The Identification of Student Learning Gap: Integrated Analytics Using Fuzzy Analytical Hierarchy Process and Profile Matching

In order to restrict the spread of the COVID-19 pandemic, physical face-to-face learning sessions were switched to online learning mode. However, the effectiveness of online learning for a developing country such as Indonesia is constrained by its digital education readiness, especially financial su...

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Bibliographic Details
Main Authors: Okfalisa, Jaya Putra, Ridho Anugrah, Rusnedy, Hidayati, Alias, Rose Alinda, Toto, Saktioto
Format: Conference Proceeding
Language:English
Subjects:
COVID-19
Decision Support System
Education
Fuzzy Analytical Hierarchy Process
Learning systems
Online Learning
Pandemics
Performance evaluation
Performance Measurement
Profile Matching
Technological innovation
Weight measurement
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Summary:In order to restrict the spread of the COVID-19 pandemic, physical face-to-face learning sessions were switched to online learning mode. However, the effectiveness of online learning for a developing country such as Indonesia is constrained by its digital education readiness, especially financial support, infrastructure, culture, and skills. Therefore, this paper analyzes the effectiveness of online learning by progressive analysis of the gap between offline and online learning methods. The Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) is applied to weigh the preferences of indicators set as criteria for measuring online learning efficacy. Meanwhile, Profile Matching is used to calculate the gap between both learning conditions during pre-and post-pandemic. The performance of fifty students at Faculty Economic and Social Universitas Islam Negeri Sultan Syarif Kasim Riau (FESUIN) was used as a case study. This paper revealed that students' grade point average (GPA) was the highest weighted criterion for student learning performance. It is followed by electricity resources, signal quality, learning-supported devices, and internet quotes. Thus, the value gap distinguished the potential of the face-to-face learning mode compared to online learning. The analysis shows that offline learning is the more effective learning method for this case.
ISSN:2324-8157
DOI:10.1109/ICRIIS53035.2021.9617033