The two perfect scorers for technology acceptance

This research paper examines the acceptance of technology for learning by senior secondary school students and university newcomers. The objectives of the study are to measure the computer competency, computer self-efficacy of selected student cohorts on the acceptance of technology for learning. The study uses the extended Technology Acceptance Model (TAM) with two additional attributes, computer competencies and computer self-efficacies to examine students’ behavior towards learning with technology. Two sets of data were collected; one was from Year 12 and Year 13 students from 33 secondary schools in Fiji, and the other from newcomers of a regional university in the South Pacific. The cohorts were surveyed with a unipolar Likert scale 1–5 questionnaire. The results were analysed using the “Statistical Package for the Social Sciences” – SPSS software and the proposed extended TAM model was analysed using the Smart Partial least squares (SmartPLS) software. The results from the regression analysis confirmed that the two attributes had a significant positive impact on the acceptance of the technology, that is, computer competency and computer self – efficacy were significant predictors of students’ intention to continue using technology for learning. Therefore, a new model incorporating the two perfect scorers is designed and presented in this paper. The high values for Cronbach’s alpha also show that the results were reliable and valid. Finally, the study shows that computer competencies and computer self-efficacies are essential contributors to the continuous use of technology for learning.

[1]  Chua Yan Piaw,et al.  Relationship Between Teacher ICT Competency and Teacher Acceptance and Use of School Management System (SMS) , 2016 .

[2]  Tuuli Turja,et al.  Self-efficacy and acceptance of robots , 2019, Comput. Hum. Behav..

[3]  James P. Neelankavil,et al.  Self-efficacy as an antecedent of cognition and affect in technology acceptance , 2014 .

[4]  Bibhya Sharma,et al.  Measuring the digital competency of freshmen at a higher education institute , 2020, PACIS.

[5]  B. Sharma,et al.  Mobile Learning Readiness and ICT Competency: A Case Study of Senior Secondary School Students in the Pacific Islands , 2017, 2017 4th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE).

[6]  Aspasia Togia,et al.  IT self-efficacy and computer competence of LIS students , 2012, Electron. Libr..

[7]  Chi Cheng Chang,et al.  Perceived Convenience in an Extended Technology Acceptance Model: Mobile Technology and English Learning for College Students. , 2012 .

[8]  Ronny Scherer,et al.  The role of ICT self-efficacy for students' ICT use and their achievement in a computer and information literacy test , 2016, Comput. Educ..

[9]  Sahin Gokcearslan Perspectives of Students on Acceptance of Tablets and Self-directed Learning with Technology , 2017 .

[10]  Tingru Zhang,et al.  Key characteristics in designing massive open online courses (MOOCs) for user acceptance: an application of the extended technology acceptance model , 2019, Interact. Learn. Environ..

[11]  Serap Kurbanoglu,et al.  Self-efficacy: a concept closely linked to information literacy and lifelong learning , 2003, J. Documentation.

[12]  Mehrbakhsh Nilashi,et al.  Decision to adopt online collaborative learning tools in higher education: A case of top Malaysian universities , 2018, Education and Information Technologies.

[13]  Chin-Lung Hsu,et al.  Why do people play on-line games? An extended TAM with social influences and flow experience , 2004, Inf. Manag..

[14]  Pritika Reddy,et al.  Student Readiness and Perception of Tablet Learning in Higher Education in the Pacific- A Case Study of Fiji and Tuvalu: Tablet Learning at USP , 2020, J. Cases Inf. Technol..

[15]  R. Shroff,et al.  Analysis of the technology acceptance model in examining students' behavioural intention to use an e-portfolio system , 2011 .

[16]  Steve Drew,et al.  The Role of Self-efficacy in Technology Acceptance , 2018, Proceedings of the Future Technologies Conference (FTC) 2018.

[17]  Duvince Zhalimar Dumpit,et al.  Analysis of the use of social media in Higher Education Institutions (HEIs) using the Technology Acceptance Model , 2017, International Journal of Educational Technology in Higher Education.

[18]  Mohamed Yeou,et al.  An Investigation of Students’ Acceptance of Moodle in a Blended Learning Setting Using Technology Acceptance Model , 2016 .

[19]  Bibhya Sharma,et al.  Effectiveness of tablet learning in online courses at University of the South Pacific , 2015, 2015 2nd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE).

[20]  Rebecca Strachan,et al.  A modified TAM for predicting acceptance of digital educational games by teachers , 2017, 2017 IEEE Global Engineering Education Conference (EDUCON).

[21]  Yu Zhonggen,et al.  An extended technology acceptance model of a mobile learning technology , 2019, Comput. Appl. Eng. Educ..

[22]  Yunfei Du The Relationship between Students' Computer Competency and Perception of Enjoyment and Difficulty Level in Web-Based Distance Learning. , 2017 .

[23]  Students’ Technology Acceptance, Motivation and Self-Efficacy towards the eSchoolbag: An Exploratory Study , 2017 .

[24]  Young Ju Joo,et al.  Factors Influencing Preservice Teachers' Intention to Use Technology: TPACK, Teacher Self-efficacy, and Technology Acceptance Model , 2018, J. Educ. Technol. Soc..

