research-article

Mobile-Based Assessment

Authors:

Stavros A. Nikou,

Anastasios A. EconomidesAuthors Info & Claims

Computers in Human Behavior, Volume 68, Issue C

Pages 83 - 95

https://doi.org/10.1016/j.chb.2016.11.020

Published: 01 March 2017 Publication History

Abstract

Mobile-Based Assessment (MBA) is an alternative or complementary to paper- or computer-based assessment delivery mode. Its successful implementation depends on users' acceptance. However, no study exists exploring the factors that influence students' acceptance of mobile-based assessment. Furthermore, research that combines acceptance with motivational factors is limited. The current study builds on the theoretical framework of the Self-Determination Theory (SDT) of Motivation and the Technology Acceptance Model (TAM) and proposes the Mobile Based Assessment - Motivational and Acceptance Model (MBA-MAM), a combined model that explains and predicts Behavioral Intention to Use Mobile-based Assessment. One-hundred and forty students (N=140) from a European senior-level secondary school participated in mobile-assisted assessment activities and self-reported their perceptions about afterwards. Structured equation modeling used to analyze quantitative survey data. The study confirmed the proposed model, explaining and predicting students intention to use MBA in terms of both acceptance and motivational (autonomy, competence and relatedness) factors. The study provides a better understanding towards the development of mobile-based assessments by relating acceptance and motivational factors into an integrated model. Implications are discussed within the wider context of mobile learning acceptance research. The study explores students' acceptance of Mobile-Based Assessment (MBA).We propose Mobile Based Assessment - Motivation and Acceptance Model (MBA-MAM).The model is based on Self-Determination Theory and Technology Acceptance Model.Intention to use MBA is explained in terms of motivation and acceptance factors.

References

[1]

A. Abu-Al-Aish, S. Love, Factors influencing students' acceptance of mlearning: An investigation in higher education, The International Review of Research in Open and Distance Learning, 14 (2013) 82-107.

[2]

S.A. Nikou, A.A. Economides, Mobile assessment: state of the art, in: Handbook of mobile learning, Routledge, Florence, KY, 2013, pp. 346-355.

[3]

P.P. Baard, E.L. Deci, R.M. Ryan, Intrinsic need satisfaction: A motivational basis of performance and well-being in two work settings, Journal of Applied Social Psychology, 34 (2004) 2045-2068.

[4]

L. Blasco-Arcas, I. Buil, B. Hernndez-Ortega, F.J. Sese, Using clickers in class. The role of interactivity, active collaborative learning and engagement in learning performance, Computers & Education, 62 (2013) 102-110.

Digital Library

[5]

Z. Bogdanovic, D. Barac, B. Jovanic, S. Popovic, B. Radenkovic, Evaluation of mobile assessment in a learning management system, British Journal of Educational Technology, 45 (2013) 231-244.

[6]

L. Briz-Ponce, A. Pereira, L. Carvalho, J.A. Juanes-Mndez, F.J. Garca-Pealvo, Learning with mobile technologies students' behavior, Computers in Human Behavior (25 May 2016).

Digital Library

[7]

K. Burden, M. Kearney, Research in Science Education, 2016 (2016) 287.

[8]

C. Burgers, A. Eden, M.D. van Engelenburg, S. Buningh, How feedback boosts motivation and play in a brain-training game, Computers in Human Behavior, 48 (2015) 94-103.

Digital Library

[9]

C.-H. Chen, The implementation and evaluation of a mobile self- and peer-assessment system, Computers & Education, 55 (2010) 229-236.

Digital Library

[10]

K. Chen, J.V. Chen, D.C. Yen, Dimensions of self-efficacy in the study of smart phone acceptance, Computer Standards & Interfaces, 33 (2011) 422-431.

Digital Library

[11]

K.-C. Chen, S.-J. Jang, Motivation in online learning: Testing a model of self-determination theory, Computers in Human Behavior (2010) 26741-26752.

Digital Library

[12]

J. Cheon, S. Lee, S.M. Crooks, J. Song, An investigation of mobile learning readiness in higher education based on the theory of planned behavior, Computers & Education, 59 (2012) 1054-1064.

Digital Library

[13]

W.W. Chin, The partial least squares approach to structural equation Modeling, in: Modern business research methods, Lawrence Erlbaum Associates, Mahwah, NJ, 1998, pp. 295-336.

[14]

D. Compeau, C. Higgins, Computer self-efficacy: development of a measure and initial test, MIS Quarterly, 19 (1995) 189-211.

