Abstract
An experimental study was conducted to predict the student's awareness of Information and Communication Technology (ICT) and Mobile Technology (MT) in Indian and Hungarian university's students. A primary dataset was gathered from two popular universities located in India and Hungary in the academic year 2017–2018. This paper focuses on the prediction of two major parameters from dataset such as usability and educational benefits using four machine learning classifiers multilayer perceptron (ANN), Support vector machine (SVM), K-nearest neighbor (KNN) and Discriminant (DISC). The multi-classification problem was solved with test, train and validated datasets using machine learning classifiers. One hand, feature aggregation with the train-test-validation technique improved the ANN's prediction accuracy of educational benefits for both countries. Another hand, ANN's accuracy decreases significantly in the prediction of usability. Further, SVM and ANN outperformed the KNN and the DISC in the prediction of awareness level towards ICT and MT in India and Hungary. Also, this paper reveals that the future awareness level for the educational benefits will be Very High or Moderate in both countries. Also, the awareness level is predicted as High and Moderate for usability parameter in both countries. Further, ANN and SVM accuracy and prediction time is compared with T-test at 0.05 significance level which distinguished CPU training time is taken by ANN and SVM using K-fold and Hold out method. Also, K-fold enhanced the significant prediction accuracy of SVM and ANN. the authors also used a STAC web platform to compare the accuracy datasets using T-test and ANOVA test at 0.05 significant level and we found ANN and SVM classifier has no significant difference in prediction accuracy in each dataset. Also, the authors recommend presented predictive models to be deployed as a real-time module of the institute's website for the real-time prediction of ICT & MT awareness level.
Highlights
Data mining often called knowledge discovery in database (KDD), is known for its powerful role in uncovering hidden information from large volumes of data [1]
Educational Data Mining (EDM) is a budding discipline related with innovative methods for discovering the exclusive and increasingly big data that come from the educational background and using those methods to better understand to the stakeholders [3]
First section belongs to 9 demographic attributes, second section belongs to the Development-Availability (DA) with 16 attributes, third section relates to the Attitude (A) with 6 attributes, fourth section belongs to the Usability (U) parameter with 6 attributes and last section belongs to the Educational Benefits
Summary
Data mining often called knowledge discovery in database (KDD), is known for its powerful role in uncovering hidden information from large volumes of data [1]. We just train datasets with test and validate input with preconceived output, having the idea that there is a relationship between the input and the output For this many machine learning classifiers are trending to classify the data patterns in various fields [10]. The presented predictive models may help in the development of the real-time ICT based prediction system to predict future awareness in stakeholders towards the use of the latest ICT and MT resources. The responses of students may be recorded on real-time website of the university and the predictive models may be useful to predict the future attitude, awareness levels and demographic features of the students towards the technological access
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