Abstract
Technological revolutionary changes have boosted mobile learning’s evolution from supplementary material for teaching to a flexible, strategic, and convenient resource, driving new paths in higher education. With global increases in wireless internet access and the advent of highly functional smartphones and tablets, which have impacted the rise in mobile device ownership, mobile learning has expanded its applications as a direct way to implement tailored learning settings. Notably, during the COVID-19 pandemic, together with other educational technologies, it became a solicited tool in remote education. In this systematic review, we will explore how educators and researchers have been documenting the development and impact of mobile learning tools in the teaching and learning process since the COVID-19 outbreak. Results show that, embedded with online higher education programs, mobile learning has empowered interaction in content creation, communication, and collaboration between learners and instructors, significantly impacting learning effectiveness. Moreover, although this technology is well established in higher education, it remains attractive for educators who actively use it because of its pedagogic potential.
Article PDF
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Adanır, A., & Muhametjanova, G. (2021). University students’ acceptance of mobile learning: A comparative study in Turkey and Kyrgyzstan. Education and Information Technologies, 26(5), 6163–6181. https://doi.org/10.1007/S10639-021-10620-1/TABLES/5
Akour, I., Alshurideh, M., Kurdi, B. A., Ali, A., & Salloum, S. (2021). Using Machine Learning Algorithms to Predict People’s Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach. JMIR Medical Education, 7(1), e24032. https://doi.org/10.2196/24032
Almaiah, M. A., Almomani, O., Al-Khasawneh, A., & Althunibat, A. (2021). Predicting the Acceptance of Mobile Learning Applications During COVID-19 Using Machine Learning Prediction Algorithms. Studies in Systems, Decision and Control (Vol. 348, pp. 319–332). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-67716-9_20
Almaiah, M. A., Ayouni, S., Hajjej, F., Lutfi, A., Almomani, O., & Awad, A. B. (2022). Smart Mobile Learning Success Model for Higher Educational Institutions in the Context of the COVID-19 Pandemic. Electronics, 11(8), 1278. https://doi.org/10.3390/ELECTRONICS11081278
Althunibat, A., Almaiah, M. A., & Altarawneh, F. (2021). Examining the Factors Influencing the Mobile Learning Applications Usage in Higher Education during the COVID-19 Pandemic. Electronics, 10(21). https://doi.org/10.3390/electronics10212676
Alturki, U., & Aldraiweesh, A. (2022). Students’ Perceptions of the Actual Use of Mobile Learning during COVID-19 Pandemic in Higher Education. Sustainability, 2022(3), 1125–1125. https://doi.org/10.3390/SU14031125
Antee, A. (2021). Student perceptions and mobile technology adoption: implications for lower-income students shifting to digital. Educational Technology Research and Development, 69(1), 191–194. https://doi.org/10.1007/s11423-020-09855-5
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/J.JOI.2017.08.007
Bahia, K., & Delaporte, A. (2020). The State of Mobile Internet Connectivity 2020. GSMA Reports.
Becke, R. A., Wilks, A. R., Brownrigg, R., Minka, T. P., & Deckmyn, A. (2021). Draw Geographical Maps (Package ‘maps’) (3.4.0). Repository CRAN.
Bernacki, M. L., Crompton, H., & Greene, J. A. (2020). Towards convergence of mobile and psychological theories of learning. Contemporary Educational Psychology, 60, 101828. https://doi.org/10.1016/J.CEDPSYCH.2019.101828c
Borroto, G., Medina Olazabal, B., I and Sánchez Mesa, & Fonseca Montes E Oca, L. (2021). Online teaching tasks in the subjects biology and Spanish as a foreign language. Campus Virtuales, 10(1), 163–172.
Cavus, N. (2020). Evaluation of MoblrN m-learning system: Participants’ attitudes and opinions. World Journal on Educational Technology: Current Issues, 12(3), 150–164. https://doi.org/10.18844/WJET.V12I3.4978
Chen, Y. C., Fan, K. K., & Fang, K. T. (2021). Effect of flipped teaching on cognitive load level with mobile devices: The case of a graphic design course. Sustainability(13), 13–13. https://doi.org/10.3390/su13137092
Chessa, M., & Solari, F. (2021). The sense of being there during online classes: analysis of usability and presence in web-conferencing systems and virtual reality social platforms. Behaviour & Information Technology, 40(12), 1237–1249.
