INTERACTIVE PERSUASIVE LEARNING ELEMENTS AMONG ELDERLY: A MEASUREMENT MODEL

The increasing usage of computer technology in myriad fields involves almost all level of the society to interact with the technology. Although the receptions are going well, however, for certain level of ages in the society, the need seems to be difficult to them. Of the level, elderly is a must to be considered for designing and developing computer technology systems that suit them better. Hence, our study proposes a conceptual model of an interactive persuasive learning system to encourage the elderly to use a computer application for learning. This paper is part of our study that evaluates a measurement model of interactive elements of persuasive learning among elderly. This study used empirical study as a method for data collection. Data was collected from 300 elderly respondents and each respondent was supplied with a laptop to enable him/her to use the interactive courseware. The data was analyzed using the Structural Equation Modeling (SEM) with Analysis of Moment Structures (AMOS). The results have shown that the measurement model fits the data. Therefore, the model is suitable for interactive media among elderly. Further, this study intends to identify the relationship between the interactive media features and persuasive learning elements among elderly.

[1]  Michael Welton Black and Blue All Over - Adult Education: Evolution and Achievements in a Developing Field of Study. John M. Peters, Peter Jarvis and Associates (Eds.). 1991. San Francisco: Jossey-Bass. , 1992 .

[2]  Alexander Mikroyannidis,et al.  weSPOT: A Personal and Social Approach to Inquiry-Based Learning , 2013, J. Univers. Comput. Sci..

[3]  Marek R. van de Watering,et al.  The Impact of Computer Technology on the Elderly , 2005 .

[4]  Ioannis Tarnanas,et al.  A virtual reality exposure therapy (VRET) scenario for the reduction of fear of falling and balance rehabilitation training of elder adults with hip fracture history , 2007, 2007 Virtual Rehabilitation.

[5]  Anu Kankainen,et al.  Creative personal projects of the elderly as active engagements with interactive media technology , 2011, C&C '11.

[6]  B. J. Fogg,et al.  Motivating, influencing, and persuading users , 2002 .

[7]  Leon Staphorst,et al.  Structural equation modelling based data fusion for technology forecasting: A generic framework , 2013, 2013 Proceedings of PICMET '13: Technology Management in the IT-Driven Services (PICMET).

[8]  B. J. Fogg,et al.  ! 7 ! MOTIVATING , INFLUENCING , AND PERSUADING USERS , 2010 .

[9]  Karen R. Barnett,et al.  Ageing, Learning, and Computer Technology in Australia , 2007 .

[10]  James C. Anderson,et al.  The Effects of Sampling Error and Model Characteristics on Parameter Estimation for Maximum Likelihood Confirmatory Factor Analysis. , 1985, Multivariate behavioral research.

[11]  H. Oinas-Kukkonen,et al.  Persuasive Features in Web-Based Alcohol and Smoking Interventions: A Systematic Review of the Literature , 2011, Journal of medical Internet research.

[12]  B. J. Fogg,et al.  Persuasive technology: using computers to change what we think and do , 2002, UBIQ.

[13]  Harri Oinas-Kukkonen,et al.  Towards Deeper Understanding of Persuasion in Software and Information Systems , 2008, First International Conference on Advances in Computer-Human Interaction.

[14]  Xianggui Qu,et al.  Multivariate Data Analysis , 2007, Technometrics.

[15]  Mark A. Neerincx,et al.  Persuasive robotic assistant for health self-management of older adults: Design and evaluation of social behaviors , 2010, Int. J. Hum. Comput. Stud..

[16]  Meryl P. Gardner,et al.  Responses to Commercials in Laboratory Versus Natural Settings: a Conceptual Framework , 1983 .

[17]  Gerald Quirchmayr,et al.  An evaluation model for analysing persuasive systems in mobile healthcare , 2014, 2014 International Conference on Computer, Information and Telecommunication Systems (CITS).

[18]  Claire Dormann Designing electronic shops, persuading consumers to buy , 2000, Proceedings of the 26th Euromicro Conference. EUROMICRO 2000. Informatics: Inventing the Future.

[19]  Dorothy E. Leidner,et al.  Research Commentary: Technology-Mediated Learning - A Call for Greater Depth and Breadth of Research , 2001, Inf. Syst. Res..

[20]  H. Marsh,et al.  Application of confirmatory factor analysis to the study of self-concept: First- and higher order factor models and their invariance across groups. , 1985 .

[21]  L. Tucker,et al.  A reliability coefficient for maximum likelihood factor analysis , 1973 .

[22]  Rilla Khaled,et al.  Culturally-Relevant Persuasive Technology , 2008 .

[23]  M. Browne,et al.  Alternative Ways of Assessing Model Fit , 1992 .

[24]  Starr Roxanne Hiltz,et al.  The “Virtual Classroom”: Using Computer‐Mediated Communication for University Teaching , 1986 .

[25]  Peter M. Bentler,et al.  EQS : structural equations program manual , 1989 .

[26]  Georgios B. Giannakis,et al.  Identifiability of sparse structural equation models for directed and cyclic networks , 2013, 2013 IEEE Global Conference on Signal and Information Processing.

[27]  Chien-Hung Liu,et al.  Learning effectiveness in a Web-based virtual learning environment: a learner control perspective , 2005, J. Comput. Assist. Learn..

[28]  Martijn H. Vastenburg,et al.  Flowie: A persuasive virtual coach to motivate elderly individuals to walk , 2009, 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare.

[29]  C. O. Houle,et al.  Adult Education: Evolution and Achievements in a Developing Field of Study , 1991 .

[30]  Richard E. Mayer,et al.  Multimedia Learning: INTRODUCTION TO MULTIMEDIA LEARNING , 2009 .

[31]  J. Hair Multivariate data analysis , 1972 .

[32]  John C. Reinard Communication research statistics , 2006 .