Digital Library

[15]

D. Compeau, C.A. Higgins, S. Huff, Social cognitive theory and individual reactions to computing technology: A longitudinal study, MIS Quarterly, 23 (1999) 145-158.

Digital Library

[16]

F.D. Davis, Perceived usefulness, perceived ease of use and user acceptance of information technology, MIS Quartely, 13 (1989) 319-340.

Digital Library

[17]

F.D. Davis, R. Bagozzi, P. Warshaw, User acceptance of computer technology: A comparison of two theoretical models, Management Science, 35 (1989).

Digital Library

[18]

E.L. Deci, R.M. Ryan, Intrinsic motivation and self-determination in human behavior, Plenum, New York, 1985.

[19]

E.L. Deci, R.M. Ryan, Handbook of self-determination research, University of Rochester Press, Rochester, NY, 2002.

[20]

E.L. Deci, R.M. Ryan, Optimizing students' motivation in the era of testing and pressure: A self-determination theory perspective - building autonomous learners: Perspectives from research and practice using self-determination theory, in: Optimizing students' motivation in the era of testing and pressure: A self-determination theory perspective - building autonomous learners: Perspectives from research and practice using self-determination theory, Singapore: Springer, Singapore, 2016, pp. 9-29.

[21]

E.L. Deci, R.J. Vallerand, L.G. Pelletier, R.M. Ryan, Motivation and education: The self determination perspective, Educational Psychologist, 26 (1991) 325-346.

[22]

A.A. Economides, Conative feedback in computer-based assessment, Computers in the Schools, 26 (2009) 207-223.

[23]

A.A. Economides, A. Grousopoulou, Mobiles in education: Students' usage, preferences and desires, International Journal of Mobile Learning and Organisation, 4 (2010) 235-252. http://conta.uom.gr/conta/filter_pub.php?filter=34&lang=gr

Digital Library

[24]

M.H. Fagan, S. Neill, B.R. Wooldridge, Exploring the intention to use computers: An empirical investigation of the role of intrinsic motivation, extrinsic motivation, and perceived ease of use, Journal of Computer Information Systems, 48 (2008) 31-37.

[25]

M.S. Hagger, S. Koch, N.L.D. Chatzisarantis, The effect of causality orientations and positive competence-enhancing feedback on intrinsic motivation: A test of additive and interactive effects, Personality and Individual Differences, 72 (2015) 107-111.

[26]

M.K. Hartnett, Influences that undermine learners' perceptions of autonomy, competence and relatedness in an online context, Australasian Journal of Educational Technology, 31 (2015).

[27]

J. Hattie, H. Timperley, The power of feedback, Review of Educational Research, 77 (2007) 81-112.

[28]

I. Ha, Y. Yoon, M. Choi, Determinants of adoption of mobile games under mobile broadband wireless access environment, Information & Management, 44 (2007) 276-286.

Digital Library

[29]

H. van der Heijden, User acceptance of hedonic information systems, MIS Quarterly, 28 (2004) 695-702.

Digital Library

[30]

Y.M. Huang, Exploring the factors that affect the intention to use collaborative technologies: The differing perspectives of sequential/global learners, Australasian Journal of Educational Technology, 31 (2015) 278-292.

[31]

Y.-M. Huang, Exploring students' acceptance of team messaging services: The roles of social presence and motivation, British Journal of Educational Technology (2016).

[32]

Y.-M. Huang, P.-S. Chiu, T.-C. Liu, T.-S. Chen, The design and implementation of a meaningful learning-based evaluation method for ubiquitous learning, Computers & Education, 57 (2011) 2291-2302.

Digital Library

[33]

K.C. Huff, The comparison of mobile devices to computers for web-based assessments, Computers in Human Behavior, 49 (2015) 208-212.

Digital Library

[34]

N.J. Hunsu, O. Adesope, D.J. Bayly, Ameta-analysis of the effects of audience response systems (clicker-based technologies) on cognition and affect, Computers & Education, 94 (2016) 102-119.

Digital Library

[35]

G.J. Hwang, C.C. Tsai, S.J.H. Yang, Criteria, strategies and research issues of context-aware ubiquitous learning, Educational Technology & Society, 11 (2008) 81-91.

[36]

S. Iqbal, I.A. Qureshi, m-learning adoption: A perspective from a developing country, The International Review of Research in Open and Distance Learning, 3 (2012) 147-164.

[37]

L. Johnson, S. Adams Becker, M. Cummins, V. Estrada, A. Freeman, C. Hall, NMC horizon report: 2016 higher education edition, The New Media Consortium, Austin, Texas, 2016.