Chu, Y. B. (2022). A mobile augmented reality system to conduct electrical machines laboratory for undergraduate engineering students during the COVID pandemic. Education and Information Technologies. Education and Information Technologies, 27, 8519–8532.
Cohen, L., Manion, L., & Morrison, K. (2018). Research Methods in Education. Routledge.
Coskun-Setirek, A., & Tanrikulu, Z. (2021). M-Universities: Critical Sustainability Factors. SAGE Open, 11(1). https://doi.org/10.1177/2158244021999388
Couso, D. (2016). Participatory Approaches to Curriculum Design From a Design Research Perspective. In D. Psillos & P. Kariotoglou (Eds.), Iterative Design of Teaching-Learning Sequences (pp. 47–71). Springer Netherlands. https://doi.org/10.1007/978-94-007-7808-5_4
Crebert, G., Bates, M., Bell, B., Patrick, C. J., & Cragnolini, V. (2004). Developing generic skills at university, during work placement and in employment: Graduates’ perceptions. Higher Education Research and Development, 23(2), 147–165. https://doi.org/10.1080/0729436042000206636
Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
Crompton, H., & Burke, D. (2017). The use of mobile learning in higher education: A systematic review. Computers and Education, 123, 53–64. https://doi.org/10.1016/j.compedu.2018.04.007
Csárdi, G. N. T., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1–9.
Cui, K. (2022). Artificial intelligence and creativity: piano teaching with augmented reality applications. Interactive Learning Environments, 1–12. https://doi.org/10.1080/10494820.2022.2059520
Dhawan, S. (2020). Online Learning: A Panacea in the Time of COVID-19 Crisis. Journal of Educational Technology Systems, 49(1), 5–22. https://doi.org/10.1177/0047239520934018
Diaz-Nunez, C., Sanchez-Cochachin, G., Ricra-Chauca, Y., Andrade-Arenas, L., Diaz-Núñez, C., Sanchez-Cochachin, G., … Ricra-Chauca, Y. (2021). Impact of Mobile Applications for a Lima University in Pandemic. International Journal of Advanced Computer Science and Applications, 12(2), 752–758.
Ding, Y., Li, Y., & Cheng, L. (2020). Application of Internet of Things and Virtual Reality Technology in College Physical Education. IEEE Access, 8, 96065–96074. https://doi.org/10.1109/ACCESS.2020.2992283
Educause. (2019). 2019 EDUCAUSE Horizon Report. Higher Education Edition.
Egilsdottir, H. Ö., Heyn, L. G., Brembo, E. A., Byermoen, K. R., Moen, A., & Eide, H. (2021). Configuration of mobile learning tools to support basic physical assessment in nursing education: Longitudinal participatory design approach. JMIR MHealth and UHealth, 9(1). https://doi.org/10.2196/22633
Eldokhny, A. A., & Drwish, A. M. (2021). Effectiveness of Augmented Reality in Online Distance Learning at the Time of the COVID-19 Pandemic. International Journal of Emerging Technologies in Learning, 16(9), 198–218. https://doi.org/10.3991/IJET.V16I09.17895
Fan, M., Ndavi, J. W., Qalati, S. A., Huang, L., & Zhengjia, P. (2022). Applying the time continuum model of motivation to explain how major factors affect mobile learning motivation: a comparison of SEM and fsQCA. Online Information Review. https://doi.org/10.1108/OIR-04-2021-0226/FULL/XML
Grames, E. M., Stillman, A. N., Tingley, M. W., & Elphick, C. S. (2019). An automated approach to identifying search terms for systematic reviews using keyword co-occurrence networks. Methods in Ecology and Evolution, 10(10), 1645–1654. https://doi.org/10.1111/2041-210X.13268
GSMA. (2022). The Mobile Economy 2022.
