Students’ Academic Use of Mobile Technology and Higher-Order Thinking Skills: The Role of Active Engagement
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The academic use of mobile technology engages students beyond traditional classroom contexts. Over the past few years, higher education institutions have promoted students’ learning and growth by supporting their use of mobile technology. Mobile technology offers educational possibilities that can enhance students’ growth in higher education. The aim of this study is to examine the relationship between college students’ academic use of mobile technology and higher-order thinking skills through their active engagement and learning effort. The sample consisted of 456 students at a university in South Korea, and the data were analyzed using partial least squares structural equation modeling. The results suggest that the academic use of mobile technology influences students’ higher-order thinking skills directly, in addition to their learning effort and active engagement in courses. These findings provide valuable information for higher education institutions that seek to introduce interactive and technology-integrated environments.
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HOTS can be developed through active engagement in educational activities.
“Kim et al. (2020) argued that HOTS can be developed through active engagement in educational activities.”
From Incorporating Computational Thinking Into Virtual Laboratories to Enhance Learning Motivation, Engagement, and Higher‐Order Thinking Skills (2025) · cites this paper as Kim et al. (2020)
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Cites
A Paradigm for Developing Better Measures of Marketing Constructs
Churchill, G. A. (1979) · Journal of Marketing Research · doi:10.1177/002224377901600110 · Cited by 16,549 · see citing works
Sung, Y.-T., Chang, K.-E., & Liu, T.-C. (2016) · Computers & Education · doi:10.1016/j.compedu.2015.11.008 · Cited by 1,574 · see citing works
Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results
Streukens, S., & Leroi-Werelds, S. (2016) · European Management Journal · doi:10.1016/j.emj.2016.06.003 · Cited by 1,120 · see citing works
Computer-based technology and student engagement: a critical review of the literature
Schindler, L. A., Burkholder, G. J., Morad, O. A., & Marsh, C. (2017) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-017-0063-0 · Cited by 475 · see citing works
Chen, Y.-F., & Peng, S. S. (2008) · CyberPsychology & Behavior · doi:10.1089/cpb.2007.0128 · Cited by 256 · see citing works
Flagged first, then by citations
Research works printed but not linked: 70
- Han, I.; Shin, W.S. The use of a mobile learning management system and academic achievement of online students. Comput. Educ. 2016, 102, 79-89. [CrossRef] · DOI
- Fu, Q.K.; Hwang, G.J. Trends in mobile technology-supported collaborative learning: A systematic review of journal publications from 2007 to 2016. Comput. Educ. 2018, 119, 129-143.
- Morris, N.P.; Lambe, J.; Ciccone, J.; Swinnerton, B. Mobile technology: Students perceived benefits of apps for learning neuroanatomy. J. Comput. Assist. Learn. 2016, 32, 430-442. [CrossRef] · DOI
- Shamir-Inbal, T.; Blau, I. Developing digital wisdom by students and teachers: The impact of integrating tablet computers on learning and pedagogy in an elementary school. J. Educ. Comput. Res. 2016, 54, 967-996.
- Uzun, A.M.; Kilis, S. Does persistent involvement in media and technology lead to lower academic performance? Evaluating media and technology use in relation to multitasking, self-regulation and academic performance. Comput. Human. Behav. 2019, 90, 196-203.
- Schmid, R.; Petko, D. Does the use of educational technology in personalized learning environments correlate with self-reported digital skills and beliefs of secondary-school students? Comput. Educ. 2019, 136, 75-86. [CrossRef] · DOI · Paper page
- Williams, K.M.; Stafford, R.E.; Corliss, S.B.; Reilly, E.D. Examining student characteristics, goals, and engagement in Massive Open Online Courses. Comput. Educ. 2018, 126, 433-442.
- Johnson, L.; Adams Becker, S.; Cummins, M.; Estrada, V.; Freeman, A.; Ludgate, H. NMC Horizon Report: 2013 Higher Education Edition; The New Media Consortium: Austin, TX, USA, 2013.
- Woodcock, B.; Middleton, A.; Nortcliffe, A. Considering the smartphone learner: An investigation into student interest in the use of personal technology to enhance their learning. Stud. Engagem. Exp. J. 2012, 1, 1-15.
