Evaluating Online Learning Adaptability in Students Using Machine Learning-Based Techniques: A Novel Analytical Approach

Journal Title: Education Science and Management - Year 2023, Vol 2, Issue 1

Abstract

The widespread adoption of Learning Management Systems (LMSs) in educational contexts, underscored by their critical role in facilitating cloud-based training across diverse settings, serves as the foundation of this investigation. In the era of increasing integration of technology within higher education, a notable reduction in the costs associated with the creation of online content has been observed. The shift towards remote learning, precipitated by the COVID-19 pandemic, has highlighted the indispensable nature of LMSs in the delivery of specialized content, the application of varied pedagogical strategies, and the promotion of student engagement. Adaptability, defined as the ability to adjust behavior, cognition, and emotional responses in the face of new circumstances, has been recognized as a key factor in the success of online learning. This study employs sophisticated Machine Learning Techniques (MLTs) to explore the determinants of student adaptability, introducing the novel framework of Online Learner Adaptability Assessment using MLTs (OLAMLTs). Through the analysis of comprehensive datasets, which include indicators of student behavior, performance, and engagement within online platforms, MLTs facilitate the identification of patterns and correlations pertinent to adaptability. The OLAMLTs framework applies a retrospective analysis to variables such as technological proficiency, motivation, and self-regulatory capabilities, enabling the provision of customized recommendations for educators. By facilitating targeted educational interventions, the study seeks to address the disparity between the need for adaptable learners and the availability of tools designed to foster this critical attribute. The ultimate aim is to augment the resilience and efficacy of online learning platforms in anticipation of future disruptions, including pandemics or other unforeseen challenges. This research contributes to the ongoing efforts to develop a more adaptive and resilient online learning landscape, marking a significant advancement in the fields of educational technology and pedagogy.

Authors and Affiliations

A. B Feroz Khan, Saleem Raja Abdul Samad

Keywords

Related Articles

Employing Microsoft Excel for Enhanced Mathematical and Statistical Online Pedagogy in Economics Amidst a Pandemic

During the COVID-19 pandemic, a profound impact was experienced in various domains, including education. The abrupt shift to remote learning presented challenges, especially in subjects necessitating practical problem-so...

A Study on the Influencing Factors and Enhancement Strategies of Undergraduates’ Research Quality in the Context of Online Education

In the digital age, technological advancements have reshaped the global educational landscape, prompting governments and educational institutions to recognize the critical role of research and innovative talent in drivin...

Education of Children on the Recognition of Geometric Shapes Using New Technologies

In today’s digital age, new technological tools are increasingly becoming an indispensable part of the educational process, particularly in educating children. The development of technology, including tablet computers, a...

Exploring the Impact of ChatGPT on Mathematics Performance: The Influential Role of Student Interest

This investigation examines the influence of ChatGPT on mathematics achievement, with a specific focus on the moderating role of students’ interest in mathematics. A sample of 250 students, encompassing undergraduates pu...

Evaluating the Readability of English Instructional Materials in Pakistani Universities: A Deep Learning and Statistical Approach

In educational settings of Pakistan, where English is utilized as the primary medium of instruction but not as an official language, the assessment of instructional text readability is crucial. This research investigates...

Download PDF file
  • EP ID EP732668
  • DOI https://doi.org/10.56578/esm020103
  • Views 47
  • Downloads 2

How To Cite

A. B Feroz Khan, Saleem Raja Abdul Samad (2023). Evaluating Online Learning Adaptability in Students Using Machine Learning-Based Techniques: A Novel Analytical Approach. Education Science and Management, 2(1), -. https://europub.co.uk./articles/-A-732668