Gupta, Y., Khan, F. M., & Agarwal, S. (2021). Exploring Factors Influencing Mobile Learning in Higher Education - A Systematic Review. International Journal of Interactive Mobile Technologies (IJIM), 15(12), 140–140. https://doi.org/10.3991/ijim.v15i12.22503
Gurevych, R., Silveistr, A., Mokliuk, M., Shaposhnikova, I., Gordiichuk, G., & Saiapina, S. (2021). Using Augmented Reality Technology in Higher Education Institutions. Postmodern Openings, 12(2), 109–132. https://doi.org/10.18662/PO/12.2/299
Hao, S., Dennen, V. P., & Mei, L. (2017). Influential factors for mobile learning acceptance among Chinese users. Educational Technology Research and Development, 65(1), 101–123. https://doi.org/10.1007/S11423-016-9465-2/TABLES/7
Herrador-Alcaide, T. C., Hernández-Solís, M., Hontoria, J. F., Hernandez-Solis, M., Hontoria, J. F., Hernández-Solís, M., & Hontoria, J. F. (2020). Online Learning Tools in the Era of m-Learning: Utility and Attitudes in Accounting College Students. Sustainability, 12–12. https://doi.org/10.3390/su12125171
Huang, D. H., & Chueh, H. E. (2022). Behavioral intention to continuously use learning apps: A comparative study from Taiwan universities. Technological Forecasting and Social Change, 177. https://doi.org/10.1016/j.techfore.2022.121531
Humida, T., Mamun, M. H. A., & Keikhosrokiani, P. (2021). Predicting behavioral intention to use e-learning system: A case-study in Begum Rokeya University, Rangpur, Bangladesh. Education and Information Technologies, 27, 2241–2265. https://doi.org/10.1007/s10639-021-10707-9
Iqbal, M. Z., Alradhi, H. I., Alhumaidi, A. A., Alshaikh, K. H., Alobaid, A. M., Alhashim, M. T., & Alsheikh, M. H. (2020). Telegram as a tool to supplement online medical education during covid-19 crisis. Acta Informatica Medica, 28(2), 94–97. https://doi.org/10.5455/aim.2020.28.94-97
Kayaalp, F., & Dinc, F. (2022). A mobile app for algorithms learning in engineering education: Drag and drop approach. Computer Applications in Engineering Education, 30(1), 235–250.
Kemp, S. (2022). Digital 2022 July Global Statshot Report.
Kumar, J. A., Osman, S., Mesquita, D., Lima, R. M., Kumar, J., Osman, S., … Rasappan, R. (2022). Mobile Learning Acceptance Post Pandemic: A Behavioural Shift among Engineering Undergraduates. Sustainability 2022, 14(6), 3197–3197. https://doi.org/10.3390/SU14063197
Lan, E. M. (2022). A comparative study of computer and mobile-assisted pronunciation training: The case of university students in Taiwan. Education and Information Technologies, 27(2), 1559–1583. https://doi.org/10.1007/s10639-021-10647-4
Laurens-Arredondo, L. (2022). Mobile augmented reality adapted to the ARCS model of motivation: a case study during the COVID-19 pandemic. Education and Information Technologies, 27, 7927–7946. https://doi.org/10.1007/S10639-022-10933-9
Liaw, S. S., Hatala, M., & Huang, H. M. (2010). Investigating acceptance toward mobile learning to assist individual knowledge management: Based on activity theory approach. Computers & Education, 54(2), 446–454.
Lin, Y., Liu, Y., Fan, W., Tuunainen, V. K., & Deng, S. (2021). Revisiting the relationship between smartphone use and academic performance: A large-scale study. Computers in Human Behavior, 122. https://doi.org/10.1016/j.chb.2021.106835
Loh, X. K., Lee, V. H., Loh, X. M., Tan, G. W. H., Ooi, K. B., & Dwivedi, Y. K. (2021). The Dark Side of Mobile Learning via Social Media: How Bad Can It Get? Information Systems Frontiers, 24, 1887–1904. https://doi.org/10.1007/s10796-021-10202-z
Mât, ă, L., Clipa, O., Cojocariu, V. M., Robu, V., Dobrescu, T., Hervás-Gómez, C., & Stoica, I. V. (2021). Students’ Attitude towards the Sustainable Use of Mobile Technologies in Higher Education. Sustainability, 13(11), 5923. https://doi.org/10.3390/SU13115923
Márquez-Díaz, J. E. (2020). Virtual World as a Complement to Hybrid and Mobile Learning. International Journal of Emerging Technologies in Learning, 15(22), 267–274.