- Sánchez-Prieto, J.C.; Hernández-García, Á.; García-Peñalvo, F.J.; Chaparro-Peláez, J.; Olmos-Migueláñez, S. Break the walls! Second-order barriers and the acceptance of mLearning by first-year pre-service teachers. Comput. Human. Behav. 2019, 95, 158-167. [CrossRef] · DOI
- Burden, K.; Kearney, M.; Schuck, S.; Hall, T. Investigating the use of innovative mobile pedagogies for school-aged students: A systematic literature review. Comput. Educ. 2019, 138, 83-100. [CrossRef] · DOI
- Chen, F.; Sager, J. The effects of tablet PC use in the classroom on teaching and learning processes. J. Learn. High. Educ. 2011, 7, 55-67.
- Negahban, A.; Chung, C.H. Discovering determinants of users perception of mobile device functionality fit. Comput. Human. Behav. 2014, 35, 75-84. [CrossRef] · DOI
- Beetham, H.; Sharpe, R. Rethinking Pedagogy for a Digital Age: Designing for 21st Century Learning: Rethinking Pedagogy for a Digital Age, 2nd ed.; Routledge: New York, NY, USA, 2013.
- Tapscott, D. Grown up Digital: How the Net Generation is Changing Your World; McGraw-Hill: New York, NY, USA, 2009.
- Rashid, T.; Asghar, H.M. Technology use, self-directed learning, student engagement and academic performance: Examining the interrelations. Comput. Human. Behav. 2016, 63, 604-612. [CrossRef] · DOI · Paper page
- Kearney, M.; Schuck, S.; Burden, K.; Aubusson, P. Viewing mobile learning from a pedagogical perspective. Res. Learn. Technol. 2012, 20. [CrossRef] · DOI
- O'Bannon, B.W.; Thomas, K. Teacher perceptions of using mobile phones in the classroom: Age matters! Comput. Educ. 2014, 74, 15-25. [CrossRef] · DOI
- Fitch, J.L. Student feedback in the college classroom: A technology solution. Educ. Technol. Res. Dev. 2004, 52, 71-77. [CrossRef] · DOI
- Barak, M.; Lipson, A.; Lerman, S. Wireless laptops as means for promoting active learning in large lecture halls. J. Res. Technol. Educ. 2006, 38, 245-263. [CrossRef] · DOI · Paper page
- Siegle, D.; Foster, T. Laptop computers and multimedia and presentation software: Their effects on student achievement in anatomy and physiology. J. Res. Technol. Educ. 2001, 34, 29-37.
- Yang, S.H. Exploring college students' attitudes and self-efficacy of mobile learning. Turk. Online J. Educ. Technol. 2012, 11, 148-154.
- Anderson, L.W.; Krathwohl, D.R. A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives; Addison Wesley Longman: New York, NY, USA, 2001.
- Ramos, J.L.S.; Dolipas, B.B.; Villamor, B.B. Higher order thinking skills and academic performance in physics of college students: A regression analysis. Int. J. Innov. Interdiscip. Res. 2013, 4, 48-60.
- Goodfellow, R.; Lea, M.R. Literacy in the Digital University: Critical Perspectives on Learning, Scholarship, and Technology; Routledge: London, UK, 2013.
- Echenique, E.G.; Molías, L.M.; Bullen, M. Students in higher education: Social and academic uses of digital technology. Int. J. Educ. Technol. High. Educ. 2015, 12, 25-37.
- Guzmán-Simón, F.; García-Jiménez, E.; López-Cobo, I. Undergraduate students' perspectives on digital competence and academic literacy in a Spanish University. Comput. Hum. Behav. 2017, 74, 196-204.
- Margaryan, A.; Littlejohn, A.; Vojt, G. Are digital natives a myth or reality? University students' use of digital technologies. Comput. Educ. 2011, 56, 429-440. [CrossRef] · DOI
- Kuh, G.D.; Vesper, N. Do computers enhance or detract from student learning? Res. High. Educ. 2001, 42, 87-102. [CrossRef] · DOI
- Lee, J.; Choi, H. What affects learner's higher-order thinking in technology-enhanced learning environments? The effects of learner factors. Comput. Educ. 2017, 115, 143-152.
- Barak, M.; Dori, Y.J. Enhancing higher order thinking skills among inservice science teachers via embedded assessment. J. Sci. Teacher Educ. 2009, 20, 459-474. [CrossRef] · DOI
- Barak, M.; Levenberg, A. Flexible thinking in learning: An individual differences measure for learning in technology-enhanced environments. Comput. Educ. 2016, 99, 39-52. [CrossRef] · DOI
- Marra, R.; Palmer, B. Encouraging intellectual growth: Senior college student profiles. J. Adult Dev. 2004, 11, 111-122.
