Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Exploring the impact of augmented reality on student academic self-efficacy in higher education

Published: 01 December 2023 Publication History

Abstract

This study develops and empirically tests a conceptual model comprising eight hypotheses that focus on the impact of Augmented Reality (AR) on student academic self-efficacy. A controlled experiment was conducted, followed by an online questionnaire with 65 students. Partial Least Square (PLS) Structural Equation Modelling (SEM) is used to assess and test the model. The results show that student cognitive strategies impact student perception and engagement with technology and learning tasks, with AR positively impacting student academic self-efficacy in higher education. Interestingly, neither the learning space nor the students' perception of task value was shown to impact their intention to use AR. Instead, it was the characteristics of the technology itself which encouraged adoption. The study's findings reinforce the need to (1) teach and facilitate effective cognitive strategies among third level students and (2) identify and overcome negative cognitive strategies that harm student self-efficacy and engagement.

Highlights

Empirically tested conceptual model focusing on Augmented Reality (AR) and Student Academic Self-Efficacy
The characteristics of the AR application enticed the students to use it.
The learning environment nor the value students placed on the AR based activity influenced their intention to use AR.
The strategies the students used to learn influenced their confidence in their abilities and their view of the technology.
AR was also shown to increase students' academic confidence.

References

[1]
R. Agarwal, V. Venkatesh, Assessing a firm's web presence: A heuristic evaluation procedure for the measurement of usability, Information Systems Research 13 (2) (2002) 168–186,.
[2]
I. Ajzen, M. Fishbein, Theory of reasoned action in understanding attitudes and predicting social behaviour. Englewood cliffs, Prentice-Hall, 1980.
[3]
P.A. Alexander, S. Graham, K.R. Harris, A perspective on strategy research: Progress and prospects, Educational Psychology Review 10 (2) (1998) 129–154.
[4]
U. Alizkan, F. Wibowo, L. Sanjaya, B. Kurniawan, B. Prahani, Trends of augmented reality in science learning: A review of the literature, Journal of Physics: Conference Series (2021).
[5]
L. Anthonysamy, A.-C. Koo, S.-H. Hew, Self-regulated learning strategies and non-academic outcomes in higher education blended learning environments: A one decade review, Education and Information Technologies 25 (5) (2020) 3677–3704.
[6]
A.R. Artino, D.B. McCoach, Development and initial validation of the online learning value and self-efficacy scale, Journal of Educational Computing Research 38 (3) (2008) 279–303,.
[7]
M. Asoodar, S. Vaezi, B. Izanloo, Framework to improve e-learner satisfaction and further strengthen e-learning implementation, Computers in Human Behavior 63 (2016) 704–716,.
[8]
F.N. Astuti, S. Suranto, M. Masykuri, Augmented reality for teaching science: Students' problem solving skill, motivation, and learning outcomes, JPBI (Jurnal Pendidikan Biologi Indonesia) 5 (2) (2019) 305–312.
[9]
C. Avila-Garzon, J. Bacca-Acosta, J. Duarte, J. Betancourt, Augmented reality in education: An overview of twenty-five years of research, Contemporary Educational Technology 13 (3) (2021).
[10]
A. Bandura, Regulation of cognitive processes through perceived self-efficacy, Developmental Psychology 25 (5) (1989) 729.
[11]
A. Bandura, Self-efficacy in changing societies, Cambridge University Press, 1997, https://books.google.ie/books?id=JbJnOAoLMNEC.
[12]
A. Bandura, Social cognitive theory: An agentic perspective, Asian Journal of Social Psychology 2 (1) (1999) 21–41.
[13]
A. Bandura, Guide for constructing self-efficacy scales in urdan, tim, and frank pajares, in: Self-efficacy beliefs of adolescents, IAP, 2006, pp. 307–337. 2006. 5(1.
[14]
K. Bartimote-Aufflick, A. Bridgeman, R. Walker, M. Sharma, L. Smith, The study, evaluation, and improvement of university student self-efficacy, Studies in Higher Education 41 (11) (2016) 1918–1942,.
