Identification of Student’s Demographic, Geographic Features and Opinions towards ICT and Mobile Technology

dentification of Student’s Demographic, Geographic Features and Opinions towards ICT and Mobile Technology Awareness (Summary) The present compared the Information and Communication Technology and Mobile Technology (ICTMT) awareness among students of Indian and Hungarian universities located on different continents. I performed the differential, the inferential, and the predictive analysis of students’ perceptions towards ICTMT. The opinions of Hungarian university students were analyzed using the Multiple Linear Regression (MLR) methods and predicted high. The Pearson correlation exhibited that technology benefits positively correlate with the student’s opinion and technology usability. Additional, Exploratory Factor Analysis (EFA) recommended significant features for the MLR model that predicted students’ opinions using the educational benefit and usability parameters. Therefore, a significant impact on technology usability and benefits has been observed in the Hungarian student’s opinions. Later, the attitude of Indian students was identified with the LR approach based on the technology benefit of ICTMT provided. The Pearson Correlation explored a significant positive correlation between their attitude and ICTMT benefits. All the features were recommended with EFA with PCA approach. Afterward, the Mann-Whitney U test explored the opinion disparity between Indian and Hungarian students regarding the technology use and benefits. The impact of technology benefits on the country has also been advocated with the T-test. The Correspondence Analysis (CA) method exhibited a relevant association of technology usage among Indian and Hungarian students. It also proved no significant association of technology benefits with Indian students, but it discovered a meaningful association of technology benefits with Hungarian students. An in-depth analysis of their opinion was also evaluated with the Chi2 , Fisher's Exact (FE), and Cramer's v (CV) tests. Subsequently, the Mann-Whitney U test found a significant disparity between the Hungarian and Indian students' attitudes. Then, the opinion towards technological progress and access were compared with Kruskal--Wallis H test, Welch's t- test, and the Mann-Whitney U test. These statistical tests have discovered a significant dissimilarity between the Indian and Hungarian students' opinions. The optimistic machine learning models: Support Vector Machine (SVM) and Multilayer Perceptron (MLP) followed by PCA, were presented to predict the student’s nations based on their opinion towards technology. I have also presented another machine learning predictive models to identify the student’s gender and locality towards ICTMT. The research findings might be helpful to several stakeholders: students, teachers, parents, institutions, and country administration. The students of the two countries can be aware of technology trends in education. The institution’s management, technical coordinator, and teacher can also know about students’ technology awareness and train accordingly.