- Pike, G.R. Using college students' self-reported learning outcomes in scholarly research. New. Direct. Inst. Res. 2011, 41-58. [CrossRef] · DOI
- Coates, H. Student Engagement in Campus-Based and Online Education: University Connections; Routledge: New York, NY, USA, 2006.
- Cheng, M.T.; Lin, Y.W.; She, H.C. Learning through playing Virtual Age: Exploring the interactions among student concept learning, gaming performance, in-game behaviors, and the use of in-game characters. Comput. Educ. 2015, 86, 18-20.
- Fonseca, D.; Martí, N.; Redondo, E.; Navarro, I.; Sánchez, A. Relationship between student profile, tool use, participation, and academic performance with the use of augmented reality technology for visualized architecture models. Comput. Human. Behav. 2014, 31, 434-445.
- Crawford, C.M.; Smith, M.S. Rethinking Bloom's taxonomy: Implicit cognitive vulnerability as an impetus towards higher order thinking skills. In Exploring Implicit Cognition: Learning, Memory, and Social Cognitive Processes; IGI Global: Hershey, PA, USA, 2015; pp. 86-103. [CrossRef] · DOI
- Tendhar, C.; Culver, S.M.; Burge, P.L. Validating the National Survey of Student Engagement (NSSE) at a research-intensive university. J. Educ. Train. Stud. 2013, 1, 182-193.
- Fensham, P.J.; Bellocchi, A. Higher order thinking in chemistry curriculum and its assessment. Think. Ski. Creat. 2013. [CrossRef] · DOI
- Zohar, A.; Dori, Y.J. Higher order thinking skills and low-achieving students: Are they mutually exclusive? J. Learn. Sci. 2003, 12, 145-181. [CrossRef] · DOI
- Atkinson, R.C.; Geiser, S. Reflections on a century of college admissions tests. Edu. Res. 2009, 38, 665-676.
- Song, Y. "Bring Your Own Device (BYOD)" for seamless science inquiry in a primary school. Comput. Educ. 2014, 74, 50-60.
- Trimmel, M.; Bachmann, J. Cognitive, social, motivational and health aspects of students in laptop classrooms. J. Comput. Assist. Learn. 2004, 20, 151-158.
- Ranieri, M.; Raffaghelli, J.E.; Bruni, I. Game-based student response system: Revisiting its potentials and criticalities in large-size classes. Act. Learn. High. Educ. 2018. [CrossRef] · DOI · Paper page
- Drain, T.S.; Grier, L.E.; Sun, W. Is the growing use of electronic devices beneficial to academic performance? Results from archival data and a survey. Issues Inf. Syst. 2012, 13, 225-231.
- Carr, R.; Palmer, S.; Hagel, P. Active learning: The importance of developing a comprehensive measure. Act. Learn. High. Educ. 2015, 16, 173-186. [CrossRef] · DOI
- Krause, K.-L.; Coates, H. Students' engagement in first-year university. Assess. Eval. High. Educ. 2008, 33, 493-505. [CrossRef] · DOI · Paper page
- Kuh, G.D. How to help students achieve. Chron. High. Educ. 2007, 53, 12-13.
- Rau, W.; Durand, A. The academic ethic and college grades: Does hard work help students to "make the grade"? Sociol. Educ. 2000, 73, 19-38. [CrossRef] · DOI
- Schuman, H.; Walsh, E.; Olson, C.; Etheridge, B. Effort and reward: The assumption that college grades are affected by quantity of study. Soc. Forces 1985, 63, 945-966. [CrossRef] · DOI
- Kuh, G.D. What student affairs professionals need to know about student engagement. J. Coll. Stud. Dev. 2009, 50, 683-706. [CrossRef] · DOI
- Richter, D.; Lehrl, S.; Weinert, S. Enjoyment of learning and learning effort in primary school: The significance of child individual characteristics and stimulation at home and at preschool. Early Child Dev. Care 2016, 186, 96-116. [CrossRef] · DOI
- Franklin, M. The effects of differential college environments on academic learning and student perceptions of cognitive development. Res. High. Educ. 1995, 36, 127-153. [CrossRef] · DOI
- Hoshower, L.; Chen, Y. Persuading students of their responsibilities in the learning process. Coll. Teach. Learn. 2005, 2, 7-16. [CrossRef] · DOI
- Rodgers, T. Student engagement in the e-learning process and the impact on their grades. Int. J. Cyber Soc. Educ. 2008, 1, 143-156.