[15]
H. Bougsiaa, Teaching and learning context in augmented reality environment, Ars Educandi 13 (2016) 23–31.
[16]
M. Bower, C. Howe, N. McCredie, A. Robinson, D. Grover, Augmented Reality in education–cases, places and potentials, Educational Media International 51 (1) (2014) 1–15.
[17]
S. Brooman, S. Darwent, 'Yes, as the articles suggest, I have considered dropping out': Self-awareness literature and the first-year student, Studies in Higher Education 37 (1) (2012) 19–31,.
[18]
S. Cai, E. Liu, Y. Yang, J.C. Liang, Tablet‐based AR technology: Impacts on students' conceptions and approaches to learning mathematics according to their self‐efficacy, British Journal of Educational Technology 50 (1) (2019) 248–263.
[19]
S. Cai, X. Wang, F.-K. Chiang, A case study of Augmented Reality simulation system application in a chemistry course, Computers in Human Behavior 37 (2014) 31–40,.
[20]
S. Çelik, E. Arkın, D. Sabriler, EFL learners' use of ICT for self-regulated learning, Journal of Language and Linguistic Studies 8 (2) (2012).
[21]
T. Chandrasekera, S.-Y. Yoon, Augmented reality, virtual reality and their effect on learning style in the creative design process, Design and Technology Education 23 (1) (2018) n1.
[22]
S.-Y. Chen, S.-Y. Liu, Using augmented reality to experiment with elements in a chemistry course, Computers in Human Behavior 111 (2020),.
[23]
C.-H. Chen, T.-K. Liu, K. Huang, Scaffolding vocational high school students' computational thinking with cognitive and metacognitive prompts in learning about programmable logic controllers, Journal of Research on Technology in Education (2021) 1–18.
[24]
F.-K. Chiang, X. Shang, L. Qiao, Augmented reality in vocational training: A systematic review of research and applications, Computers in Human Behavior 129 (2022).
[25]
T.H.C. Chiang, S.J.H. Yang, G.-J. Hwang, An augmented reality-based mobile learning system to improve students' learning achievements and motivations in natural science inquiry activities, Journal of Educational Technology & Society 17 (4) (2014) 352–365. https://www.proquest.com/scholarly-journals/augmented-reality-based-mobile-learning-system/docview/1660156736/se-2.
[26]
W.W. Chin, The partial least squares approach to structural equation modeling, Modern methods for business research 295 (2) (1998) 295–336.
[27]
C.-M. Chiu, E.T.G. Wang, Understanding Web-based learning continuance intention: The role of subjective task value, Information & Management 45 (3) (2008) 194–201,.
[28]
F.D. Davis, Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly 13 (3) (1989) 319–340,.
[29]
W.H. Delone, E.R. McLean, The DeLone and McLean model of information systems success: A ten-year update, Journal of Management Information Systems 19 (4) (2003) 9–30.
[30]
Á. Di Serio, M.B. Ibáñez, C.D. Kloos, Impact of an augmented reality system on students' motivation for a visual art course, Computers & Education 68 (2013) 586–596.
[31]
Y.K. Dwivedi, N.P. Rana, A. Jeyaraj, M. Clement, M.D. Williams, Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model, Information Systems Frontiers 21 (3) (2019) 719–734.
[32]
H. Fan, R. Ahmad, T.-K. Chen, A study of fuzhou's undergraduate behavioural intentions to use tablet personal computer as replacement of textbook, International Journal of Data Science and Advanced Analytics 1 (1) (2019) 32–38.
[33]
D.L. Feick, F. Rhodewalt, The double-edged sword of self-handicapping: Discounting, augmentation, and the protection and enhancement of self-esteem [journal article], Motivation and Emotion 21 (2) (1997) 147–163.
[34]
C. Fornell, D.F. Larcker, Evaluating structural equation models with unobservable variables and measurement error, Journal of Marketing Research 18 (1) (1981) 39–50.
[35]
S.A. Gadbois, R.D. Sturgeon, Academic self-handicapping: Relationships with learning specific and general self-perceptions and academic performance over time, British Journal of Educational Psychology 81 (2) (2011) 207–222,.
[36]
