A Machine Learning Model to Early Detect Low Performing Students from LMS Logged Interactions
- 514 Downloads
Grade prediction has been for a long time a subject that interests both teachers and researchers. Before the digital age this type of predictions was something nearly impossible to achieve. With the increasing integration of Learning Management Systems in education, grade prediction seems to have become a viable option. The general adoption of this type of systems brings to the research area a database known as “registry”, or more simply known as logged data. Using this new source of information several attempts regarding the prediction of student grades have been proposed. The methodology proposed in this study is capable of, analyzing student online behavior, using the information collected by the Moodle system and making a prediction on what the final grade of the student will be, at any point in the semester. Our novel approach uses the gathered information to examine the academic path of the student in order to determine an interaction pattern, then it tries to establish a link with other, present or past, known successful paths. Making this comparison, the model can automatically determine if a student is going to fail or pass the course, which then would leave a space for the teacher or the student to circumvent the situation. Our results show that the system is not only viable, as it is also robust to make prediction at an early stage in the course.
Keywordse-Learning Machine learning Prediction of failures Data mining
This study is financed by National Funds through the Portuguese funding agency, FCT – “Fundação para a Ciência e a Tecnologia”, within the project: UID/EEA/50014/2019.
- 2.Gašević, D., Dawson, S., Rogers, T.: Learning analytics should not promote one size fits all: the effects of instructional conditions in predicting academic success. Internet High. Educ. 68–84, 28 (2016)Google Scholar
- 3.Figueira, Á.: Mining Moodle logs for grade prediction: a methodology walk-through. In: Proceedings of the 5th International Conference on Technological Ecosystems for Enhancing Multiculturality, p. 44 (2017)Google Scholar
- 7.Joksimović, S., Gašević, D., Loughin, T.M., Kovanović, V., Hatala, M.: Learning at distance: effects of interaction traces on academic achievement. Comput. Educ. 204–217, 87 (2015)Google Scholar