This paper deals with the comparative analysis of prediction classifiers in the blended learning environment. The model proposed in this paper predicts students’ final grades based on activities within different educational environments. A comparative study of classifier performance has been performed in order to determine the classifier most suitable for multiclass feature dataset. Important results for different classes have been obtained using different classifiers, and the majority vote scheme is subsequentially used to form an ensemble based on Naïve Bayes, Hidden Naïve Bayes, J48 decision tree and Random Forest. According to experimental evaluation, there is a significant improvement of proposed model's precision and accuracy regarding the students’ grades prediction in blended learning environment scenario. The major contribution of the research presented in this paper is an efficient multi‐class prediction model applicable to aforementioned environment.
The paper suggests the implementation of association analysis for improving the process of e‐testing in blended learning environment. The research has been conducted using knowledge tests at the Computer Graphics Moodle Course. In the preprocessing phase, data matrices have been created and prepared for the process of discovering significant relationships and links between students' answers to the questions from preparatory tests and those for testing knowledge, the ways of doing, and achieved results. By implementing Apriori and Predictive Apriori algorithms, a great number of association rules has been discovered. Important and interesting rules have been singled out by implementing objective and subjective assessment measures. The examples of interesting rules, as well as discovered patterns in items' relationships, have also been presented. The contribution of the described case study is visible in providing important feedback which enables the teacher to get a better insight into the concepts of created tests and decide on how to make changes to improve testing.
Purpose
This paper aims to deal with the previously unknown prediction accuracy of students’ activity pattern in a blended learning environment.
Design/methodology/approach
To extract the most relevant activity feature subset, different feature-selection methods were applied. For different cardinality subsets, classification models were used in the comparison.
Findings
Experimental evaluation oppose the hypothesis that feature vector dimensionality reduction leads to prediction accuracy increasing.
Research limitations/implications
Improving prediction accuracy in a described learning environment was based on applying synthetic minority oversampling technique, which had affected results on correlation-based feature-selection method.
Originality/value
The major contribution of the research is the proposed methodology for selecting the optimal low-cardinal subset of students’ activities and significant prediction accuracy improvement in a blended learning environment.
In this work we present a new approach in teaching the subject Programmable logic devices with the use of Moodle platform. A special accent is put on implementation of a multimedia laboratory guide, the contents and conception of which facilitate realization of practical tasks related to designing, implementation and testing of hardware in the development environment Quartus II, which includes a software package and UP2 development system and is used in laboratory exercises in the third grade of basic studies at College of Electrical Engineering and Computer Science in Belgrade. Specific characteristics of the multimedia laboratory guide lie in a possibility of parallel operation of a video tutorial and real software package, with full simulation of the hardware environment for design testing.
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