R.M. Gillies, M. Boyle, Teachers' reflections on cooperative learning: Issues of implementation, Teaching and Teacher Education 26 (4) (2010) 933–940.
[37]
D.L. Goodhue, R.L. Thompson, Task-technology fit and individual performance, MIS Quarterly 19 (2) (1995) 213–236,.
[38]
P. Goodyear, R.A. Ellis, A. Marmot, Learning spaces research: Framing actionable knowledge, in: Spaces of teaching and learning, Springer, 2018, pp. 221–238.
[39]
M. Graham, M. Zook, A. Boulton, Augmented reality in urban places: Contested content and the duplicity of code, in: S. Carta (Ed.), Machine learning and the city: Applications in architecture and urban design, Wiley, 2022, pp. 341–366,.
[40]
J.R. Hackman, G.R. Oldham, Motivation through the design of work: Test of a theory, Organizational Behavior & Human Performance 16 (2) (1976) 250–279,.
[41]
J.F. Hair, G.T.M. Hult, C.M. Ringle, M. Sarstedt, K.O. Thiele, Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods, Journal of the Academy of Marketing Science 45 (5) (2017) 616–632.
[42]
J.F. Hair, J.J. Risher, M. Sarstedt, C.M. Ringle, When to use and how to report the results of PLS-SEM, European Business Review, 2019.
[43]
J. Hanham, C.B. Lee, T. Teo, The influence of technology acceptance, academic self-efficacy, and gender on academic achievement through online tutoring, Computers & Education 172 (2021),.
[44]
A.A. Hayat, K. Shateri, The role of academic self-efficacy in improving students' metacognitive learning strategies, Journal of Advances in Medical Education & Professionalism 7 (4) (2019) 205–212,.
[45]
A.A. Hayat, K. Shateri, M. Amini, N. Shokrpour, Relationships between academic self-efficacy, learning-related emotions, and metacognitive learning strategies with academic performance in medical students: A structural equation model, BMC Medical Education 20 (1) (2020) 76,.
[46]
H. van der Heijden, User acceptance of hedonic information systems, MIS Quarterly 28 (4) (2004) 695–704,.
[47]
M. Jankowska, M. Atlay, Use of creative space in enhancing students' engagement, Innovations in Education & Teaching International 45 (3) (2008) 271–279.
[48]
I. Junglas, C. Abraham, R.T. Watson, Task-technology fit for mobile locatable information systems, Decision Support Systems 45 (4) (2008) 1046–1057.
[49]
G.Y.-M. Kao, C.-A. Ruan, Designing and evaluating a high interactive augmented reality system for programming learning, Computers in Human Behavior 132 (2022).
[50]
K.E. Kariippanon, D.P. Cliff, S.L. Lancaster, A.D. Okely, A.-M. Parrish, Perceived interplay between flexible learning spaces and teaching, learning and student wellbeing, Learning Environments Research 21 (3) (2018) 301–320.
[51]
S. Kesici, I. Sahin, A.O. Akturk, Analysis of cognitive learning strategies and computer attitudes, according to college students' gender and locus of control, Computers in Human Behavior 25 (2) (2009) 529–534,.
[52]
S.E. Kirkley, J.R. Kirkley, Creating next generation blended learning environments using mixed reality, video games and simulations, TechTrends 49 (3) (2005) 42–53.
[53]
C. Lai, M. Gu, Self-regulated out-of-class language learning with technology, Computer Assisted Language Learning 24 (4) (2011) 317–335.
[54]
S. Lakhal, H. Khechine, Student intention to use desktop web-conferencing according to course delivery modes in higher education, International Journal of Management in Education 14 (2) (2016) 146–160.
[55]
O. Lawanto, H.B. Santoso, W. Goodridge, K.N. Lawanto, Task value, self-regulated learning, and performance in a web-intensive undergraduate engineering course: How are they related, Journal of Online Learning and Teaching 10 (1) (2014) 97.
[56]
T.-J. Lin, Exploring the differences in Taiwanese university students' online learning task value, goal orientation, and self-efficacy before and after the COVID-19 outbreak, The Asia-Pacific Education Researcher 30 (3) (2021) 191–203,.
[57]
E.A. Linnenbrink, P.R. Pintrich, Motivation as an enabler for academic success, School Psychology Review 31 (3) (2002) 313–327.
[58]
K. Madhusudhana, The cognitive dimension and course content modeling: An ontological approach, International Journal of Emerging Technologies in Learning (iJET) 12 (5) (2017) 181–188,.
