Smart Mobile Learning During the COVID-19 Pandemic: Comparison
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Please note this is a comparison between Version 2 by Beatrix Zheng and Version 1 by Mohammed Amin Almaiah.

Universities throughout the world are attempting to develop electronic learning (e-learning) and mobile learning (M-learning) systems in order to merge these educational systems with the traditional ones currently used. E-learning and M-learning systems have become critical in order to accomplish learning objectives in a successful way during the COVID-19 pandemic. It is critical for universities to take full advantage of e-learning in order to remain competitive in the globalised 21st century. The recent revolution in information and communication technology (ICT) has resulted in a move away from face-to-face learning, toward e-learning. During the COVID-19 pandemic, this technology has helped universities on a global scale to ensure the continuity of learning processes. It has also altered how students communicate and interact with instructors. This technological advancement has transformed the learning process through the use of e-learning systems and M-learning applications to achieve sustainable education.

  • mobile learning
  • distance learning
  • COVID-19
  • Saudi Arabia

1. Related Works on Mobile Learning Acceptance

With the increasing number of features offered by smartphones, there has been tremendous growth in interest in using mobile devices in higher education [18][1]. Hence, research interest in factors that affect M-learning acceptance among students has also increased. Several studies have been undertaken to explain the main drivers for the adoption of M-learning in different contexts [19,20,21,22][2][3][4][5]. According to previous studies [23[6][7][8][9][10][11][12][13][14][15][16][17][18],24,25,26,27,28,29,30,31,32,33,34,35], students’ acceptance of M-learning is an essential step in guaranteeing the full usage of this system. To achieve such acceptance, the main aspects and factors of students’ adoption of M-learning applications should be properly understood [36][19]. In addition, students’ needs and requirements should be correctly determined by mobile service providers and designers from the outset. Several studies have addressed this issue. For example, For example, [19][2] found that information technology infrastructure (ITI) is one necessary component of M-learning acceptance. As a result, the ITI in Saudi universities requires extensive analysis. Providing adequate ITI is necessary when introducing new technologies such as M-learning applications, as insufficient IT resources and infrastructure can impede the acceptance and usage of any new technology [20][3]. Therefore, this restudyearch adopted this factor to investigate its effect on M-learning success in Saudi universities.
In addition, previous studies [21,22,23,24][4][5][6][7] have confirmed that university management support is vital to the development of M-learning system adoption and, thus, reflects positively on the actual use (AU) and acceptance of M-learning by students. According to [24][7], support by university management is associated with their willingness to provide all necessary resources to ensure the development success of the M-learning project. In other words, a positive attitude among top management towards an M-learning project is a real indicator that a university will support the adoption of M-learning. Therefore, outhe researcher studys included this factor in the proposed model to investigate its effect on M-learning success in Saudi universities.
On the other hand, university culture could play a crucial role in how universities adopt M-learning systems. Information system researchers have found that university culture is predictive of technology adoption, including M-learning adoption [25][8]. According to [26][9], public culture development is qualitatively distinct from physical infrastructure development. The COVID-19 pandemic has led to cultural shifts in attitudes towards distance learning technologies, as well as possible resistance from students to the use of these new technologies [27,28,29,30][10][11][12][13]. Hence, this restudyearch investigates the effect of university culture on M-learning success in Saudi universities.
Moreover, several researchers have indicated that one of the main issues that should be addressed to increase the involvement and use of M-learning applications is inadequate awareness of the technology’s existence [31,32,33,34,35][14][15][16][17][18]. Prior studies have shown that aware ness is crucial in the adoption of M-learning systems. Therefore, this restudyearch adopted this factor to investigate its effect on M-learning success in Saudi universities.