Matzavela, V., & Alepis, E. (2021). M-learning in the COVID-19 era: physical vs digital class. Education and Information Technologies, 26(6), 7183–7203. https://doi.org/10.1007/S10639-021-10572-6/FIGURES/3
Minichiello, A., Armijo, D., Mukherjee, S., Caldwell, L., Kulyukin, V., Truscott, T., … Bhouraskar, A. (2021). Developing a mobile application-based particle image velocimetry tool for enhanced teaching and learning in fluid mechanics: A design-based research approach. Computer Applications in Engineering Education, 29(3), 517–537. https://doi.org/10.1002/CAE.22290
Mir, S. B., & Llueca, G. F. (2020). Introduction to Programming Using Mobile Phones and MIT App Inventor. Revista Iberoamericana de Tecnologias Del Aprendizaje, 15(3), 192–201. https://doi.org/10.1109/RITA.2020.3008110
Motiwalla, L. F. (2007). Mobile learning: A framework and evaluation. Computers & Education, 49(3), 581–596. https://doi.org/10.1016/J.COMPEDU.2005.10.011
Mubayrik, H. F., Bin, & Alabbad, A. H. (2021). Applications of Mobile Learning and Transactional Distance Theory in the Context of Higher Education during COVID-19 Pandemic. International Journal of Educational Sciences, 34(1-3), 1–10. https://doi.org/10.31901/24566322.2021/34.1-3.1190
Muthuprasad, T., Aiswarya, S., Aditya, K. S., & Jha, G. K. (2021). Students’ perception and preference for online education in India during COVID -19 pandemic. Social Sciences & Humanities Open, 3(1), 100101.
Navandar, A., López, D. F., & Alejo, L. B. (2021). The Use of Instagram in the Sports Biomechanics Classroom. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.711779
Neffati, O. S., Setiawan, R., Jayanthi, P., Vanithamani, S., Sharma, D. K., Regin, R., … Sengan, S. (2021). An educational tool for enhanced mobile e-Learning for technical higher education using mobile devices for augmented reality. Microprocessors and Microsystems, 83. https://doi.org/10.1016/j.micpro.2021.104030
O’Connor, S., & Andrews, T. (2018). Smartphones and mobile applications (apps) in clinical nursing education: A student perspective. Nurse Education Today, 69, 172–178. https://doi.org/10.1016/J.NEDT.2018.07.013
Page, M. J., Mckenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Journal of Clinical Epidemiology, 134, 178–189. https://doi.org/10.1016/j.jclinepi.2021.03.001
Paré, G., Trudel, M. C., Jaana, M., & Kitsiou, S. (2015). Synthesizing information systems knowledge: A typology of literature reviews. Information & Management, 52(2), 183–199. https://doi.org/10.1016/J.IM.2014.08.008
Pedersen, T. L. (2021). Package “ggraph” Type Package Title An Implementation of Grammar of Graphics for Graphs and Networks.