[59]
D. Mahr, J. Heller, K. de Ruyter, Augmented reality (AR): The blurring of reality in human-computer interaction, Computers in Human Behavior 145 (2023),.
[60]
R. Mantooth, E.L. Usher, A.M.A. Love, Changing classrooms bring new questions: Environmental influences, self-efficacy, and academic achievement, Learning Environments Research 24 (3) (2021) 519–535,.
[61]
J. Martín-Gutiérrez, P. Fabiani, W. Benesova, M.D. Meneses, C.E. Mora, Augmented reality to promote collaborative and autonomous learning in higher education, Computers in Human Behavior 51 (2015) 752–761,.
[62]
J. Martín-Gutiérrez, P. Fabiani, W. Benesova, M.D. Meneses, C.E. Mora, Augmented reality to promote collaborative and autonomous learning in higher education, Comput. Hum. Learn. Behav. Collab. Soc. Mob. Netw. Era 51 (2015) 752–761,.
[63]
A.J. Martin, H.W. Marsh, R.L. Debus, Self-handicapping and defensive pessimism: Exploring a model of predictors and outcomes from a self-protection perspective, Journal of Educational Psychology 93 (1) (2001) 87,.
[64]
K.E. Matthews, V. Andrews, P. Adams, Social learning spaces and student engagement, Higher Education Research and Development 30 (2) (2011) 105–120.
[65]
P. Metallidou, A. Vlachou, Children's self-regulated learning profile in language and mathematics: The role of task value beliefs, Psychology in the Schools 47 (8) (2010) 776–788,.
[66]
Y. Min, Y. Zhou, T. Jiang, Y. Wu, Exploring the controlled experiment by social bots, Graph Data Mining: Algorithm, Security and Application (2021) 223–243.
[67]
G. Nasa, Academic self-efficacy: A reliable predictor of educational performances prof. Hemant lata sharma, British Journal of Education 2 (3) (2014) 57–64.
[68]
J. Neroni, C. Meijs, H.J.M. Gijselaers, P.A. Kirschner, R.H.M. de Groot, Learning strategies and academic performance in distance education, Learning and Individual Differences 73 (2019) 1–7,.
[69]
S. Neuville, M. Frenay, E. Bourgeois, Task value, self-efficacy and goal orientations: Impact on self-regulated learning, choice and performance among university students, Psychologica Belgica 47 (1) (2007).
[70]
D. Oblinger, J. Oblinger, Is it age or IT: First steps toward understanding the net generation, Educating the net generation 2 (1–2) (2005) 20.
[71]
P.R. Pintrich, E.V. De Groot, Motivational and self-regulated learning components of classroom academic performance, Journal of Educational Psychology 82 (1) (1990) 33–40.
[72]
P.R. Pintrich, D.A.F. Smith, T. Garcia, W.J. McKeachie, A manual for the use of the motivated Strategies for learning questionnaire (MSLQ) (NCRIPTAL-91-B-004), 1991.
[73]
P.A. Rauschnabel, R. Felix, C. Hinsch, H. Shahab, F. Alt, What is XR? Towards a framework for augmented and virtual reality, Computers in Human Behavior 133 (2022),.
[74]
D. Resnyansky, E. İbili, M. Billinghurst, The potential of augmented reality for computer science education, in: 2018 IEEE international conference on teaching, assessment, and learning for engineering (TALE), 2018.
[75]
D. Sahin, R.M. Yilmaz, The effect of Augmented Reality Technology on middle school students' achievements and attitudes towards science education, Computers & Education 144 (2020).
[76]
D.H. Schunk, Self-efficacy and academic motivation, Educational Psychologist 26 (3–4) (1991) 207–231,.
[77]
C.-w. Shen, J.-t. Ho, P.T.M. Ly, T.-c. Kuo, Behavioural intentions of using virtual reality in learning: Perspectives of acceptance of information technology and learning style, Virtual Reality 23 (3) (2019) 313–324.
[78]
Y. Tang, H. Tseng, X. Tang, The impact of information-seeking self-efficacy and online learning self-efficacy on students' performance proficiency, The Journal of Academic Librarianship 48 (5) (2022),.
[79]
A. Theodoropoulos, G. Lepouras, Augmented reality and programming education: A systematic review, International Journal of Child-Computer Interaction 30 (2021).
[80]
C.L. Thomas, G.M. Pavlechko, J.C. Cassady, An examination of the mediating role of learning space design on the relation between instructor effectiveness and student engagement, Learning Environments Research 22 (1) (2019) 117–131.