[38]

M.K. Lee, C.M. Cheung, Z. Chen, Acceptance of internet-based learning medium: The role of extrinsic and intrinsic motivation, Information & Management, 42 (2005) 1095-1104.

Digital Library

[39]

Y. Lee, J. Lee, Y. Hwang, Relating motivation to information and communication technology acceptance: Self-determination theory perspective, Computers in Human Behavior, 51 (2015) 418-428.

Digital Library

[40]

S.S. Liaw, H.-M. Huang, Exploring learners' acceptance toward mobile learning, in: Technology acceptance in Education: Research and issues, Sense Publishers, 2011, pp. 145-157.

[41]

S.S. Liaw, H.M. Huang, G.D. Chen, An activity-theoretical approach to investigate learners' factors toward e-learning systems, Computers in Human Behavior, 23 (2007) 1906-1920.

Digital Library

[42]

Y. Liu, S. Han, H. Li, Understanding the factors driving m-learning adoption: A literature review, Campus-Wide Information Systems, 27 (2010) 210-226.

[43]

Y. Liu, H. Li, C. Carlsson, Factors driving the adoption of m-learning: An empirical study, Computers & Education, 55 (2010) 1211-1219.

Digital Library

[44]

H. Lu, Y.-p Hu, J.-j Gao, Kinshuk, The effects of computer self-efficacy, training satisfaction and test anxiety on attitude and performance in computerized adaptive testing, Computers & Education, 100 (2016) 45-55.

Digital Library

[45]

K. Mac Callum, L. Jeffrey, Kinshuk, Comparing the role of ICT literacy and anxiety in the adoption of mobile learning, Computers in Human Behavior, 39 (2014) 8-19.

Digital Library

[46]

E. McAuley, T. Duncan, V.V. Tammen, Psychometric properties of the intrinsic motivation inventory in a competitive sport setting: A confirmatory factor analysis, Research Quarterly for Exercise and Sport, 60 (1989) 48-58.

[47]

M. Milrad, L.-H. Wong, M. Sharples, G.-J. Hwang, C.-K. Looi, H. Ogata, Seamless Learning: An international perspective on next generation technology enhanced learning, in: Handbook of mobile learning, Routledge, New York, 2013.

[48]

B. Muirhead, Encouraging interaction in online classes, International Journal of Instructional Technology and Distance Learning, 1 (2004) 45-50.

[49]

B. Muirhead, C. Juwah, Interactivity in computer-mediated college and university education: A recent review of the literature, Educational Technology & Society, 7 (2004) 12-20.

[50]

J. Mumm, B. Mutlu, Designing motivational agents: The role of praise, social comparison, and embodiment in computer feedback, Computers in Human Behavior, 27 (2011) 1643-1650.

Digital Library

[51]

J.D. Naeghel, H.V. Keer, M. Vansteenkiste, L. Haerens, N. Aelterman, Promoting elementary school students' autonomous reading motivation: Effects of a teacher professional development workshop, The Journal of Educational Research, 109 (2016) 232-252.

[52]

S. Narciss, S. Sosnovsky, L. Schnaubert, E. Andrs, A. Eichelmann, G. Goguadze, Exploring feedback and student characteristics relevant for personalizing feedback strategies, Computers & Education, 71 (2014) 56-76.

Digital Library

[53]

C.P. Niemiec, R.M. Ryan, Autonomy, competence, and relatedness in the classroom: Applying self-determination theory to educational practice, Theory and Research in Education, 7 (2009) 133-144.

[54]

S.A. Nikou, A.A. Economides, Amodel for mobile-based assessment adoption based on self-determination theory of motivation, in: International conference on interactive mobile communication technologies and learning (IEEE), 8690, Thessaloniki, Greece, 2014.

[55]

S.Y. Park, M.-W. Nam, S.-B. Cha, University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model, British Journal of Educational Technology, 43 (2012) 592-605.

[56]

P.P.E. Pe-Than, D.H.-L. Goh, C.S. Lee, Making work fun: Investigating antecedents of perceived enjoyment in human computation games for information sharing, Computers in Human Behavior, 39 (2014) 88-99.

Digital Library

[57]

M. Pedrotti, N. Nistor, User motivation and technology acceptance in online learning environments, in: User motivation and technology acceptance in online learning environments, 2016, pp. 472-477.

[58]

M. Prince, Does active learning work? a review of the research, Journal of Engineering Education, 93 (2004) 223-231.

[59]

Ringle, C. M., Wende, S. & Will, A. SmartPLS 2.0 (beta), {computer software}, Retrieved from http://www.smartpls.de.

