Improvement of the Accuracy of Prediction Using Unsupervised Discretization Method: Educational Data Set Case Study

This paper presents a comparison of the efficacy of unsupervised and supervised discretization methods for educational data from blended learning environment. Naïve Bayes classifier was trained for each discretized data set and comparative analysis of prediction models was conducted. The research goal was to transform numeric features into maximum independent discrete values with minimum loss of information and reduction of classification error. Proposed unsupervised discretization method was based on the histogram distribution and implementation of oversampling technique. The main contribution of this research is improvement of accuracy prediction using the unsupervised discretization method which reduces the effect of ignoring class feature for educational data set

COMPARATIVE STUDY: FEATURE SELECTION METHODS IN THE BLENDED LEARNING ENVIRONMENT

Research presented in this paper deals with the unknown behavior pattern of students in the blended learning environment. In order to improve prediction accuracy it was necessary to determine the methodology for students` activities assessments. The Training set was created by combining distributed sources – Moodle database and traditional learning process. The methodology emphasizes data mining preprocessing phase: transformation and features selection. Information gain, Symmetrical Uncert Feature Eval, RelieF, Correlation based Feature Selection, Wrapper Subset Evaluation, Classifier Subset Evaluator features selection methods were implemented to find the most relevant subset. Statistical dependence was determined by calculating mutual information measure. Naïve Bayes, Aggregating One-Dependence Estimators, Decision tree and Support Vector Machines classifiers have been trained for subsets with different cardinality. Models were evaluated with comparative analysis of statistical parameters and time required to build them. We have concluded that the RelieF, Wrapper Subset Evaluation and mutual information present the most convenient features selection methods for blended learning environment. The major contribution of the presented research is selecting the optimal low-cardinal subset of students’ activities and a significant prediction accuracy improvement in blended learning environment

Razvoj metodologije za otkrivanje znanja u Moodle sistemu za upravljanje učenjem

Prediction of students' success is one of the topics of research of the area known as the discovery of knowledge from educational data sets. The aim of this dissertation is focused on determining the students' success factors in a blended learning environment by combined implementation of methods for knowledge discovery and in-depth analysis of data. A case study study conducted at the School of Electrical and Computer Engineering of Applied Studies in Belgrade has been described. Available data about the pedagogical process and the academic results of the Computer graphics course enabled comparative analyzes of the realized experiments. The outcome of the research is to establish a methodology for discovering knowledge from a blended learning environment. The conceptual model of prediction is based on realized student activities within the Moodle course and the classical way of teaching