[81]
R. Thompson, D. Compeau, C. Higgins, Intentions to use information technologies: An integrative model, Journal of Organizational and End User Computing 18 (3) (2006) 25–46,.
[82]
N. Tuli, G. Singh, A. Mantri, S. Sharma, Augmented reality learning environment to aid engineering students in performing practical laboratory experiments in electronics engineering, Smart Learning Environments 9 (1) (2022) 26,.
[83]
Ö.F. Ursavaş, Y. Yalçın, E. Bakır, The effect of subjective norms on preservice and in‐service teachers' behavioural intentions to use technology: A multigroup multimodel study, British Journal of Educational Technology 50 (5) (2019) 2501–2519.
[84]
A.B. Ustun, E. Simsek, F.G. Karaoglan-Yilmaz, R. Yilmaz, The effects of AR-enhanced English language learning experience on students' attitudes, self-efficacy and motivation, TechTrends 66 (5) (2022) 798–809,.
[85]
S.-L. Wang, P.-Y. Wu, The role of feedback and self-efficacy on web-based learning: The social cognitive perspective, Computers & Education 51 (4) (2008) 1589–1598,.
[86]
P.G. Wicks, P. Reason, Initiating action research: Challenges and paradoxes of opening communicative space, Sage Publications Sage UK, London, England, 2009.
[87]
A. Wigfield, The role of children's achievement values in the self-regulation of their learning outcomes, in: D.H. Schunk, B.J. Zimmerman (Eds.), Self-regulationof learning and performance: Issues and educational applications, L. Erlbaum Associates, 1994, pp. 101–124.
[88]
M. Yamada, A. Shimada, F. Okubo, M. Oi, K. Kojima, H. Ogata, Learning analytics of the relationships among self-regulated learning, learning behaviors, and learning performance, Research and Practice in Technology Enhanced Learning 12 (1) (2017) 13,.
[89]
G. Yi-Ming Kao, C.-A. Ruan, Designing and evaluating a high interactive augmented reality system for programming learning, Computers in Human Behavior 132 (2022),.
[90]
G. Yusri, N.M. Rahimi, P.M. Shah, W.H. Wah, Cognitive and metacognitive learning strategies among Arabic language students, Interactive Learning Environments 21 (3) (2013) 290–300,.
[91]
N. Zanjani, S.L. Edwards, S. Nykvist, S. Geva, The important elements of LMS design that affect user engagement with e-learning tools within LMSs in the higher education sector, Australasian Journal of Educational Technology 33 (1) (2017) 19–31,.
[92]
J. Zhang, A. Ogan, T.-C. Liu, Y.-T. Sung, K.-E. Chang, The influence of using augmented reality on textbook support for learners of different learning styles, in: 2016 IEEE international symposium on mixed and augmented reality (ISMAR), 2016.
[93]
J. Zumbach, L. Rammerstorfer, I. Deibl, Cognitive and metacognitive support in learning with a serious game about demographic change, Computers in Human Behavior 103 (2020) 120–129,.

Cited By

View all

Index Terms

  1. Exploring the impact of augmented reality on student academic self-efficacy in higher education
        Index terms have been assigned to the content through auto-classification.

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image Computers in Human Behavior
        Computers in Human Behavior  Volume 149, Issue C
        Dec 2023
        493 pages

        Publisher

        Elsevier Science Publishers B. V.

        Netherlands

        Publication History

        Published: 01 December 2023

        Author Tags

        1. Academic self-efficacy
        2. Augmented reality
        3. Conceptual model
        4. Cognitive strategies
        5. Learning space

        Qualifiers

        • Research-article

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • 0
          Total Citations
        • 0
          Total Downloads
        • Downloads (Last 12 months)0
        • Downloads (Last 6 weeks)0
        Reflects downloads up to 08 Feb 2025

        Other Metrics

        Citations

        Cited By

        View all

        View Options

        View options

        Figures

        Tables

        Media

        Share

        Share

        Share this Publication link

        Share on social media