Firta Sari Panjaitan, Sonya Enjelina Gorat

Web-Based on-line learning (e-learning) decision support system

Introduction

Web-based on-line learning (e-learning) decision support system. Enhance e-learning with Web-Based Decision Support Systems (DSS). Optimize online education, boosting student engagement, knowledge retention, and academic achievement effectively.

Abstract

The rapid advancement of technology has revolutionized education, paving the way for innovative learning methods such as E-Learning. However, optimizing the effectiveness of online education poses challenges in data management and decision-making processes. This research investigates the integration of Web-Based Decision Support Systems (DSS) in E-Learning to enhance learning outcomes. The study develops a mathematical formulation that quantifies the impact of DSS by considering student engagement, knowledge retention, and academic achievement. A numerical example is presented to demonstrate the application of the formulation, showcasing the positive influence of the DSS on individual students and the overall cohort. The results emphasize the potential benefits of personalized learning experiences, data-driven insights, and informed decision-making facilitated by the DSS. Nonetheless, the limitations of the study are acknowledged, warranting further research with larger and more diverse samples. Overall, this research contributes to the discourse surrounding the role of Web-Based DSS in shaping the future of online education, empowering educators and learners to unlock the full potential of E-Learning in the digital age

Review

This paper addresses the increasingly pertinent challenge of optimizing online education's effectiveness by integrating Web-Based Decision Support Systems (DSS) into E-Learning. In an era where technological advancements are rapidly reshaping educational paradigms, the research offers a timely investigation into how DSS can mitigate issues related to data management and decision-making in digital learning environments. The core objective is to enhance learning outcomes, a critical area for improving the quality and reach of online education, positioning the study as a valuable contribution to the discourse on E-Learning's future. The methodology employed involves the development of a mathematical formulation designed to quantify the impact of DSS, meticulously considering key metrics such as student engagement, knowledge retention, and academic achievement. This quantitative approach is a strength, providing a structured framework for analysis. The subsequent presentation of a numerical example effectively demonstrates the practical application of this formulation, illustrating the positive influence of the DSS on both individual student performance and the overall cohort. The results highlight the promising potential of DSS to facilitate personalized learning experiences, offer data-driven insights, and enable more informed decision-making, thereby empowering educators and learners alike. While the research presents compelling arguments and a clear demonstration of the DSS's potential, it commendably acknowledges its limitations, specifically regarding the sample size and diversity. This self-awareness is crucial for scientific rigor, indicating that the generalizability of these findings may be constrained without broader validation. Future research would benefit significantly from expanding the scope to include larger and more diverse samples, which would strengthen the robustness of the conclusions. Despite these acknowledged limitations, the study successfully contributes to the academic conversation surrounding the strategic role of Web-Based DSS in shaping the trajectory of online education, laying foundational work for further advancements in this critical field.

Full Text

Comments

You need to be logged in to post a comment.