Andreas F. Gkontzis
ABSTRACT
The domination of digital technology has expanded in the field of education and it is inevitably linked to e-learning methods. Learning management systems, as an integral part of distance learning infrastructure, support a global heterogeneous student population to interact with tutors, tools and applications. Exploitation and analysis of interaction data allow colleges to understand the differences in student learning progress and provide more personalized intervention. Implementation of learning analytics and machine learning tools can accurately predict students' achievements and failures. Early prediction could lead to prompt targeted action in order to improve learning outcomes.
This work proposes a learning analytics approach using data mining and machine learning to predict the grades of the four main assignments in an annual module of Hellenic Open University. By dividing the academic year into four periods, a data analysis workflow is developed to compare several regression algorithms to accurately predict students' marks for the assignments of each period. The paper concludes in the algorithm with the highest degree of precision that determines the predictability of the main written assignments. Subsequently, a statistical measure is applied to classify the influence of models' variables. In addition, the analytical framework provides a comparison of the actual and predicted values identifying students who have included third-party services in their assignments.
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Index Terms
- An effective LA approach to predict student achievement
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