[60]

J.C.M. Roca, M. Gagn, Understanding e-learning continuance intention in the workplace: A self-determination theory perspective, Computers in Human Behavior, 24 (2008) 1585-1604.

Digital Library

[61]

R.M. Ryan, E.L. Deci, Intrinsic and extrinsic motivations: Classic definitions and new directions, Contemporary Educational Psychology, 25 (2000) 54-67.

[62]

R.M. Ryan, E.L. Deci, When rewards compete with nature: The undermining of intrinsic motivation and self-regulation, Academic Press, New York: NY, 2000.

[63]

D.Y. Shee, Y.-S. Wang, Multi-criteria evaluation of the web-based e-learning system: A methodology based on learner satisfaction and its applications, Computer & Education, 50 (2008) 894-905.

Digital Library

[64]

H.-P. Shih, Assessing the effects of self-efficacy and competence on individual satisfaction with computer use: An IT student perspective, Computers in Human Behavior, 22 (2006) 1012-1026.

[65]

H.-J. So, T.A. Brush, Student perceptions of collaborative learning, social presence and satisfaction in a blended learning environment: Relationships and critical factors, Computers & Education, 51 (2008) 318-336.

Digital Library

[66]

. Sreb, H. Halvari, V.F. Gulli, R. Kristiansen, The role of self-determination theory in explaining teachers' motivation to continue to use e-learning technology, Computers & Education, 53 (2009) 1177-1187.

Digital Library

[67]

M. Standage, J.L. Duda, N. Ntoumanis, Students' motivational processes and their relationship to teacher ratings in school physical education: A self-determination theory approach, Research Quarterly for Exercise and Sport, 77 (2006) 100-110.

[68]

B. Sumak, M. Hericko, M. Pusnik, Ameta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types, Computers in Human Behavior, 27 (2011) 2067-2077.

Digital Library

[69]

T. Teo, C.B. Lee, C.S. Chai, D. Choy, Modeling pre-service teachers' perceived usefulness of an ICT-based student-centered learning (SCL) curriculum: A Singapore study, Asia Pacific Education Review, 10 (2009).

[70]

V. Terzis, A.A. Economides, The acceptance and use of computer based assessment, Computers & Education, 56 (2011) 1032-1044.

Digital Library

[71]

V. Terzis, C.N. Moridis, A.A. Economides, The effect of emotional feedback on behavioral intention to use computer based assessment, Computers & Education, 59 (2012) 710-721.

Digital Library

[72]

K. Thamadharan, N. Maarop, The acceptance of e-assessment considering security Perspective: Work in progress, World Academy of Science, Engineering and Technology, International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 9 (2015).

[73]

V. Venkatesh, Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model, Information Systems Research, 11 (2000) 342-365.

Digital Library

[74]

V. Venkatesh, M.G. Morris, G.B. Davis, F.D. Davis, User acceptance of information technology: Toward a unified view, MIS Quarterly, 27 (2003) 425-478.

Digital Library

[75]

S.-L. Wang, P.-Y. Wu, The role of feedback and self-efficacy on web-based learning: The social cognitive perspective, Computers & Education, 51 (2008) 1589-1598.

Digital Library

[76]

Y.-S. Wang, M.-C. Wu, H.-Y. Wang, Investigating the determinants and age and gender differences in the acceptance of mobile learning, British Journal of Educational Technology, 40 (2009) 92-118.

[77]

M. West, S.E. Vosloo, UNESCO policy guidelines for mobile learning, UNESCO, Paris, 2013.

[78]

S.J. Yoo, S.-h. Han, W. Huang, The roles of intrinsic motivators and extrinsic motivators in promoting e-learning in the workplace: A case from South Korea, Computers in Human Behavior, 28 (2012) 942-950.

Digital Library

[79]

S. Zhang, J. Zhao, W. Tan, Extending TAM for online learning systems: An intrinsic motivation perspective, Tsinghua Science & Technology, 13 (2008) 312-317.

[80]

M. Zhou, Chinese university students' acceptance of MOOCs: A self-determination perspective, Computers & Education, 9293 (2016) 194-203.

