F153-DDL An Investigation of Mobile Learning Readiness and Design Considerations for Higher Education

This study employed the theory of planned behavior as a framework for identifying college students’ current perceptions and needs for mobile learning. The use of mobile devices continues to evolve, and many educators are eager to explore the potential of these devices to enhance student-centered learning by facilitating anytime/anywhere collaboration and communication. Self-reported data from 238 college students was analyzed with a structural equation modeling method. The results confirmed the theory that their attitude, behavioral control and subjective norm positively influenced their acceptance of m-learning, while they perceived that a social environment is not strong enough to implement m-learning. In addition, other findings revealed preferable instructional activities with mobile devices in higher education. Introduction In recent years, we have witnessed an explosion in the growth of mobile devices, such as smart phones (e.g., iPhone) and mobile tablets (e.g., iPad) which use 3G or wireless networks. These devices are altering how we live and how we learn (Abdullah & Siraj, 2010). Mobile learning (m-learning) enables people to access learning anytime and anywhere. These devices are also important for supporting just-in-time, customized, and life-long education. Since college classrooms are filled with students living in a mobile age, institutions in higher education have an opportunity to revitalize the process of teaching and learning via m-learning. However, m-learning is still in its infancy in higher education. Many universities provide a free App (an application for a mobile phone), but it contains mostly non-instructional contents such as news, event calendars or maps. Although m-learning has the potential to augment formal education with flexible access, immediate communication and supplemental learning materials, there are serious concerns about the readiness of college campuses to adopt m-learning (Al-Mushasha, 2010), and there is lack of research exploring the readiness of college environments for m-learning. This study adapted the theory of planned behavior (TPB) to investigate the determinants of college students’ intention to use m-learning. The theory focuses on the formulation of an intention to behave in a particular way, and the sources of the intention are attitude, subjective norm, and behavioral control (Ajzen, 1991). Based on this approach, we proposed new antecedents of attitudinal constructs and draw out conceptual frameworks. Our research questions were: (a) What are the significant salient beliefs of college students that contribute to the levels of attitudinal constructs? (b) How strongly do their attitudinal constructs influence their intention to use m-learning? (c) How do college students want to use a mobile device in their course work? The answers to these questions will allow us to identify the readiness of college students for m-learning which will be a basis for designing effective mlearning environments in higher education. Mobile learning m-learning refers to any sort of learning that happens when the learner is not at a fixed, predetermined location, or learning that happens when the learner takes advantage of learning opportunities offered by mobile technologies (O’Mally et al., 2003). m-learning, in general, could enrich students’ learning experiences with enhanced mobility and connectivity. More specifically, there are five educational benefits based on previous literature: (a) portability, (b) interactivity, (c) context sensitivity, (d) connectivity, and (e) individuality (e.g., BenMoussa, 2003; Churchill & Churchill, 2008; Sharples, 2000) . Previous research has proposed general considerations for m-learning (e.g., Gu, Gu & Laffey, 2011; Liu, Li & Carlsson, 2010; Shih & Mills, 2007). For

[1]  Elena Karahanna,et al.  Time Flies When You're Having Fun: Cognitive Absorption and Beliefs About Information Technology Usage , 2000, MIS Q..

[2]  P. Shah,et al.  Who Are Employees' Social Referents? Using a Network Perspective to Determine Referent Others , 1998 .

[3]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[4]  A. Bandura Social Foundations of Thought and Action: A Social Cognitive Theory , 1985 .

[5]  Peter Lonsdale,et al.  WP 4 - GUIDELINES FOR LEARNING/TEACHING/TUTORING IN A MOBILE ENVIRONMENT , 2003 .

[6]  A. Bandura Self-Efficacy: The Exercise of Control , 1997, Journal of Cognitive Psychotherapy.

[7]  Daniel Churchill,et al.  Educational affordances of PDAs: A study of a teacher's exploration of this technology , 2008, Comput. Educ..

[8]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[9]  Christer Carlsson,et al.  Factors driving the adoption of m-learning: An empirical study , 2010, Comput. Educ..

[10]  Deborah Compeau,et al.  Computer Self-Efficacy: Development of a Measure and Initial Test , 1995, MIS Q..

[11]  M. Conner,et al.  Extending the Theory of Planned Behavior: A Review and Avenues for Further Research , 1998 .

[12]  James M. Laffey,et al.  Designing a mobile system for lifelong learning on the move , 2011, J. Comput. Assist. Learn..

[13]  Gwo-Dong Chen,et al.  An activity-theoretical approach to investigate learners' factors toward e-learning systems , 2007, Comput. Hum. Behav..

[14]  Yuhsun Edward Shih,et al.  Setting the New Standard with Mobile Computing in Online Learning , 2007 .

[15]  I. Ajzen The theory of planned behavior , 1991 .

[16]  Henri Barki,et al.  Explaining the Role of User Participation in Information System Use , 1994 .

[17]  Younghwa Lee,et al.  Investigating factors affecting the adoption of anti-spyware systems , 2005, CACM.

[18]  Shirley Taylor,et al.  Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions , 1995 .

[19]  Icek Ajzen,et al.  From Intentions to Actions: A Theory of Planned Behavior , 1985 .

[20]  Mike Sharples,et al.  The design of personal mobile technologies for lifelong learning , 2000, Comput. Educ..

[21]  Fred D. Davis Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[22]  Marilyn E. Gist,et al.  EFFECTS OF ALTERNATIVE TRAINING METHODS ON SELF-EFFICACY AND PERFORMANCE IN COMPUTER SOFTWARE TRAINING , 1989 .

[23]  Saedah Siraj,et al.  M-Learning Curriculum Design for Secondary School: A Needs Analysis , 2010 .