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

Mindset reading of a student towards technology is a challenging task. The student’s demographic features prediction has a significant aspect for the learning activities in educational institutions. The current studies predicted the student’s native place based on technological awareness having various features such as development, availability, usability, educational benefits, etc. However,these studies have not explored the identification of sentiment identification about the technology through ML,optimization,etc.Motivated from these facts,in this paper, we propose a machine learning (ML) model with optimizing techniques to tune the hyper-parameters. In the proposed model, a primary dataset gathered from Indian and Hungarian universities, which analyzed with a Multi-Layer-Perceptron (MLP) with three popular optimization algorithms, such as Adaptive Moment Estimation (Adam), Stochastic Gradient Descent (SGD), Limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS). The optimized MLP has compared with the Support Vector Machine (SVM). Besides, numerous testing methods and to select the most prominent features, Principal Component Analysis (PCA) trained both models. Association of the Adam optimizer with the ReLu activation function in the MLP proved significant play in prediction with regularization. The PCA components covering most of the variance improved the optimized MLP accuracy with 2.3% and boosted the accuracy of the SVM with 2.9%. The Gain-ratio and the Info-gain suggested 11 features with significant weights. Both predictive models are found not only competitive but also outperformed with an identical prediction accuracy of 94% to identify the native place of the student. The Statistical t-test supported the equal predictive strength of both models and proved the significant enhancement in the SVM performance using the PCA components. Further, a considerable reduction is also achieved in the prediction error and prediction time to support the institute’s web-based real-time system. Based on deep experiments, we recommend the optimistic native identification models for the higher educational institutions to analyze the attitude and technical awareness among students based on their native place.

[1]  Jorge Nocedal,et al.  A Stochastic Quasi-Newton Method for Large-Scale Optimization , 2014, SIAM J. Optim..

[2]  Sapan H. Mankad.  Predicting learning behaviour of students: Strategies for making the course journey interesting , 2016, 2016 10th International Conference on Intelligent Systems and Control (ISCO).

[3]  Chaman Verma,et al.  Age Group Predictive Models for the Real Time Prediction of the University Students using Machine Learning: Preliminary Results , 2019, 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT).

[4]  Zoltán Illés,et al.  National Identity Predictive Models for the Real Time Prediction of European School’s Students: Preliminary Results , 2019, 2019 International Conference on Automation, Computational and Technology Management (ICACTM).

[5]  Juan A. Gómez-Pulido,et al.  Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review , 2020, Applied Sciences.

[6]  Florin Leon,et al.  Predicting Academic Performance Based on Learner Traces in a Social Learning Environment , 2018, IEEE Access.

[7]  Chaman Verma,et al.  Towards enthusiasm prediction of Portuguese school's students towards higher education in realtime , 2020, 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM).

[8]  Chaman Verma,et al.  Smart Approach for Real-Time Gender Prediction of European School’s Principal Using Machine Learning , 2020 .

[9]  D. Surendran,et al.  Classification of Neurodegenerative Disease Stages using Ensemble Machine Learning Classifiers , 2019, Procedia Computer Science.

[10]  Deepak Mehta,et al.  Prediction of Cancer Diagnosis Patients from Fine-Needle Aspirates Using Machine Learning , 2020 .

[11]  Rashmi Agrawal,et al.  Analysis of Educational Data Mining using Classification , 2019, 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon).

[12]  Mohammad S. Obaidat,et al.  Automated Machine Learning: The New Wave of Machine Learning , 2020, 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA).

[13]  Hironori Washizaki,et al.  Student placement and skill ranking predictors for programming classes using class attitude, psychological scales, and code metrics , 2018, Research and Practice in Technology Enhanced Learning.

[14]  Raoul Kwuimi,et al.  Educational Data Mining to Improve Learner Performance in Gauteng Primary Schools , 2018, 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD).

[15]  Jing Zhang,et al.  Predicting Student Behaviors and Performance in Online Learning Using Decision Tree , 2018, 2018 Seventh International Conference of Educational Innovation through Technology (EITT).

[16]  Chaman Verma,et al.  Ensemble Methods to Predict the Locality Scope of Indian and Hungarian Students for the Real Time: Preliminary Results , 2021 .

[17]  V.P. Bresfelean,et al.  Analysis and Predictions on Students' Behavior Using Decision Trees in Weka Environment , 2007, 2007 29th International Conference on Information Technology Interfaces.

[18]  Zoltán Illés,et al.  Prediction of residence country of student towards information, communication and mobile technology for real-time: preliminary results , 2020 .

[19]  Shubha Puthran,et al.  Educational Data Mining techniques and their applications , 2015, 2015 International Conference on Green Computing and Internet of Things (ICGCIoT).

[20]  Chaman Verma,et al.  Study Level Prediction of Indian and Hungarian Students towards ICT and Mobile Technology for the Real-Time , 2020, 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM).

[21]  Neeraj Kumar,et al.  Machine Learning Models for Secure Data Analytics: A taxonomy and threat model , 2020, Comput. Commun..

[22]  Chaman Verma,et al.  Feature selection to identify the residence state of teachers for the real-time , 2020, 2020 International Conference on Intelligent Engineering and Management (ICIEM).

[23]  Zoltán Illés,et al.  Predictive Modeling to Predict the Residency of Teachers Using Machine Learning for the Real-Time , 2019 .

[24]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[25]  Zdzislaw Polkowski,et al.  Machine Learning-based Software Effort Estimation: An Analysis , 2019, 2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI).

[26]  Chaman Verma,et al.  Gender Prediction of the European School’s Teachers Using Machine Learning: Preliminary Results , 2018, 2018 IEEE 8th International Advance Computing Conference (IACC).

[27]  Tutut Herawan,et al.  A Systematic Review on Educational Data Mining , 2017, IEEE Access.

[28]  Wei-Chiang Hong,et al.  Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward , 2020, IEEE Access.

[29]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[30]  Alexander J. Smola,et al.  Efficient mini-batch training for stochastic optimization , 2014, KDD.

[31]  Ehsan Sheybani,et al.  Educational Data Mining and Learning Analytics: Overview of Benefits and Challenges , 2017, 2017 International Conference on Computational Science and Computational Intelligence (CSCI).

[32]  Michael M. Richter,et al.  Applied Machine Learning , 2009 .

[33]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[34]  Chaman Verma,et al.  Real-time classification of national and international students for ICT and mobile technology: an experimental study on Indian and Hungarian University , 2020 .

[35]  Johannes Berens,et al.  Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods , 2018, SSRN Electronic Journal.

[36]  Maliha S. Nash,et al.  Handbook of Parametric and Nonparametric Statistical Procedures , 2001, Technometrics.

[37]  Chaman Verma,et al.  Prediction of students’ awareness level towards ICT and mobile technology in Indian and Hungarian University for the real-time: preliminary results , 2019, Heliyon.

[38]  K. Sreenivasa Rao,et al.  Educational data mining for student placement prediction using machine learning algorithms , 2017 .

[39]  G.Shiyamala Gowri,et al.  Educational Data Mining Application for Estimating Students Performance in Weka Environment , 2017 .

[40]  Mohamed Elemam Shehab,et al.  Predicting student personality based on a data-driven model from student behavior on LMS and social networks , 2015, 2015 Fifth International Conference on Digital Information Processing and Communications (ICDIPC).