A multi-criteria decision-making (MCDM) approach for data-driven distance learning recommendations

Area of Science:

• Education
• Educational Technology
• Online Learning

Background:

• The COVID-19 pandemic necessitated a global shift to distance learning, often termed Emergency Remote Teaching (ERT).
• This rapid transition highlighted challenges in technological adoption, student engagement, parental involvement, and teacher workload.
• Existing educational frameworks required adaptation to assess and manage remote learning outcomes.

Purpose of the Study:

• To analyze distance learning alternatives and their impact on student academic performance and retention.
• To evaluate stakeholder utilization of distance learning tools and criteria influencing its effectiveness.
• To propose a model for identifying and recommending optimal distance learning strategies.

Main Methods:

• Examined stakeholder use of distance learning for educational objectives.
• Evaluated various distance learning alternatives and influencing criteria.
• Developed a multi-criteria decision-making model to score and rank alternatives.
• Integrated a recommendation system for customized student and teacher support.

Main Results:

• Identified key factors influencing the effectiveness of distance learning alternatives.
• The proposed model successfully assigns weights to alternatives for optimal selection.
• The recommendation system provides tailored strategies to enhance student outcomes.

Conclusions:

• Distance learning, including ERT, requires careful selection of alternatives to maximize student academic performance.
• The developed multi-criteria decision-making and recommendation model offers a framework for improving online education effectiveness.
• This research provides valuable insights for educators and policymakers navigating the landscape of distance learning.