In recent years Educational Data Mining (EDM) has emerged as a new field of research due to the development of several statistical approaches to explore data in educational context. One such application of EDM is early prediction of student results. This is necessary in higher education for identifying the "weak" students so that some form of remediation may be organized for them. In this paper a set of attributes are first defined for a group of students majoring in Computer Science in some undergraduate colleges in Kolkata. Since the numbers of attributes are reasonably high, feature selection algorithms are applied on the data set to reduce the number of features. Five classes of Machine Learning Algorithm (MLA) are then applied on this data set and it was found that the best results were obtained with the decision tree class of algorithms. It was also found that the prediction results obtained with this model are comparable with other previously developed models.
The aim of this article is to propose a method for development of concept map in webbased environment for identifying concepts a student is deficient in after learning using traditional methods. Direct Hashing and Pruning algorithm was used to construct concept map. Redundancies within the concept map were removed to generate a learning sequence. Prototype learning system was developed based on this learning sequence using Android Emulator. For analysis purpose, 42 learners were given to learn the course Java Programming taught at graduation level. A posttest was conducted after learning for evaluation purpose. Multiple regression analysis method was applied on these results to develop regression equations for the proposed method of learning. Statistical Package for Social Sciences software was used for statistical analysis purposes. It was found that posttest results are directly proportional to the quality of traditional learning. Better quality students require less time in constructing prototype system. Further concept mapping was found to have a positive impact on proposed method of learning. When the number of concepts is large, a learning sequence among these can be generated using the proposed method. This learning sequence can be used to identify the concept a student needs additional learning.
This study uses homogeneity in personal learning styles and heterogeneity in subject knowledge for collaborative learning group decomposition indicating that groups are “mixed” in nature. Homogeneity within groups was formed using K-means clustering and greedy search, whereas heterogeneity imbibed using agenda-driven search. For checking learning effectiveness, a simple schema of collaborative learning was proposed and prototype learning system developed using Android Emulator. Multiple regression analysis was applied on their learning results to derive regression coefficients for determining learning efficiency. The derived set of regression coefficients suggests more the time taken to form groups, better the student learning quality.
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