Finally, in the ourresearchers' proposed model, wethey adopted two main constructs of the TAM model as predictors of acceptance and usage of M-learning, namely, perceived ease of use (EUS), and perceived usefulness (PUS). Previous studies [36,37,38,39,40][19][20][21][22][23] have confirmed that these two factors could play a crucial role in the success, usage, and acceptance of M-learning systems. In general, users do not like to use systems that require high levels of skill or are highly complex. Several studies have supported the belief that EUS influences users’ intention to use a particular technology [40,41,42,43][23][24][25][26]. Similarly, previous research has indicated that users find M-learning technology useful and productive if its use does not require much time and effort [44,45,46,47,48][27][28][29][30][31]. Hence, it can be argued that students are more likely to use M-learning services if they find that doing so is not complicated. Similarly, the success of an M-learning system would increase if users realised the importance of such a system in improving their performance.

2. Overview of Mobile Learning as a Distance Learning Tool

During the COVID-19 pandemic, many universities around the world started to use distance learning platforms, such as M-learning platforms, Blackboard, and others [49][32]. For example, many universities in Saudi Arabia—such as King Faisal University (KFU)—used the Blackboard and M-learning systems as distance learning tools, as a result of the decision by the Saudi government to close all universities during the COVID-19 pandemic [50][33]. M-learning systems served as online classrooms in which smartphones could be used by instructors and their students to continue the learning process during the COVID-19 pandemic. An M-learning system enables instructors to upload all learning materials, learning activities, assignments, and quizzes. On the other side, students can access online classrooms and interact with instructors through online classes, download learning materials, and submit homework using the M-learning system. An M-learning platform is a distance learning platform with many features that support the learning and teaching processes for all education levels in universities. It also contributes to ensuring that lesson plans are carried out and the educational goals of the curriculum are met [50,51,52,53][33][34][35][36]. An M-learning system features a package of educational tools to support the teaching and learning processes. It is a virtual classroom that enables learners and their teachers to meet simultaneously via virtual meetings, or at any convenient time through recorded lessons [54,55,56,57][37][38][39][40]. In addition, the platform includes excellent features for ease of communication between students and teachers, such as email service, Microsoft Teams, and a variety of channels for communication between students, teachers, and parents [58][41].

3. Factors Affecting the Mobile Learning Success Context

Several recent studies have examined a number of factors that could influence the acceptance, adoption, usage, and implementation of M-learning. For example, [59,60,61][42][43][44] found that information technology infrastructure (ITI) is one necessary component of M-learning acceptance. As a result, the ITI in Saudi universities requires extensive analysis. Providing adequate ITI is necessary when introducing new technologies such as M-learning applications, as insufficient IT resources and infrastructure can impede the acceptance and usage of any new technology [62][45]. Therefore, this restudyearch adopted this factor to investigate its effect on M-learning success in Saudi universities.
In addition, previous studies [63,64,65,66,67][46][47][48][49][50] have confirmed that university management support is vital to the development of M-learning system adoption and, thus, reflects positively on the actual use (AU) and acceptance of M-learning by students. According to [54][37], support by university management is associated with their willingness to provide all necessary resources to ensure the development success of the M-learning project. In other words, a positive attitude among top management towards an M-learning project is a real indicator that a university will support the adoption of M-learning. Therefore, outhe researcher studys included this factor in the proposed model to investigate its effect on M-learning success in Saudi universities.
On the other hand, university culture could play a crucial role in how universities adopt M-learning systems. Information system researchers have found that university culture is predictive of technology adoption, including M-learning adoption [68][51]. According to [69][52], public culture development is qualitatively distinct from physical infrastructure development. The COVID-19 pandemic has led to cultural shifts in attitudes towards distance learning technologies, as well as possible resistance from students to the use of these new technologies. Hence, this restudyearch investigates the effect of university culture on M-learning success in Saudi universities.
Moreover, several researchers have indicated that one of the main issues that should be addressed to increase the involvement and use of M-learning applications is inadequate awareness of the technology’s existence [70][53]. Prior studies have shown that aware ness is crucial in the adoption of M-learning systems. Therefore, this restudyearch adopted this factor to investigate its effect on M-learning success in Saudi universities.
Finally, in outhe researchers' proposed model, wethey adopted two main constructs of the TAM model as predictors of acceptance and usage of M-learning, namely, perceived ease of use (EUS), and perceived usefulness (PUS). Previous studies [71,72,73][54][55][56] have confirmed that these two factors could play a crucial role in the success, usage, and acceptance of M-learning systems. In general, users do not like to use systems that require high levels of skill or are highly complex. Several studies have supported the belief that EUS influences users’ intention to use a particular technology [74,75,76,77][57][58][59][60]. Similarly, previous research has indicated that users find M-learning technology useful and productive if its use does not require much time and effort [77,78,79,80][60][61][62][63]. Hence, it can be argued that students are more likely to use M-learning services if they find that doing so is not complicated. Similarly, the success of an M-learning system would increase if users realised the importance of such a system in improving their performance.

References

  1. Salloum, S.A.; Alhamad, A.Q.; Al-Emran, M.; Monem, A.A.; Shaalan, K. Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model. IEEE Access 2019, 7, 128445–128462.
  2. Mulhem, A.A.; Almaiah, M.A. A Conceptual Model to Investigate the Role of Mobile Game Applications in Education during the COVID-19 Pandemic. Electronics 2021, 10, 2106.
  3. Almaiah, M.A.; Almomani, O.; Al-Khasawneh, A.; Althunibat, A. Predicting the Acceptance of Mobile Learning Applica-tions During COVID-19 Using Machine Learning Prediction Algorithms. In Emerging Technologies during the Era of COVID-19 Pandemic; Springer: Cham, Switzerland, 2021; Volume 348, p. 319.
  4. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–340.
  5. Aldossry, B. Evaluating the madrasati platform for the virtual classroom in Saudi arabian education during the time of COVID-19 Pandemic. Eur. J. Open Educ. e-Learn. Stud. 2021, 6, 89–99.
  6. Madrasati. Ministry of Education: Riyadh, Saudi Arabia. 2021. Available online: https://backtoschool.sa/ (accessed on 19 June 2021).
  7. Khalife, W. The effectiveness of using active learning based on distance learning on improving academic achievement and developing the trend towards distance learning for the learning technologies course for mathematics education students. Sci. J. Phys. Educ. Sports Sci. 2019, 87, 321–334.
  8. Khanfar, A.R. Distance-learning entrepreneurship education in the time of corona virus—COVID-19 challenges & solution. J. Entrep. Educ. 2020, 23, 1–17.
  9. MOE. Ministry of Education: Riyadh, Saudi Arabia. 2020. Available online: https://www.moe.gov.sa/ar/mediacenter/MOEnews/Pages/MR2-2020-453.aspx (accessed on 21 June 2021).
  10. Almaiah, M.A.; Alamri, M.M. Proposing a new technical quality requirements for mobile learning applications. J. Theor. Appl. Inf. Technol. 2018, 96, 6955–6968.
  11. Almaiah, M.A. Thematic analysis for classifying the main challenges and factors influencing the successful implementation of e-learning system using Nvivo. Int. J. Adv. Trends Comput. Sci. Eng. 2020, 9, 142–152.
  12. Lutfi, A.; Alsyouf, A.; Almaiah, M.A.; Alrawad, M.; Abdo, A.A.; Al-Khasawneh, A.L.; Ibrahim, N.; Saad, M. Factors Influencing the Adoption of Big Data Analytics in the Digital Transformation Era: Case Study of Jordanian SMEs. Sustainability 2022, 14, 1802.
  13. Althunibat, A.; Almaiah, M.A.; Altarawneh, F. Examining the Factors Influencing the Mobile Learning Applications Usage in Higher Education during the COVID-19 Pandemic. Electronics 2021, 10, 2676.
  14. Al-Emran, M.; Arpaci, I.; Salloum, S.A. An empirical examination of continuous intention to use m-learning: An integrated model. Educ. Inf. Technol. 2020, 25, 2899–2918.
  15. Hoi, V.N. Understanding higher education learners’ acceptance and use of mobile devices for language learning: A Rasch-based path modeling approach. Comput. Educ. 2020, 146, 103761.
  16. Gharaibeh, M.K.; Gharaibeh, N.K. An empirical study on factors influencing the intention to use mobile learning. Adv. Sci. Technol. Eng. Syst. J. 2020, 5, 1261–1265.
  17. Al-Nawayseh, M.K.; Baarah, A.H.; Al-Masaeed, S.A.; Alnabhan, M.M. Mobile learning adoption in Jordan: Technology influencing factors. Int. J. Netw. Virtual Organ. 2019, 20, 400–417.
  18. Badwelan, A.; Bahaddad, A.A. Functional Requirements to Increase Acceptance of M-Learning Applications among University Students in the Kingdom of Saudi Arabia (KSA). Int. J. Comput. Sci. Netw. Secur. 2021, 21, 21–39.
  19. Fauziati, C. Faktor-faktor yang Memengaruhi Penerimaan Mobile Learning Applications (Apps)(Acceptance Factors of Mobile Learning Applications (Apps)). J. Iptekkom 2020, 22, 45–57.
  20. Lutfi, A. Factors Influencing the Continuance Intention to Use Accounting Information System in Jordanian SMEs from the Perspectives of UTAUT: Top Management Support and Self-Efficacy as Predictor Factors. Economies 2022, 10, 75.
  21. Wahab, R.A. Student’s acceptance of digital educational tools: A case study. In Proceedings of the 2020 Sixth International Conference on e-Learning (econf), Sakheer, Bahrain, 6–7 December 2020; pp. 81–86.
  22. Badwelan, A.; Bahaddad, A.A. Towards the Acceptance of Functional Requirements in M-Learning Application for KSA University Students. Int. J. Comput. Sci. Netw. Secur. 2021, 21, 145–166.
  23. Wei, N.; Li, Z. Telepresence and Interactivity in Mobile Learning System: Its Relation with Open Innovation. J. Open Innov. Technol. Mark. Complex. 2021, 7, 78.
  24. Al-Emran, M.; Mezhuyev, V.; Kamaludin, A. An innovative approach of applying knowledge management in M-learning application development: A pilot study. Int. J. Inf. Commun. Technol. Educ. 2019, 15, 94–112.
  25. Zhang, M.; Chen, Y.; Zhang, S.; Zhang, W.; Li, Y.; Yang, S. Understanding mobile learning continuance from an online-cum-offline learning perspective: A SEM-neural network method. Int. J. Mob. Commun. 2022, 20, 105–127.
  26. Kumar, J.A.; Osman, S.; Sanmugam, M.; Rasappan, R. Mobile Learning Acceptance Post Pandemic: A Behavioural Shift among Engineering Undergraduates. Sustainability 2022, 14, 3197.
  27. Alturki, U.; Aldraiweesh, A. Students’ Perceptions of the Actual Use of Mobile Learning during COVID-19 Pandemic in Higher Education. Sustainability 2022, 14, 1125.
  28. Mingyue, F.; Ndavi, J.W.; Qalati, S.A.; Huang, L.; Zhengjia, P. Applying the time continuum model of motivation to explain how major factors affect mobile learning motivation: A comparison of SEM and fsQCA. Online Inf. Rev. 2022, 32.
  29. Chibisa, A.; Mutambara, D. Determinants of High School Learners’ Continuous Use of Mobile Learning during the COVID-19 Pandemic. Int. J. Learn. Teach. Educ. Res. 2022, 21, 20–33.
  30. Bacolod, D.B. Mobile Learning as a Solution for Restricted Learning during the COVID-19 Pandemic. J. Digit. Educ. Technol. 2022, 2, ep2203.
  31. Lin, H.C.; Ho, C.F.; Yang, H. Understanding adoption of artificial intelligence-enabled language e-learning system: An empirical study of UTAUT model. Int. J. Mob. Learn. Organ. 2022, 16, 74–94.
  32. Pishtari, G.; Rodríguez-Triana, M.J. An Analysis of Mobile Learning Tools in Terms of Pedagogical Affordances and Support to the Learning Activity Life Cycle. In Hybrid Learning Spaces; Springer: Cham, Switzerland, 2022; pp. 167–183.
  33. Hartley, K.; Andújar, A. Smartphones and Learning: An Extension of M-Learning or a Distinct Area of Inquiry. Educ. Sci. 2022, 12, 50.
  34. Bai, H. Students’ Use of Learning Management System in Hybrid Learning: Mobile or Not. Int. J. E-Learn. 2022, 21, 5–23.
  35. Okuonghae, O.; Igbinovia, M.O.; Adebayo, J.O. Technological readiness and computer self-efficacy as predictors of E-learning adoption by LIS students in Nigeria. Libri 2022, 72, 13–25.
  36. Sayaf, A.M.; Alamri, M.M.; Alqahtani, M.A.; Alrahmi, W.M. Factors Influencing University Students’ Adoption of Digital Learning Technology in Teaching and Learning. Sustainability 2022, 14, 493.
  37. Hsu, H.T.; Lin, C.C. Extending the technology acceptance model of college learners’ mobile-assisted language learning by incorporating psychological constructs. Br. J. Educ. Technol. 2022, 53, 286–306.
  38. Mehra, A.; Rajput, S.; Paul, J. Determinants of adoption of latest version smartphones: Theory and evidence. Technol. Forecast. Soc. Chang. 2022, 175, 121410.
  39. Ismail, S.N.; Omar, M.N.; Don, Y.; Purnomo, Y.W.; Dzahir, M. Teachers’ acceptance of mobile technology use towards innovative teaching in Malaysian secondary schools. Int. J. Eval. Res. Educ. 2021, 11, 120–127.
  40. Khlaif, Z.N.; Sanmugam, M.; Ayyoub, A. Impact of Technostress on Continuance Intentions to Use Mobile Technology. Asia-Pac. Educ. Res. 2022, 12, 15–26.
  41. Alsharida, R.; Hammood, M.; Al-Emran, M. Mobile Learning Adoption: A Systematic Review of the Technology Acceptance Model from 2017 to 2020. Int. J. Emerg. Technol. Learn. 2021, 16, 147–162.
  42. Moya, S.; Camacho, M. Identifying the key success factors for the adoption of mobile learning. Educ. Inf. Technol. 2021, 26, 3917–3945.
  43. Gupta, Y.; Khan, F.M.; Agarwal, S. Exploring Factors Influencing Mobile Learning in Higher Education—A Systematic Review. Int. J. Interact. Mob. Technol. 2021, 15, 140–157.
  44. Alowayr, A.; Al-Azawei, A. Predicting mobile learning acceptance: An integrated model and empirical study based on higher education students’ perceptions. Australas. J. Educ. Technol. 2021, 37, 38–55.
  45. Criollo-C, S.; Guerrero-Arias, A.; Jaramillo-Alcázar, Á.; Luján-Mora, S. Mobile learning technologies for education: Benefits and pending issues. Appl. Sci. 2021, 11, 4111.
  46. Alhumaid, K.; Habes, M.; Salloum, S.A. Examining the factors influencing the mobile learning usage during COVID-19 Pandemic: An Integrated SEM-ANN Method. IEEE Access 2021, 9, 102567–102578.
  47. Sungur-Gül, K.; Ateş, H. Understanding pre-service teachers’ mobile learning readiness using theory of planned behavior. Educ. Technol. Soc. 2021, 24, 44–57.
  48. Buabeng-Andoh, C. Exploring University students’ intention to use mobile learning: A research model approach. Educ. Inf. Technol. 2021, 26, 241–256.
  49. Al-Hamad, M.; Mbaidin, H.; AlHamad, A.; Alshurideh, M.; Kurdi, B.; Al-Hamad, N. Investigating students’ behavioral intention to use mobile learning in higher education in UAE during Coronavirus-19 pandemic. Int. J. Data Netw. Sci. 2021, 5, 321–330.
  50. Ibrahim, M.; Khairudin, N.; Salleh, D. Unwinding Environment in the Lecture Room Using Mobile Learning (M-Learning) Adoption among the Millennial Generation. In Journal of Physics: Conference Series; IOP Publishing: Bristol, UK, 2021; Volume 1874, p. 012033.
  51. Saikat, S.; Dhillon, J.S.; Wan Ahmad, W.F.; Jamaluddin, R. A systematic review of the benefits and challenges of mobile learning during the COVID-19 pandemic. Educ. Sci. 2021, 11, 459.
  52. Antee, A. Student perceptions and mobile technology adoption: Implications for lower-income students shifting to digital. Educ. Technol. Res. Dev. 2021, 69, 191–194.
  53. Loh, X.K.; Lee, V.H.; Loh, X.M.; Tan, G.W.; Ooi, K.B.; Dwivedi, Y.K. The dark side of mobile learning via social media: How bad can it get? Inf. Syst. Front. 2021, 22, 22–35.
  54. Yuan, Y.P.; Tan, G.W.; Ooi, K.B.; Lim, W.L. Can COVID-19 pandemic influence experience response in mobile learning? Telemat. Inform. 2021, 64, 101676.
  55. Vallejo-Correa, P.; Monsalve-Pulido, J.; Tabares-Betancur, M. A systematic mapping review of context-aware analysis and its approach to mobile learning and ubiquitous learning processes. Comput. Sci. Rev. 2021, 39, 100335.
  56. Okai-Ugbaje, S. Towards a pedagogical and sociotechnical framework for the strategic integration of mobile learning in higher education in low and middle income countries. High. Educ. Res. Dev. 2021, 40, 581–598.
  57. Criollo-C, S.; Lema, M.; Gonzalez, M.S.; Jaramillo-Alcázar, A.; Guerrero-Arias, A.; Luján-Mora, S. Exploring the technological acceptance of a mobile learning tool used in the teaching of an indigenous language. PeerJ Comput. Sci. 2021, 7, e550.
  58. Qashou, A. Influencing factors in M-learning adoption in higher education. Educ. Inf. Technol. 2021, 26, 1755–1785.
  59. Zhang, X.; Lo, P.; So, S.; Chiu, D.K.; Leung, T.N.; Ho, K.K.; Stark, A. Medical students’ attitudes and perceptions towards the effectiveness of mobile learning: A comparative information-need perspective. J. Librariansh. Inf. Sci. 2021, 53, 116–129.
  60. Sophonhiranrak, S. Features, barriers, and influencing factors of mobile learning in higher education: A systematic review. Heliyon 2021, 7, e06696.
  61. Dorouka, P.; Papadakis, S.; Kalogiannakis, M. Nanotechnology and mobile learning: Perspectives and opportunities in young children’s education. Int. J. Technol. Enhanc. Learn. 2021, 13, 237–252.
  62. Shodipe, T.O.; Ohanu, I.B. Electrical/electronics technology education teachers attitude, engagement, and disposition towards actual usage of Mobile learning in higher institutions. Educ. Inf. Technol. 2021, 26, 1023–1042.
  63. Hoi, V.N.; Mu, G.M. Perceived teacher support and students’ acceptance of mobile-assisted language learning: Evidence from Vietnamese higher education context. Br. J. Educ. Technol. 2021, 52, 879–898.
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