EXPLORING LEARNING STYLES IN HIGHER EDUCATION THROUGH ARTIFICIAL INTELLIGENCE PLATFORMS

Main Article Content

WILLIAM ORLANDO ALVAREZ ARAQUE, ARACELY FORERO ROMERO, KELLY JOHANNA DONCEL GONZALEZ,DIEGO ALEXANDER GUTIERREZ PONGUTA

Abstract

A documentary review was carried out on the production and publication of research papers related
to the study of the variables Learning Styles, Higher Education and Artificial Intelligence. The
purpose of the bibliometric analysis proposed in this document was to know the main characteristics
of the volume of publications registered in the Scopus database during the period 2017-2022,
achieving the identification of 37 publications. The information provided by this platform was
organized through graphs and figures categorizing the information by Year of Publication, Country
of Origin, Area of Knowledge and Type of Publication. Once these characteristics have been
described, the position of different authors towards the proposed theme is referenced through a
qualitative analysis. Among the main findings made through this research, it is found that the United
States and China, with 8 publications, were the countries with the highest scientific production
registered in the name of authors affiliated with institutions of these nations. The Area of
Knowledge that made the greatest contribution to the construction of bibliographic material
referring to the study of Learning Styles in Higher Education through Artificial Intelligence
platforms, was Computer Science with 20 published documents, and the Type of Publication most
used during the period indicated above were Journal Articles with 46% of the total scientific
production.

Article Details

Section
Articles

References

Gupta, S. C. (2022). Do you support inclusive learning using chatbots? An interview study led by Chatbot.

United States.

Hu, Y. D. (2022). Cross-validation of a rubric for the automatic classification of cognitive presence in

MOOC discussions. New Zealand.

Munir, H. V. (2022). Artificial intelligence and machine learning approaches in digital education: a

systematic review. Sweden.

Sangree, R. H. (2022). Student performance, engagement, and satisfaction in a flipped classroom of static

and materials mechanics: a case study. United States.

Bonini, P. (2020). When tomorrow comes: Technology and the future of sustainability learning in higher

education. Environment, 62(4), 39-48. doi:10.1080/00139157.2020.1764300

Chen, P. (2022). Design and construction of an interactive intelligent learning system for english learners

in higher education institutions. Advances in Multimedia, 2022 doi:10.1155/2022/6364796

Diao, S. (2020). The reform of teaching management mode based on artificial intelligence in the era of

big data. Paper presented at the Journal of Physics: Conference Series, , 1533(4) doi:10.1088/1742

/1533/4/042050 Retrieved from www.scopus.com

Donnelly, L. F., Grzeszczuk, R., Guimaraes, C. V., Zhang, W., & Bisset III, G. S. (2019). Using a natural

language processing and machine learning algorithm program to analyze inter-radiologist report style

variation and compare variation between radiologists when using highly structured versus more free text

reporting. Current Problems in Diagnostic Radiology, 48(6), 524-530. doi:10.1067/j.cpradiol.2018.09.005

Fu, X., Lokesh Krishna, K., & Sabitha, R. (2022). Artificial intelligence applications with e-learning system

for

china's

higher

education

platform. Journal

of

Interconnection

Networks,

doi:10.1142/S0219265921430167

RUSSIAN LAW JOURNAL Volume X (2022) Issue 4

García-Vélez, R., Moreno, B. V., Ruiz-Ichazu, A., Rivera, D. M., & Rosero-Perez, E. (2021). Automating the

generation of study teams through genetic algorithms based on learning styles in higher education

doi:10.1007/978-3-030-51328-3_38 Retrieved from www.scopus.com

Genale, A. S., Sundaram, B. B., Pandey, A., Janga, V., Aweke, D., & Karthika, P. (2022). Big data analysis

for knowledge based on machine learning using classification algorithm. Paper presented at the

International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2022

Proceedings, 108-113. doi:10.1109/ICSCDS53736.2022.9760898 Retrieved from www.scopus.com

Gong, J., Ruan, M., Yang, W., Peng, M., Wang, Z., Ouyang, L., & Yang, G. (2021). Application of blended

learning approach in clinical skills to stimulate active learning attitudes and improve clinical practice

among medical students. PeerJ Computer Science, 9 doi:10.7717/peerj.11690

Gunasilan, U. (2022). Debate as a learning activity for teaching programming: A case in the subject of

machine learning. Higher Education, Skills and Work-Based Learning, 12(4), 705-718. doi:10.1108/HESWBL

-2021-0006

Guo, J., Bai, L., Yu, Z., Zhao, Z., & Wan, B. (2021). An AI-application-oriented in-class teaching evaluation

model by using statistical modeling and ensemble learning. Sensors (Switzerland), 21(1), 1-28.

doi:10.3390/s21010241

Gupta, K. P., & Bhaskar, P. (2020). INHIBITING AND MOTIVATING FACTORS INFLUENCING TEACHERS'

ADOPTION OF AI-BASED TEACHING AND LEARNING SOLUTIONS: PRIORITIZATION USING ANALYTIC HIERARCHY

PROCESS. Journal of Information Technology Education: Research, 19, 693-723. doi:10.28945/4640

Gupta, S., & Chen, Y. (2022). Supporting inclusive learning using chatbots? A chatbot-led interview

study. Journal of Information Systems Education, 33(1), 98-108. Retrieved from www.scopus.com

Hu, Y., Donald, C., & Giacaman, N. (2021). Cross validating a rubric for automatic classification of

cognitive presence in MOOC discussions. International Review of Research in Open and Distance Learning,

(2), 242-260. doi:10.19173/IRRODL. V23I3.5994

Hu, Y., Donald, C., & Giacaman, N. (2022). Cross validating a rubric for automatic classification of

cognitive presence in MOOC discussions. International Review of Research in Open and Distance Learning,

(2), 242-260. doi:10.19173/irrodl.v23i2.5994

Kanuru, S. L., & Priyaadharshini, M. (2020). Lifelong learning in higher education using learning analytics.

Paper presented at the Procedia Computer Science, , 172 848-852. doi:10.1016/j.procs.2020.05.122

Retrieved from www.scopus.com

Kunnath, M. L. A. (2017). Virtualized higher education: Where E-learning trends and new faculty roles

converge towards personalization. Paper presented at the International Conference on Information

Society, i-Society 2016, 109-114. doi:10.1109/i-Society.2016.7854189 Retrieved from www.scopus.com

Lamilla, E., Montero, E., Guzman, D., Roblero, J., Pazmino, A., Gutierrez, E., . . . Romero, A. (2022).

Peer project learning methodology: A novel implementation in the context of teaching-learning

transformation in latin america higher education. Paper presented at the 2022 IEEE ANDESCON:

Technology

and

Innovation

for

Andean

Industry,

doi:10.1109/ANDESCON56260.2022.9989953 Retrieved from www.scopus.com

ANDESCON

,

Li, H., & Graesser, A. (2017). Impact of pedagogical agents' conversational formality on learning and

engagement doi:10.1007/978-3-319-61425-0_16 Retrieved from www.scopus.com

Munir, H., Vogel, B., & Jacobsson, A. (2022). Artificial intelligence and machine learning approaches in

digital education: A systematic revision. Information (Switzerland), 13(4) doi:10.3390/info13040203

Nanglae, L., Iam-On, N., Boongoen, T., Kaewchay, K., & Mullaney, J. (2021). Determining patterns of

student graduation using a bi-level learning framework. Bulletin of Electrical Engineering and Informatics,

(4), 2201-2211. doi:10.11591/EEI. V10I4.2502