Gender Prediction of the European School’s Teachers Using Machine Learning: Preliminary Results

Verma, Chaman; Tarawneh, Ahmad S.; Illés, Zoltán; Stoffová, Veronika; Dahiya, Sanjay

doi:10.1109/iadcc.2018.8692100

Cited by 22 publications

(8 citation statements)

References 8 publications

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“…This section explores the results of experiments conducted using statistical T-test at 0.05 level of significance with Weka Experiment environment. To evaluate the performance of classification algorithms in terms of prediction accuracy versus CPU training time with the help of statistical analysis is significant and suggested [29, 30]. To present a real-time significant model, this experiment compared the induced User CPU time to predict the student's awareness level.…”

Section: Methodsmentioning

confidence: 99%

“…The Support Vector Machine (SVM) is a supervised learning model introduced for binary classification in both linear and nonlinear versions [11, 12]. SVM performs classification by constructing an N-dimensional hyperplane that optimally separates the data into the two categories [13, 14, 15]. With the use of boosting technique, ANN generates a sequence of models to obtain more accurate predictions which are also called the ensemble model [16].…”

Section: Introductionmentioning

confidence: 99%

“…The objective is to learn a function (f):X ⇒ Y in which predictors f(x):X can confidently be predicting the corresponding target Y which is Awareness levels. The demographic features of teachers and students were predicted in Asian and European institutions using machine learning [15, 17, 18, 19, 20]. Also, many of researcher had worked on educational datasets using machine learning classifiers as well [21, 22, 23, 24, 25].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of students’ awareness level towards ICT and mobile technology in Indian and Hungarian University for the real-time: preliminary results

Verma¹,

Stoffová²,

Illés³

2019

Heliyon

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of students’ awareness level towards ICT and mobile technology in Indian and Hungarian University for the real-time: preliminary results

Verma¹,

Stoffová²,

Illés³

2019

Heliyon

Self Cite

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“…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

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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.

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Smart Approach for Real-Time Gender Prediction of European School’s Principal Using Machine Learning

Bathla

Verma

Kumar

2019

Lecture Notes in Electrical Engineering

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Gender Prediction of the European School’s Teachers Using Machine Learning: Preliminary Results

Cited by 22 publications

References 8 publications

Prediction of students’ awareness level towards ICT and mobile technology in Indian and Hungarian University for the real-time: preliminary results

Prediction of students’ awareness level towards ICT and mobile technology in Indian and Hungarian University for the real-time: preliminary results

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

Smart Approach for Real-Time Gender Prediction of European School’s Principal Using Machine Learning

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