Penuel, W. R., Allen, A. R., Henson, K., Campanella, M., Patton, R., Rademaker, K., … Zivic, A. (2022). Learning Practical Design Knowledge through Co-Designing Storyline Science Curriculum Units. Cognition and Instruction, 40, 148–170. https://doi.org/10.1080/07370008.2021.2010207
Pham, X. L., & Chen, G. D. (2018). PACARD: A New Interface to Increase Mobile Learning App Engagement, Distributed Through App Stores. Information for Journal of Educational Computing Research, 57(3), 618–645. https://doi.org/10.1177/0735633118756298
Pramana, C., Susanti, R., Violinda, Q., Yoteni, F., Rusdiana, E., Prihanto, Y. J. N., … Haimah (2020). Virtual learning during the covid-19 pandemic, a disruptive technology in higher education in indonesia. International Journal of Pharmaceutical Research, 12(2), 3209–3216. https://doi.org/10.31838/IJPR/2020.12.02.430
Radianti, J., Majchrzak, T. A., Fromm, J., & Wohlgenannt, I. (2020). A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda. Computers and Education, 147, 103778. https://doi.org/10.1016/j.compedu.2019.103778
Rangel-de Lázaro, G., & Duart, J. M. (2023). You Can Handle, You Can Teach It: Systematic Review on the Use of Extended Reality and Artificial Intelligence Technologies for Online Higher Education. Sustainability, 15(4). https://doi.org/10.3390/su15043507
Rodríguez Muñoz, R., & Formoso Mieres, A. A. (2020). Efectos de YouTube y WhatsApp en procesos de enseñanza - aprendizaje ante el nuevo coronavirus. Revista Conrado, 16(77), 346–353.
Romero-Rodriguez, J. M., Aznar-Diaz, I., Hinojo-Lucena, F. J., & Gomez-Garcia, G. (2020). Mobile Learning in Higher Education: Structural Equation Model for Good Teaching Practices. IEEE Access, 8, 91761–91769.
Rose, S., Engel, D., Cramer, N., & Cowley, W. (2010). Automatic key-word extraction from individual documents. In M. W. Berry & J. Kogan (Eds.), Text mining (pp. 1–20). John Wiley & Sons Ltd.
Salas-Rueda, R. A., Ramírez-Ortega, J., Eslava-Cervantes, A. L., Castañeda-Martínez, R., & De-La-Cruz-Martínez, G. (2022). Percepción de los profesores sobre los juegos web y dispositivos móviles en el nivel educativo superior durante la pandemia COVID-19. Texto Livre, 15, e37074. https://doi.org/10.35699/1983-3652.2022.37074
Sarkadi, A. R., Cahyana, U., & Paristiowati, M. (2020). The Application of Mobile Learning for University Students in the Pancasila Education Modul in Developing Character of Students Empathy. Universal Journal of Educational Research, 8(9), 3825–3833. https://doi.org/10.13189/ujer.2020.080905
Singh, R., Timbadia, D., Kapoor, V., Reddy, R., Churi, P., & Pimple, O. (2021). Question paper generation through progressive model and difficulty calculation on the Promexa Mobile Application. Education and Information Technologies, 26(4), 4151–4179. Retrieved from https://doi.org/10.1007/s10639-021-10461-y https://doi.org/10.1007/s10639-021-10461-y
Sooryah, N., & Soundarya, K. R. (2020). Live Captioning for Live Lectures - An Initiative to Enhance Language Acquisition in Second Language Learners, through Mobile Learning. Webology, 17(2), 238–245. https://doi.org/10.14704/WEB/V17I2/WEB17027
Sprenger, D. A., & Schwaninger, A. (2021). Technology acceptance of four digital learning technologies (classroom response system, classroom chat, e-lectures, and mobile virtual reality) after three months’ usage. International Journal of Educational Technology in Higher Education, 18(1), 1–17. https://doi.org/10.1186/S41239-021-00243-4/TABLES/5
Stephens, M., Rudiger, N., & Faires, D. (2021). Student Perceptions and Use of Mobile Devices for LIS Coursework: Implications for Educators. Journal of Education for Library & Information Science, 62(4), 443–459.
Teymurova, V., Abdalova, M., Babayeva, S., Huseynova, V., Mammadov, E., & Islamova, N. (2020). Implementation of Mobile Entrepreneurial Learning in the Context of Flexible Integration of Traditions and Innovations. International Journal of Interactive Mobile Technologies, 14(21), 118–135. https://doi.org/10.3991/ijim.v14i21.18445
Thedpitak, A., & Somphong, M. (2021). Exploring thai efl learners’ attitudes toward the use of mobile applications for language learning. LEARN Journal: Language Education and Acquisition Research Network, 14(1), 370–398.
