International Journal of Engineering and Modern Technology (IJEMT )

E-ISSN 2504-8848
P-ISSN 2695-2149
VOL. 10 NO. 5 2024
DOI: 10.56201/ijemt.v10.no5.2024.pg103.129


Comparative Review of Selected Adaptive e-Learning Platforms

Ibuomo R. Tebepah and Efiyeseimokumo S. Ikeremo


Abstract


This work was centered on reviewing the ten most adaptive e-learning platforms; identifying features, functionalities and the overall appearance of the application. The main objective was to enable educationist, institution heads, technologist, and other learning stakeholders to make knowledgeable decisions in regards to adaptive learning platforms. The reviewing took 2 dimensions; reviewing of related literature, and the hands-on review. In the course of the review, some were identified to be more suitable for corporate trainings with very minimal educational or learning pedagogy consideration. A comparison table was created, summarizing each for easy selection and choice.



References:


Alkhalaf, S., Nguyen, A., & Draw, S. (2010). Assessing e-learning system in the kingdom of
saudi arabia’s higher education sector: an explanatory analysis. 2010 International
Conference on Intelligent Network and Computing, 284-287. https://www.academia.edu/941587/

Arovo, L., Dolog, P., Houben, G., Kravcik, M., Naeve, A., Nilsson, M., & Wild, F. (2006).
Interoperability in personalised adaptive learning. Educational Technology and Society,
9(2), 4-18. https://www.researchgate.net/publication/220374558

BaitiAfini, N., Shuib, N., Nasir, H., Bimba, A., Idris, N., & Balakrishnan, V. (2019).
Identification of personal traits in adaptive learning environment: systematic literature
review. Computers and Education. 130, 168-190.
https://www.sciencedirect.com/science/article/abs/pii/S0360131518303026

Balasubramania, V., & Annocia, S. (2016). Learning style detection based on cognitive skills to
support adaptive learning environment: a reinforcement approach. Ain Shams
Engineering Journal, 9(4), 1-8. https://www.researchgate.net/publication/304493930

Ben-Naim, D., Bain, M., & Marcus, N. (2009). A user-driven and data-driven approach for
supporting teachers in reflection and adaptation of adaptive tutorials. Proceedings of the
2nd International Conference on Educational Data Mining, Spain.
https://www.researchgate.net/publication/221570376

Ben-Naim, D., Marcus, N., & Bain, M. (2007). Virtual apparatus framework
approach to constructing adaptive tutorials. Proceedings of the 2007 International
Conference on E-Learning, E-Business, Enterprise Information Systems and E-
Government EEE 2007, Nevada. https://www.researchgate.net/publication/221186398

Ben-Naim, D., Marcus, N., & Bain, M. (2008). Visualisation and analysis of student interactions
in an adaptive exploratory learning environment. ResearchGate. 138, 1-10. http://ceur-
ws.org/Vol-381/paper01.pdf

Brooks, C., Greer, J., Melis, E., & Ullrich, C., (2014). Combining its and e-learning technologies
opportunities and challenges. Researchgate, 28, 1-11.
https://www.researchgate.net/publication/200166244_

Budiharto, W., Chayani, D., Rumordon, P., & Suhartono, D. (2017). Edurobot: intelligent
humanoid robot with natural interaction for education and entertainment. Procedia
Computer Science, 116, 564-570.
https://www.sciencedirect.com/science/article/pii/S1877050917321142

Ciloglugil, B. (2016). Adaptivity based on felder-silverman learning styles modeling e-learning.
4th International Symposium on Innovative Technologies in Engineering and Science
(ISITES 2016) Turkey, 1523-1532. https://www.researchgate.net/publication/311597011_

Cingi, C. (2013). Computer aided education. Social and Behavioural Sciences, 103, 220-229.
https://www.sciencedirect.com/science/article/pii/S1877042813037749

Dina, D., Cofini, V., Mascio, T., & Cecilia, M. (2016). The silent reading supported by adaptive
learning technology: influence in the children outcomes. Computers in Human
Behaviours,
55, 1125-1130. https://www.academia.edu/19922299/

Elumalai, V., Shankar, J., Kalaichelvi, R., John, J., Menon, N., Salem, M., & May, A. (2020).
Factors affecting the quality of e-learning during the covid-19 pandemic from the
perspectie of higher education students. Journal of Information Technology Education
Research, 19, 731-751. http://www.jite.org/documents/Vol19/JITE-Rv19p731-

Farashahi, S., Donahue, C.H., Khorsand, P., Seo, H., Lee, D., & Soltani, A. (2017).
Metaplasticity as a neural substrate for adaptive learning and choice under uncertainty.
Neuron, Elsevier, 94, 401- 414. https://pubmed.ncbi.nlm.nih.gov/28426971/

