Using big data to assess an affective domain for distance education

Abstract

Distance education is popular worldwide, especially after the COVID-19 pandemic. Assessing the affective domain of distance education becomes challenging because the instructor cannot observe the student’s behavior. This article proposes an assessment of the affective domain for distance education systems implemented with big data technologies. An assessment model called AMADEUS has been developed. Exploratory factor analysis (EFA) was used to explore assessment factors and formulate the model. Then, the constructed model was verified by confirmatory factor analysis (CFA). AMADEUS consists of 12 factors in 4 components: responsibility, interest in course material, class participation, and honesty. The experimental results indicated that AMADEUS was consistent with the empirical data and passed all criteria for consideration. After that, an assessment and reporting tool called MOZART has been developed. MOZART employed the AMADEUS model to assess students’ affective domain from log files using Hadoop and ELK stack technologies. The MOZART usability assessment results are at a good level in all aspects.