Ugur-Erdogmus, F., & Cakir, R. (2022). Effect of Gamified Mobile Applications and the Role of Player Types on the Achievement of Students. Journal of Educational Computing Research, 60(4), 1063–1080. https://doi.org/10.1177/07356331211065679
UNESCO Institute for Statistics. (2012). International Standard Classification of Education. Verdes, A., Navarro, C., & Álvarez Campos, P. (2021). Mobile learning applications to improve invertebrate zoology online teaching. Invertebrate Biology, 140(1).
Vigil García, P. A., Padrón, R. A., Emilio, E., Betancourt, A., Dumpierrés Otero, E., Castillo, O. L., … Castillo, L. (2020). Mobile learning: el uso de Whatsapp en el aprendizaje del inglés. Revista Conrado, 16(77), 201–208.
Voogt, J., Laferrière, T., Breuleux, A., Itow, R. C., Hickey, D. T., & Mckenney, S. (2015). Collaborative design as a form of professional development. Instructional Science, 43(2), 259–282. https://doi.org/10.1007/S11251-014-9340-7/TABLES/1
Voshaar, J., Knipp, M., Loy, T., Zimmermann, J., & Johannsen, F. (2022). The impact of using a mobile app on learning success in accounting education. Accounting Education, 32, 222–247. https://www.tandfonline.com/doi/full/10.1080/09639284.2022.2041057
Wendler, R. (2012). The maturity of maturity model research: A systematic mapping study. Information and Software Technology, 54(12), 1317–1339. https://doi.org/10.1016/J.INFSOF.2012.07.007
Wickham, H. (2016). ggplot2. Springer International Publishing. Retrieved from https://doi.org/10.1007/978-3-319-24277-4
Wickham, H., Averick, M., Bryan, J., Chang, W., Mcgowan, L., François, R., … Yutani, H. (2019). Welcometo the Tidyverse. Journal of Open Source Software, 4(43), 1686–1686. https://doi.org/10.21105/joss.01686
Wickham, H., François, R., Henry, L., Müller, K., & Vaughan, D. (2023). dplyr: A Grammar of Data Manipulation. Retrieved from https://github.com/tidyverse/dplyr
Wickham, H., Vaughan, D., & Girlich, M. (2023). tidyr: Tidy Messy Data.
Wu, W.-H., Jim Wu, Y.-C., Chen, C.-Y., Kao, H.-Y., Lin, C.-H., & Huang, S.-H. (2012). Review of trends from mobile learning studies: A meta-analysis. Computers & Education, 59(2), 817– 827. https://doi.org/10.1016/J.COMPEDU.2012.03.016
Yeboah, D., & Nyagorme, P. (2022). Students’ acceptance of WhatsApp as teaching and learning tool in distance higher education in sub-Saharan Africa. Cogent Education, 9(1). https://doi.org/10.1080/2331186x.2022.2077045
Yu, D., Yan, Z., & He, X. (2022). Capturing knowledge trajectories of mobile learning research: A main path analysis. Education and Information Technologies, 27(5). https://doi.org/10.1007/S10639-021-10869-6/FIGURES/5
Yuan, Y. P., Tan, G. W.-H., Ooi, K. B., & Lim, W. L. (2021). Can COVID-19 pandemic influence experience response in mobile learning? Telematics and Informatics, 64, 101676. https://doi.org/10.1016/J.TELE.2021.101676
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education - where are the educators? International Journal of Educational Technology in Higher Education, 16(1). https://doi.org/10.1186/s41239-019-0171-0
Zhang, L., & He, J. (2022). Optimization of Ideological and Political Education under the Epidemic via Mobile Learning Auxiliary Platform in the Era of Digitization. Hindawi, Special issue, 1–9. https://doi.org/10.1155/2022/6149995
Zhang, X. (2022). The Influence of Mobile Learning on the Optimization of Teaching Mode in Higher Education. Wireless Communications and Mobile Computing.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Lazaro, G.Rd., Duart, J.M. Moving Learning: A Systematic Review of Mobile Learning Applications for Online Higher Education. J. New Approaches Educ. Res. 12, 198–224 (2023). https://doi.org/10.7821/naer.2023.7.1287
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.7821/naer.2023.7.1287