A model for Mobile-based Assessment adoption based on Self-Determination Theory of Motivation

In order to successfully deliver Mobile-Based Assessments in any educational setting, it is of great importance to investigate the factors that influence its adoption from the students. The present study aims to explain and predict the Technology Acceptance Model constructs “Attitudes towards Using” (ATU) and “Intention to Use” (ITU) mobile-based assessment from the perspective of the Self-Determination Theory of Motivation. 72 medical students answered a survey questionnaire about the use of a mobile-based assessment conducted after the lecture and patient examination procedure. Partial Least Squares (PLS) was used for data analysis. Results show that the main motivational factors of self-determination theory, namely Autonomy, Relatedness and Competency, explain students' attitudes about mobile-based assessment and also predict students' adoption. Our research findings suggest that in order to enhance students' learning motivation, the design and implementation of mobile-based assessments should satisfy the three basic psychological needs for competency, autonomy and relatedness.

[1]  Chih-Ming Chen,et al.  Personalised context-aware ubiquitous learning system for supporting effective English vocabulary learning , 2010, Interact. Learn. Environ..

[2]  Kuan-Chung Chen,et al.  Motivation in online learning: Testing a model of self-determination theory , 2010, Comput. Hum. Behav..

[3]  Øystein Sørebø,et al.  The role of self-determination theory in explaining teachers' motivation to continue to use e-learning technology , 2009, Comput. Educ..

[4]  Yueh-Min Huang,et al.  Social Learning Networks: Build Mobile Learning Networks Based on Collaborative Services , 2010, J. Educ. Technol. Soc..

[5]  Hans van der Heijden,et al.  User Acceptance of Hedonic Information Systems , 2004, MIS Q..

[6]  Anastasios A. Economides,et al.  Mobile assessment: state of the art , 2013 .

[7]  Kinshuk,et al.  An investigation of attitudes of students and teachers about participating in a context-aware ubiquitous learning activity , 2011 .

[8]  Gwo-Jen Hwang,et al.  A formative assessment-based mobile learning approach to improving the learning attitudes and achievements of students , 2011, Comput. Educ..

[9]  Gill Clough,et al.  Mobile learning: Two case studies of supporting inquiry learning in informal and semiformal settings , 2013, Comput. Educ..

[10]  Marylène Gagné,et al.  Understanding e-learning continuance intention in the workplace: A self-determination theory perspective , 2008, Comput. Hum. Behav..

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

[12]  Vilson Gruber,et al.  Extending access to remote labs from mobile devices in educational contexts , 2013, Int. J. Online Eng..

[13]  Chao-hsiu Chen,et al.  The implementation and evaluation of a mobile self- and peer-assessment system , 2010, Comput. Educ..

[14]  Raafat George Saadé,et al.  Understanding Intention to Use Multimedia Information Systems for Learning , 2005 .

[15]  Yeonjeong Park,et al.  A Pedagogical Framework for Mobile Learning: Categorizing Educational Applications of Mobile Technologies into Four Types. , 2011 .

[16]  Anastasios A. Economides,et al.  Computer Based Assessment Acceptance: A Cross-cultural Study in Greece and Mexico , 2013, J. Educ. Technol. Soc..

[17]  Edward L. Deci,et al.  Intrinsic Motivation and Self-Determination in Human Behavior , 1975, Perspectives in Social Psychology.

[18]  Anastasios A. Economides,et al.  Continuance acceptance of computer based assessment through the integration of user's expectations and perceptions , 2013, Comput. Educ..

[19]  S. Thompson Social Learning Theory , 2008 .

[20]  E. Deci,et al.  Handbook of Self-Determination Research , 2002 .

[21]  Sung Youl Park,et al.  University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model , 2012, Br. J. Educ. Technol..

[22]  Yao-Ting Sung,et al.  A Blended Mobile Learning Environment for Museum Learning , 2014, J. Educ. Technol. Soc..

[23]  Anastasios A. Economides,et al.  The acceptance and use of computer based assessment , 2011, Comput. Educ..

[24]  Felix B. Tan,et al.  Adult learners' intention to adopt mobile learning: A motivational perspective , 2015, Br. J. Educ. Technol..

[25]  Anastasios A. Economides,et al.  Mobiles in education: students' usage, preferences and desires , 2010, Int. J. Mob. Learn. Organisation.

[26]  Yin-Leng Theng Mobile Learning for Tertiary Students: An Exploratory Study of Acceptance of Use , 2009 .

[27]  Anastasios A. Economides,et al.  Acceptance of Mobile-Based Assessment from the Perspective of Self-Determination Theory of Motivation , 2014, 2014 IEEE 14th International Conference on Advanced Learning Technologies.

[28]  Anastasios A. Economides,et al.  The design and evaluation of a computerized adaptive test on mobile devices , 2008, Comput. Educ..

[29]  Richard F. Kenny,et al.  Using self-efficacy to assess the readiness of nursing educators and students for mobile learning , 2012 .

[30]  Ahmad Abu-Al-Aish,et al.  International of Research in Open and Distributed Learning Factors Influencing Students’ Acceptance of M-Learning: An Investigation in Higher Education , 2022 .

[31]  Geoffrey C. Williams,et al.  How self-determination theory can assist our understanding of the teaching and learning processes in medical education. AMEE Guide No. 59 , 2011, Medical teacher.

[32]  Jerry Chih-Yuan Sun,et al.  Influence of polling technologies on student engagement: An analysis of student motivation, academic performance, and brainwave data , 2014, Comput. Educ..

[33]  Dennis F. Galletta,et al.  How Endogenous Motivations Influence User Intentions: Beyond the Dichotomy of Extrinsic and Intrinsic User Motivations , 2008, J. Manag. Inf. Syst..

[34]  Anastasios A. Economides,et al.  CAT-MD: Computerized Adaptive Testing on Mobile Devices , 2008, Int. J. Web Based Learn. Teach. Technol..

[35]  Anastasios A. Economides Requirements of Mobile Learning Applications , 2008 .

[36]  Ann Jones,et al.  Motivation and mobile devices: exploring the role of appropriation and coping strategies , 2007 .

[37]  Shengnan Han,et al.  Understanding the factors driving m‐learning adoption: a literature review , 2010 .

[38]  Terry E. Duncan,et al.  Psychometric properties of the Intrinsic Motivation Inventory in a competitive sport setting: a confirmatory factor analysis. , 1989, Research quarterly for exercise and sport.