- Plant, E.A.; Ericsson, K.A.; Hill, L.; Asberg, K. Why study time does not predict grade point average across college students: Implications of deliberate practice for academic performance. Contemp. Educ. Psychol. 2005, 30, 96-116. [CrossRef] · DOI · Paper page
- Heiphetz, A. How Mobile Technology can Enhance Student Learning and Workforce Training; McGraw-Hill: New York, NY, USA, 2011.
- Kennedy, G.E.; Judd, T.S.; Churchward, A.; Gray, K.; Krause, K.L. First year students' experiences with technology: Are they really digital natives? Australas. J. Educ. Technol. 2008, 24, 108-122. [CrossRef] · DOI
- Chin, W.W. The partial least squares approach to structural equation modeling. Mod. Methods Bus. Res. 1998, 295, 295-336.
- Hair, J.F.J.; Hult, G.T.M.; Ringle, C.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd ed.; Sage Publications: Thousand Oaks, CA, USA, 2016.
- Henseler, J.; Ringle, C.M.; Sinkovics, R.R. The use of partial least squares path modeling in international marketing. In Advances in International Marketing; Sinkovics, R.R., Ghauri, P.N., Eds.; Emerald: Bingley, UK, 2009; pp. 277-320.
- Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39-50. [CrossRef] · DOI · Paper page
- Tenenhaus, M.; Vinzi, V.E.; Chatelin, Y.M.; Lauro, C. PLS path modeling. Comput. Stat. Data Anal. 2005, 48, 159-205. [CrossRef] · DOI · Paper page
- Nitzl, C.; Roldan, J.L.; Cepeda, G. Mediation analysis in partial least squares path modelling, Helping researchers discuss more sophisticated models. Ind. Manag. Data Syst. 2016, 116, 1849-1864. [CrossRef] · DOI
- Scornavacca, E.; Huff, S.; Marshall, S. Mobile phones in the classroom: If you can't beat them, join them. Commun. ACM 2009, 52, 142-146. [CrossRef] · DOI
- Chan, S.C.H.; Wan, J.C.L.; Ko, S. Interactivity, active collaborative learning, and learning performance: The moderating role of perceived fun by using personal response systems. Int. J. Manag. Educ. 2019, 17, 94-102. [CrossRef] · DOI
- Biggs, J.B. Approaches to the enhancement of tertiary teaching. High. Educ. Res. Dev. 1989, 8, 7-26. [CrossRef] · DOI
- Kobus, M.B.W.; Rietveld, P.; Van Ommeren, J.N. Ownership versus on-campus use of mobile IT devices by university students. Comput. Educ. 2013, 68, 29-41. [CrossRef] · DOI
- Sharples, M.; Scanlon, E.; Ainsworth, S.; Anastopoulou, S.; Collins, T.; Crook, C.; Jones, A.; Kerawalla, L.; Littleton, K.; Mulholland, P.; et al. Personal inquiry: Orchestrating science investigations within and beyond the classroom. J. Learn. Sci. 2015, 24, 308-341. [CrossRef] · DOI
Cited by
Sorted by citations
Wu, T., Sarwono, E., & Huang, Y. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70017 · Cited by 9 · see citing works
- Authors
- Kim, H. J., Yi, P., & Hong, J. I.
- Affiliations
- Chung-Ang University (KR)
- Hongik University (KR)
- Corresponding author
- Yi, Pilnam
- Venue
- Education Sciences
- Publisher
- MDPI AG
- Published
- 2020-02-25
- Volume
- 10
- Issue
- 3
- Pages
- 47
- ISSN
- 2227-7102 (ISSN-L)
- OpenAlex
- W3006736586
- Cited by
- 78
- References
- 5
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 17 · 2023: 19 · 2022: 16 · 2021: 8 · 2020: 2
- FWCI
- 10.59
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Education Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Research Foundation of Korea (NRF-2017S1A3A2066878)
- Topics
- Physical Sciences › Computer Science › Information Systems › Mobile Learning in Education
- Physical Sciences › Computer Science › Information Systems › Educational Technology in Learning
- Social Sciences › Education › Technology-Enhanced Education Studies
- Keywords
- mobile technology, higher-order thinking skills, active engagement, learning effort, university students, technology-enhanced learning, higher education
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