Maximizing Learning Outcomes while Minimizing Costs: A Cost-Effective Approach to M-Learning

Sivaganesan, D (2024) Maximizing Learning Outcomes while Minimizing Costs: A Cost-Effective Approach to M-Learning. International Journal of Interactive Mobile Technologies (iJIM), 18 (11). pp. 4-14. ISSN 1865-7923

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Abstract

In the realm of education, the pursuit of effective learning outcomes often faces the challenge of limited resources. This paper explores the intersection of maximizing learning outcomes and minimizing costs through a cost-effective approach to mobile learning (m-learning). Recognizing the ubiquitous presence of mobile devices and their potential to revolutionize learning, this study explores strategies that leverage the capabilities of mobile technology while being mindful of budgetary constraints. By leveraging the flexibility and accessibility offered by m-learning platforms, educators can design engaging and interactive learning experiences that cater to diverse learners needs. The paper investigates various cost-effective methodologies, including utilizing open-source software, repurposing existing resources, and adopting collaborative learning environments. Furthermore, it examines the role of scalable technologies and adaptive learning algorithms in optimizing resource allocation and personalizing learning experiences. Through case studies and empirical analysis, this research illustrates how institutions and educators can achieve significant cost savings without compromising the quality of educational delivery. Ultimately, the findings highlight the transformative potential of a cost-effective approach to m-learning in enhancing learning outcomes and widening access to education.

Item Type: Article
Uncontrolled Keywords: Cost-effective approach; Effective Learning Outcomes; Learning experiences; Learning outcome; M-Learning; Minimizing costs; Mobile learning; Mobile Technology; Resources optimization
Subjects: C Computer Science and Engineering > Optimization Techniques
C Computer Science and Engineering > Mobile Computing
C Computer Science and Engineering > Machine Learning
Divisions: Computer Science and Engineering
Depositing User: Dr Krishnamurthy V
Date Deposited: 14 Sep 2024 03:21
Last Modified: 14 Sep 2024 03:22
URI: https://ir.psgitech.ac.in/id/eprint/1134

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