Forsyth, B., Kimble, C., Birch, J., Deel, G., & Brauer, T. (2016). Maximizing the adaptive
learning technology experience. Journal of Higher Education Theory and Practice,
16(4), 80-88. https://www.jurispro.com/files/articles/

Hedberg, B. (1981). How organisation learning and unlearn. The Learning Organisation, 24(1),
30-38.
https://www.researchgate.net/publication/313682988_Organizational_learning_and_unle
arning

Huang, Q., Yang, D., Jiang, L., Zhang, H., Liu, H., Kotani, K., (2017). An improved k-means
algorithm based on association rules. International Journal of Computer Theory and
Engineering 6(2), 146-149. http://www.ijcte.org/papers/853-IT143.pdf.

Kim, H., Hong, A., & Song, H. (2019). The roles of academic engagement and digital readiness
in students’ achievement in university e-learning environment. Journal of Educational
Technology in Higher Education, 21, 16-21.
https://www.researchgate.net/publication/333931838

Koukopoulos, Z., & Koukopoulos, D. (2017). Integrating educational theories into a feasible
digital environment. Applied Computing and Informatics, 15, 19-26.
https://www.researchgate.net/publication/319934395

Leahy, M., Holland, C., & Ward, F. (2019). The digital frontier: envisioning future technologies
impact in the classroom. Futures Elsevier, 113, 1-10.
https://reader.elsevier.com/reader/sd/pii/

Liu, L., Jiang, H., Chen, W., He, P., Gao, J., Liu, X., & Han, J. (2020). On the variance of the
adaptive learning rate and beyond. International Conference on Learning Representation.
23-31. https://arxiv.org/pdf/1908.03265.pdf

Liu, M., Kang, J., Zou, W., Pan, Z., & Corliss, S. (2019). Using data to understand how to better
design adaptive learning. Technology, Knowledge and Learning Journal, 22, 271-298.
https://doi.org/10.1007/s10758-017-9326-z

Liu, M., McKelroy, E., Corliss, S.B., & Carrigan, J. (2017). Investigating the effect of an
adaptive learning intervention on students’ learning. Educational Technology Research
and Development, 65, 1605-1625. https://link.springer.com/article/10.1007/

Luaran, J., Samsuri, N., Nadzri, A., Baharen, K., & Rom, M. (2014). A study on the student
perspective on the effectiveness of using e-learning. Social and Behavioral Sciences,
Elsevier,123, 139-144. https://www.researchgate.net/publication/275543572_

Machado, M., Moreira, T., Gomes, L., Caldeira, A., & Santos, D. (2016). A fuzzy logic
application in virtual education. Procedia computer science, 19, 19-26.
https://cyberleninka.org/article/n/676534/viewer

Mainemelis, C., Boyatzis, R., & Kolb, D. (2002). Learning styles and adaptive flexibility: testing
experiential learning theory. Management Learning, 33 (1), 5-33.
https://www.researchgate.net/publication/275714431_

Mehta, A., Morris, A., Swinnerton, B., & Homer, M. (2019). The influence of values on e-
learning adoption. Computers and Education, 141, 231- 240.
https://doi.org/10.1016/j.compedu.2019.103617

Mirata, V., Hirt, F., Bergamin, P., & Westhizen, C. (2020). Challenges and contexts in
establishing adaptive learning in higher education: findings from a delphi study.
International Journal of Educational Technology in Higher Education, 17, 1-25.
https://educationaltechnologyjournal.springeropen.com/


Murray, M., & Perez, J. (2015). Informing and performing: a study comparing adaptive learning
to traditional learning. Informing Science: The International Journal of an Emerging
Transdicipline, 18, 111-125. https://www.inform.nu/Articles/Vol18

Paramythis, A., & Loidi-Reisinger, S. (2004). Adaptive learning environment and e-learning
standard. Electronic Journal on e-Learning, 2(1), 181-194.
https://www.bibsonomy.org/bibtex/

Popenici, S., & Kerr, S. (2017). Exploring the impact of artificial intelligence in teaching and
learning in higher education. Research and Practice in Technology, 22, 1-13.
https://telrp.springeropen.com/articles/10.1186/s41039-017-0062-8

Prusty, G., Russell, C., Ford, R., Ben-Naim, D., Ho, S., Vrcelj, Z., Marcus, N., Mccarthy, T.,
Goldfinch, T., Ojeda, R., Gardner, A., Tom, M., & Roger, H. (2011). Adaptive tutorials
to target threshold concepts in mech


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