Genetic algorithm based prediction of students' course performance using learning analytics.
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Date
2024
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Abstract
Learning Analytics (LA) can play a key role in understanding students’ learning and academic
performance. By identifying poorly performing students early, LA can also be used to identify
students who are at risk of dropping out of programmes. This enables academic advisors to
intervene early and provide help to ensure students stay on track and succeed in their studies.
Hence, LA is becoming a common trend in education particularly in higher education. Previous
studies of LA have not dealt with specific courses in information systems and information
technology. Therefore, the aim of this study was to develop a model for the application of LA to
different courses with the discipline of Information Systems and Technology using various data
sources. This study used the design science research approach to help towards solving the problem
of understanding students’ learning and performance in Higher Education Institutions (HEIs).
Multiple data sources were used. The data that was obtained was pre-processed using MS Excel.
Thereafter, the WEKA tool was used in the analysis of the data and prediction of performance.
Decision tree, Random Forest and genetic-based algorithms were used to develop prediction
models for each of the courses in the discipline of Information Systems and Technology at the
University of KwaZulu-Natal.
The study also resulted in the development of an integrated dataset for the discipline of Information
Systems and Technology in higher education and a process model for the implementation of LA
in a specific discipline. The involvedness of the data allows future researchers to continuously
improve/evolve the area of LA. This study should, therefore, be of value to LA practitioners
wishing to implement LA to courses within other disciplines as well.
Description
Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.