This paper generalises the concept of Self-Learner to a problem of a finite set of entities which are required to achieve a goal within a predefined deadline.
The paper extends the original method focusing especially on targeting the problem of class imbalance. Again, the evaluation is performed on OULAD dataset.
The proposed improvements outperform the original method, and show that the best results are achieved if domain-driven techniques are utilised to tackle the imbalance problem. The improvements showed to be statistically
significant using Wilcoxon signed rank test.
This paper presents the concept of a ”self-learner” that builds the machine learning models from the data generated during the current course. The approach utilises information about already submitted assessments, which introduces the problem of imbalanced data for training and testing the classification models. The presented method proposes a solution in the absence of data from previous courses, which are usually used for training machine learning models. This situation typically occurs in new courses. OULAD was used as data source for the performed experiments.
This paper is intended to be useful for a first understanding of academic data analysis. What we can get and what we do need to do. This is the first of a series of reports that taken all together will provide a complete and consistent view towards the inclusion of data mining as a helping hand in the tutoring action. Mentions OULAD in its Proposal for data-set format section
In this paper, the authors predict students’ success in an online course using regression, clustering and classification methods.