Digital Library

Cited By

Gruner MSchrills TWrage MSieger MFranke T(2024)Driven by Motivation: Understanding Perceived Mobility Need Satisfaction in On-Demand RidepoolingProceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications10.1145/3640792.3675733(405-416)Online publication date: 22-Sep-2024
https://dl.acm.org/doi/10.1145/3640792.3675733
Zheng YWang YLiu KJiang M(2024)Examining the moderating effect of motivation on technology acceptance of generative AI for English as a foreign language learningEducation and Information Technologies10.1007/s10639-024-12763-329:17(23547-23575)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10639-024-12763-3
Cai WJin YZhao XChen L(2023)“Listen to Music, Listen to Yourself”: Design of a Conversational Agent to Support Self-Awareness While Listening to MusicProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581427(1-19)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581427
Show More Cited By

Recommendations

Mobile-based assessment

Acceptance and intention to use mobile learning is a topic of growing interest in the field of education. Although there is a considerable amount of studies investigating mobile learning acceptance, little research exists that investigates the driving ...
Factors affecting consumer stickiness to continue using mobile applications

According to a recent survey, more than half of all such apps are rarely been downloaded. This study thus examines consumer stickiness to continue to use mobile apps, using the post-acceptance model of information system continuance and technology ...
Determinants of behavioral intention to mobile banking

With the improvement of mobile technologies and devices, banking users are able to conduct banking services at anyplace and at anytime. Recently, many banks in the world have provided mobile access to financial information. The reason to understand what ...

Comments

Information & Contributors

Information

Published In

cover image Computers in Human Behavior

Computers in Human Behavior Volume 68, Issue C

March 2017

564 pages

ISSN:0747-5632

Issue’s Table of Contents

Copyright © Elsevier Ltd.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 March 2017

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

26
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 27 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Gruner MSchrills TWrage MSieger MFranke T(2024)Driven by Motivation: Understanding Perceived Mobility Need Satisfaction in On-Demand RidepoolingProceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications10.1145/3640792.3675733(405-416)Online publication date: 22-Sep-2024
https://dl.acm.org/doi/10.1145/3640792.3675733
Zheng YWang YLiu KJiang M(2024)Examining the moderating effect of motivation on technology acceptance of generative AI for English as a foreign language learningEducation and Information Technologies10.1007/s10639-024-12763-329:17(23547-23575)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10639-024-12763-3
Cai WJin YZhao XChen L(2023)“Listen to Music, Listen to Yourself”: Design of a Conversational Agent to Support Self-Awareness While Listening to MusicProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581427(1-19)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581427
Ateş HGündüzalp C(2023)A unified framework for understanding teachers’ adoption of robotics in STEM educationEducation and Information Technologies10.1007/s10639-023-12382-429:11(1-27)Online publication date: 15-Dec-2023
https://dl.acm.org/doi/10.1007/s10639-023-12382-4
Ortiz-López AMigueláñez SSánchez-Prieto J(2023)Behavioral Intention to Use Mobile Devices for Assessment in Pre-service Teachers: A Gender or an Initial Training Issue?Proceedings of TEEM 202310.1007/978-981-97-1814-6_111(1135-1146)Online publication date: 25-Oct-2023
https://dl.acm.org/doi/10.1007/978-981-97-1814-6_111
Baek H(2022)Mobile Location-Based ServicesInternational Journal of Mobile Computing and Multimedia Communications10.4018/IJMCMC.29796713:1(1-17)Online publication date: 24-Feb-2022
https://dl.acm.org/doi/10.4018/IJMCMC.297967
Stiegemeier DBringeland SKraus JBaumann MJi YJeon M(2022)User Experience of In-Vehicle Gesture Interaction: Exploring the Effect of Autonomy and Competence in a Mock-Up ExperimentProceedings of the 14th International Conference on Automotive User Interfaces and Interactive Vehicular Applications10.1145/3543174.3546847(285-296)Online publication date: 17-Sep-2022
https://dl.acm.org/doi/10.1145/3543174.3546847
Nguyen QSidorova ATorres R(2022)User interactions with chatbot interfaces vs. Menu-based interfacesComputers in Human Behavior10.1016/j.chb.2021.107093128:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.chb.2021.107093
Sungur Gül KAteş H(2022)An examination of the effect of technology-based STEM education training in the framework of technology acceptance modelEducation and Information Technologies10.1007/s10639-022-11539-x28:7(8761-8787)Online publication date: 28-Dec-2022
https://dl.acm.org/doi/10.1007/s10639-022-11539-x
Akbari MDanesh MRezvani AJavadi NBanihashem SNoroozi O(2022)The role of students’ relational identity and autotelic experience for their innovative and continuous use of e-learningEducation and Information Technologies10.1007/s10639-022-11272-528:2(1911-1934)Online publication date: 10-Aug-2022
https://dl.acm.org/doi/10.1007/s10639-022-11272-5
Show More Cited By

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents