Student Involvement in Mobile-Learning: Case of Ibn Tofail University
Abstract
In last years, the use of cell phones has reached new heights. This inflfluences teaching methods at universities. The integration of mobile technologies into the teaching process can encourage the students to be more involved in the online learning process.The main challenges of mobile learning can be summarized in the changing attitudes in the educational fifield, being able to develop adequate pedagogical frameworks, good design (pedagogical and visual) and providing the right methods to control the involvement of the learners.Although mobile devices are highly present in the daily life of learners and trainers, the use of these technologies in distance education appears to still be low. The objective of this research is to measure the involvement of the students who are using smartphones compared to those who are using desktop computers by monitoring the learner’s activity on the platform.To carry out this research, we used three Moodle distance-learning platforms from Ibn Tofail University to collect data. This data was processed by machine learning algorithms in an effort to see a link between the use of the mobile and the involvement of a student in an online learning.References
Y. Zidoun, F. El Arroum, M. Talea, and R. Dehbi, Students’ perception about mobile learning in Morocco: Survey analysis, Int. J. Interact. Mob. Technol., vol. 10, no. 4, pp. 80–84, 2016.
A. Astin, Student Involvement: A Development Theory for Higher Education, J. Coll. Stud. Dev., vol. 40, pp. 518–529, Jan. 1984.
D. S. Carlson and M. R. Frone, Relation of behavioral and psychological involvement to a new four-factor conceptualization of work-family interference, J. Bus. Psychol., vol. 17, no. 4, pp. 515–535, 2003.
S. J. Baldwin and Y.-H. Ching, Guidelines for Designing Online Courses for Mobile Devices, TechTrends, vol. 64, no. 3, pp. 413–422, 2020.
M. Z. Asghar, E. Barber`a, I. Younas, Mobile learning technology readiness and acceptance among pre-service teachers in Pakistan during the COVID-19 pandemic, Knowl. Manag. E-Learn. Int. J., vol. 13, no. 1, Art. no. 1, Apr. 2021.
M. Ibrahim, N. Khairudin, and D. Salleh, Unwinding Environment in the Lecture Room Using Mobile Learning (M-Learning) Adoption among the Millennial Generation, vol. 1874, no. 1, 2021.
J. H. Kuznekoff and S. Titsworth, The Impact of Mobile Phone Usage on Student Learning, Commun. Educ., vol. 62, no. 3, pp. 233–252, Jul. 2013.
S. Yang, S. Zhou, and X. Cheng, Why do college students continue to use mobile learning? Learning involvement and selfdetermination theory: College students mobile learning continuance, Br. J. Educ. Technol., vol. 50, no. 2, pp. 626–637, Mar. 2019.
R. F. Kizilcec and M. Chen, Student engagement in mobile learning via text message, Proceedings of the Seventh ACM Conference on Learning@ Scale, pp. 157–166, 2020.
H. Heflin, J. Shewmaker, and J. Nguyen, Impact of mobile technology on student attitudes, engagement, and learning, Comput. Educ., vol. 107, pp. 91–99, Apr. 2017.
M. Chen and R. F. Kizilcec, Return of the Student: Predicting Re-Engagement in Mobile Learning, EDM, 2020.
C. Milligan, A. Littlejohn, and A. Margaryan, Patterns of engagement in connectivist MOOCs, J. Online Learn. Teach., vol. 9, no. 2, pp. 149–159, 2013.
R. Ferguson and D. Clow, Examining engagement: analysing learner subpopulations in massive open online courses (MOOCs), in Proceedings of the fifth international conference on learning analytics and knowledge, pp. 51–58, 2015.
S. I. De Freitas, J. Morgan, and D. Gibson, Will MOOCs transform learning and teaching in higher education? Engagement and course retention in online learning provision, Br. J. Educ. Technol., vol. 46, no. 3, pp. 455–471, 2015.
C. Taylor, K. Veeramachaneni, and U.-M. O’Reilly, Likely to stop? predicting stopout in massive open online courses, ArXiv Prepr. ArXiv14083382, 2014.
R. Longadge and S. Dongre, Class imbalance problem in data mining review, ArXiv Prepr. ArXiv13051707, 2013.
C. Coffrin, L. Corrin, P. de Barba, and G. Kennedy, Visualizing patterns of student engagement and performance in MOOCs, Proceedins of the Fourth International Conference on Learning Analytics And Knowledge - LAK ’14, Indianapolis, Indiana, pp. 83–92, 2014.
J. Cole and H. Foster, Using Moodle: Teaching with the popular open source course management system., O’Reilly Media, Inc., 2007.
W. Rice, Moodle 1.9 E-Learning Course Development, Packt Publishing Ltd, 2008.
Y. Liu, Z. Li, H. Xiong, X. Gao, and J. Wu, Understanding of Internal Clustering Validation Measures, 2010 IEEE International Conference on Data Mining, pp. 911–916, Dec. 2010.
M. Z. Rodriguez et al., Clustering algorithms: A comparative approach, PLOS ONE, vol. 14, no. 1, p. e0210236, Jan. 2019.
Rice, William and William, H, Moodle, Packt publishing Birmingham, 2006.
zach, How to Calculate Z-Scores in Python, Statology, Jul. 03, 2020. Available from https://www.statology.org/z-score-python accessed Mar. 31, 2021).
S. Kumar, Silhouette Method — Better than Elbow Method to find Optimal Clusters, Medium, Aug. 09, 2021. Available from https://towardsdatascience.com/silhouette-method-better-than-elbow-method-to-find-optimal-clusters-378d62ff6891 (accessed Sep. 19, 2021).
J. Traxler, Defining mobile learning,” in IADIS International Conference Mobile Learning, pp. 261–266, 2005.
M. Sharples, I. Arnedillo-S´anchez, M. Milrad, and G. Vavoula, Mobile Learning, Technology-Enhanced Learning, pp. 233–249, 2009.
C. Romero, S. Ventura, and E. Garc´ıa, Data mining in course management systems: Moodle case study and tutorial, Comput. Educ., vol. 51, no. 1, pp. 368–384, Aug. 2008.
M. DAOUDI, N. LEBKIRI, and I. OUMAIRA, Determining the Learner’s Profile and Context Profile in Order to Propose Adaptive Mobile Interfaces Based on Machine Learning, pp. 1–6, Dec. 2020.
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