Machine Learning-Based Student’s Native Place Identification for Real-Time

Verma, Chaman; Stoffová, Veronika; Illés, Zoltán; Tanwar, Sudeep; Kumar, Neeraj

doi:10.1109/access.2020.3008830

Cited by 79 publications

(20 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A very new functionality in which implementation is in progress in the time of writing the paper is the export possibility of needed data towards the university Canvas Learning Content Management System (LCMS) Canvas Server. One of our further plans is implementing the opinion prediction module in our E-lection Server, including real-time native place identification of students [31]. Figure 2 visualizes the simplified communication process flow.…”

Section: Real-time Opinion Prediction Using E-lectionmentioning

confidence: 99%

Opinion Prediction of Hungarian Students for Real-Time E-Learning Systems: A Futuristic Sustainable Technology-Based Solution

et al. 2020

Self Cite

View full text Add to dashboard Cite

This work is a new step towards the understanding of students’ opinions about the use of technology in learning and improvements to provide sustainable E-learning solutions. Every higher educational university tries to provide well-suited, updated, and trending technology-based education facilities to its students. The task of analyzing the student’s sentiment about technology delivers benefits not only to ICT administrators, but also to management to become aware of the technological concerns. The opinions of Hungarian university students were analyzed using the regression method. We investigated 165 primary samples supported by the four hypotheses. The reliability of the data sample was calculated as 0.91 with Cronbach alpha testing. The Pearson Momentum Correlation (PMC) proved that the suggested technology benefits had a linear positive association with the student’s opinion. Furthermore, technology usability was positively correlated with the benefits. The supporting results of the regression model evidenced the significant impact of technology usability and benefits on the opinions. Using Exploratory Factor Analysis (EFA), we proposed significant features for the model that predicted students’ opinions using the educational benefit and usability parameters. These parameters statistically significantly predicted student’s opinions: F (2, 162) = 104.9, p < 0.05, R2 = 0.559. This study may be supportive of implementing the opinion mining model online and useful to university authorities to understand better the students’ sentiments about the current technological facilities provided. The authors proposed an opinion mining model to deploy on the university’s real-time “E-lection” sustainable technology.

show abstract

Section: Real-time Opinion Prediction Using E-lectionmentioning

confidence: 99%

Opinion Prediction of Hungarian Students for Real-Time E-Learning Systems: A Futuristic Sustainable Technology-Based Solution

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Earlier literature had not described the online models with significant features to predict attitude. We proposed a few student's demography identification models for real-time development [3], [4], [12]. These predictive models had not proposed significant features and even had not focused on the online perception measurement.…”

Section: A Research Motivationmentioning

confidence: 99%

“…A statistical analysis helped to explore student's attitudes towards using ICT in a social constructivist environment [2]. Recently, predictive modeling has been used to predict the student's birthplace [3], and gender towards ICT for the realtime system [4]. Against the use of ICT in social, work, and study, student attitude was identified with Linear regression [5].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Predicting Attitude of Indian Student’s Towards ICT and Mobile Technology for Real-Time: Preliminary Results

et al. 2020

Self Cite

View full text Add to dashboard Cite

This paper proposed a novel futuristic approach to support the educational informatics and overcome the conventional system of attitude measure. For this, we presented a significant predictive model to identify the attitude of students towards technology. The present approach not only explored the impact of the technology but also predicted an opinion of students. The concept of an online awareness model may overcome the traditional method. We have performed the descriptive and inferential statistics to predict the attitude of Indian students towards the ICTMT in university education with primary data samples. Factor Analysis (FA) using Principal Component Analysis (PCA) has extracted the prominent two components with nine features for technology benefits and six features for the technology use. The SQuareRoot (SQRT) and Log transformations have been used to decrease the Skewness, and it has also improved the association between attitude and educational benefit. Overall reliability of the gathered data sample size of 163 calculated 0.957 provided with Cronbach alpha test. This study has used a Pearson Correlation (PC) for exploring the technology impact and the Linear Regression Model (LRM) for the prediction. The LRM has substantiated that the educational benefit explained the attitude significantly, and a significant positive association discovered using the PC with a value of 0.75. The LRM model projected nine most significant educational benefits of ICTMT, which affected the attitude of the Indian student towards technology. We have proposed technology aids in building online predictive model wires real-time prediction.

show abstract

“…Not even the education domain stays untouched. To explore the hidden data patterns, the use of machine learning techniques play a vital role [1]. For this, two major types (supervised and unsupervised) of machine learning algorithms were used to solve various problems.…”

Section: Introductionmentioning

confidence: 99%

“…It also ensures the heterogeneity among the cluster relationships [4], [5]. According [6], the general mathematical notation of clustering shown in equation (1).…”

Section: Introductionmentioning

confidence: 99%

Student’s Belief Detection and Segmentation for Real-Time: A Case Study of Indian University

Verma¹,

Illés²,

Stoffová³

2020

Adv. sci. technol. eng. syst. j.

Self Cite

View full text Add to dashboard Cite

This paper has explored the technology beliefs of university students considering four parameters. We have proposed an automatic belief identification system for academic institutions. For this, we used two different clustering algorithms to segment the student group with different beliefs about the technology. In the Hierarchical Clustering (HC), the Agglomerative approach was followed. The beliefs were segmented with Ward's method and Squared Euclidean Distance (SED). The HC method recommended a maximum of three and a minimum of two optimal clusters. Later, we applied K-Means clustering on 37 features to validate the initial cluster solution. Based on ANOVA's results, we select 20 significant features that contributed most to detect dissimilarity in students' beliefs. The findings of the paper proved that suggested features stabilized clustering as compared to all features. The novel features provided three clusters: cluster 1 with 27.61% ; cluster 2 with 34.36%; cluster 3 with 38.04% students with similar beliefs about the technology. Based on the results provided, we found the high (mean>3.5), undecided (mean:1.73-3.63), and hybrid (mean:1.34-4.68) beliefs towards the technology available at university. We also recommended the selected features to be used as predictors for the online belief detection system. The university administration needs to cure students belonged to undecided groups.

show abstract

Machine Learning-Based Student’s Native Place Identification for Real-Time

Cited by 79 publications

References 41 publications

Opinion Prediction of Hungarian Students for Real-Time E-Learning Systems: A Futuristic Sustainable Technology-Based Solution

Opinion Prediction of Hungarian Students for Real-Time E-Learning Systems: A Futuristic Sustainable Technology-Based Solution

Predicting Attitude of Indian Student’s Towards ICT and Mobile Technology for Real-Time: Preliminary Results

Student’s Belief Detection and Segmentation for Real-Time: A Case Study of Indian University

Contact Info

Product

Resources

About