[25]  Bibhya N. Sharma,et al.  Effectiveness of online presence in a blended higher learning environment in the Pacific , 2020, Studies in Higher Education.

[26]  Emtinan Alqurashi,et al.  Self-Efficacy In Online Learning Environments: A Literature Review , 2016 .

[27]  Joseph F. Hair,et al.  When to use and how to report the results of PLS-SEM , 2019, European Business Review.

[28]  T. Teo,et al.  Exploring the drivers of technology acceptance: a study of Nepali school students , 2019, Educational Technology Research and Development.

[29]  Ahmad A. Rabaa'i Extending the technology acceptance model (TAM) to assess students' behavioural intentions to adopt an e-learning system : the case of moodle as a learning tool , 2016 .

[30]  Ibrahim Akman,et al.  User acceptance of social learning systems in higher education: an application of the extended Technology Acceptance Model , 2017 .

[31]  Timothy Teo,et al.  Modelling Facebook usage among university students in Thailand: the role of emotional attachment in an extended technology acceptance model , 2016, Interact. Learn. Environ..

[32]  Xin Bai Promote Technology Self-efficacy via a SCORM-Based e-Learning Approach , 2017 .

[33]  A. Tarhini,et al.  Technology, Demographic Characteristics and E-Learning Acceptance: A Conceptual Model Based on Extended Technology Acceptance Model , 2016 .

[34]  S. Raturi Understanding learners preferences for learning environments in higher education , 2018 .

[35]  A. Al-Adwan,et al.  Exploring Students Acceptance of E-Learning Using Technology Acceptance Model in Jordanian Universities. , 2013 .

[36]  Knut-Andreas Christophersen,et al.  Perceptions of digital competency among student teachers: contributing to the development of student teachers’ instructional self-efficacy in technology-rich classrooms , 2017 .

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

[38]  Fred D. Davis,et al.  A Model of the Antecedents of Perceived Ease of Use: Development and Test† , 1996 .

[39]  Sami Mohamed,et al.  The Effect of Self-Efficacy in the Acceptance of Information Technology in the Public Sector , 2013 .

[40]  Rupert Ward,et al.  Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors , 2016, Comput. Hum. Behav..

[41]  Andrea Bonanomi,et al.  Factors Affecting Students' Acceptance of Tablet PCs: A Study in Italian High Schools , 2018 .

[42]  Bibhya N. Sharma,et al.  Digital Literacy: A Review of Literature , 2020, Int. J. Technoethics.

[43]  Ravneil Nand,et al.  Smart Learning in the Pacific: Design of New Pedagogical Tools , 2018, 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE).

[44]  Bimal Aklesh Kumar,et al.  Usability of mobile learning applications: a systematic literature review , 2017, Journal of Computers in Education.

[45]  Matt C. Howard Creation of a Computer Self-Efficacy Measure: Analysis of Internal Consistency, Psychometric Properties, and Validity , 2014, Cyberpsychology Behav. Soc. Netw..

[46]  Swasti S Narayan,et al.  Use of Mobile Devices for Learning and Student Support in the Pacific Region , 2018, Handbook of Mobile Teaching and Learning.

[47]  Ove Edvard Hatlevik,et al.  Examining the Relationship between Teachers’ Self-Efficacy, their Digital Competence, Strategies to Evaluate Information, and use of ICT at School , 2017 .

[48]  Viraiyan Teeroovengadum,et al.  Examining the antecedents of ICT adoption in education using an Extended Technology Acceptance Model (TAM) , 2017 .

[49]  Christine Bachman,et al.  Predictors of Faculty Acceptance of Online Education Predictores de Aceptación de la Facultad de Educacion Online , 2010 .

[50]  Bibhya Sharma,et al.  Student Readiness and Perception to the Use of Smart Phones for Higher Education in the Pacific , 2016, 2016 3rd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE).

[51]  Mohamad Bilal Ali,et al.  The Influences of Technical Support, Self Efficacy and Instructional Design on the Usage and Acceptance of LMS: A Comprehensive Review. , 2016 .

[52]  Siti Rosnita Sakarji,et al.  INVESTIGATING STUDENTS ACCEPTANCE OF ELEARNING USING TECHNOLOGY ACCEPTANCE MODEL AMONG DIPLOMA IN OFFICE MANAGEMENT AND TECHNOLOGY STUDENTS AT UITM MELAKA , 2019, Journal of Information System and Technology Management.

[53]  Meltem Huri Baturay,et al.  The relationship among pre-service teachers' computer competence, attitude towards computer-assisted education, and intention of technology acceptance , 2017 .

[54]  Nidn. Seger Handoyo,et al.  Computer Competency, Test Anxiety, and Perceived Ease of Use Profile Exploration of High School Students during Computer-Based Testing , 2019 .

[55]  P. Ranganathan,et al.  Common pitfalls in statistical analysis: Linear regression analysis , 2017, Perspectives in clinical research.

[56]  Muhammad Awais Bhatti,et al.  Factors affecting e-training adoption: an examination of perceived cost, computer self-efficacy and the technology acceptance model , 2017, Behav. Inf. Technol..