes internal to the machine such as storage capabilities, power, processor speed, compatibility, and expandability. These characteristics result from the hardware and software design of the devices and have a signifi cant impact on the physical and psychological comfort levels of the users. It is important to assess these characteristics because mobile learning devices provide the interface between the mobile learner and the learning task(s) as described later in the device usability intersection (DL). TABLE 1 The Device Aspect Criteria Examples & Concepts Comments Physical Characteristics Size, weight, composition, placement of buttons and keys, right/left handed requirements, one or two-hand operability1. Affects how the user can manipulate the device and move around while using the device. Input Capabilities Keyboard, mouse, light pen, pen/stylus, touch screen, trackball, joystick, touchpad, hand/foot control, voice recognition1. Allows selection and positioning of objects or data on the device1. Mobile devices are often criticized for inadequate input mechanisms. Output Capabilities Monitors, speakers or any other visual, auditory, and tactile output mechanisms. Allows the human body to sense changes in the device; allows the user to interact with the device. Mobile devices are often criticized for limitations in output mechanisms such as small

394 citations