Elasticnet Regressive Bagging Classification for Student Academic Performance Prediction Based on Smartphone Addiction
Keywords:
Student academic performance prediction, Smartphones addiction, Canberra match data normalization, elasticnet regressive attributes selection, Ensembled Bagging ClassificationAbstract
Smartphones is an integral part of student lives throughout the day for communication, and entertainment, utilities, social network, and gaming. Smartphone addiction among students has a negative effect on academic performance. Different researchers carried out their research on student academic performance prediction based on Smartphone usage. But, the prediction performance was not enhanced by using conventional methods in terms of sensitivity and specificity. In order to address these existing issues, Canberra Normalized Elasticnet Regressive Bagging Classification (CNERBC) technique is introduced. The CNERBC technique comprised three steps, namely data pre-processing, feature selection and classification for performing student academic prediction. Initially, Canberra Match Data Normalization step is carried out to pre-process the data to eliminate the repeated data from the database. After that, Elasticnet Regressive Attribute Selection is carried out to select the relevant attributes of the input dataset. After selecting the relevant attributes, a classification step is performed. Ensembled Bagging Classification comprises ‘N’ number of C4.5 decision trees was applied for classification and prediction. In this way, an efficient student academic performance prediction is carried out in efficient manner. Experimental analysis is carried out with metrics such as accuracy, sensitivity, specificity, space complexity and time complexity. The Online system supports in education using student’s academic performance (SAP) prediction using smartphones in their education system.
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S. Rajendran, S. Chamundeswari, and A. A. Sinha, "Predicting the academic performance of middle-and high-school students using machine learning algorithms," Social Sciences & Humanities Open, vol. 6, p. 100357, 2022.
S. Aggarwal, S. Gupta, S. P. S. Satia, S. Saluja, and V. Gambhir, "Pilot Study to Predict Smartphone Addiction Through Usage Pattern of Installed Android Applications and to Derive Correlations Between Addiction and Phone Usage Behavior," Addicta: The Turkish Journal on Addictions, vol. 9, 2022.
H. Hamsa, S. Indiradevi, and J. J. Kizhakkethottam, "Student academic performance prediction model using decision tree and fuzzy genetic algorithm," Procedia Technology, vol. 25, pp. 326-332, 2016.
Y. Baashar, Y. Hamed, G. Alkawsi, L. F. Capretz, H. Alhussian, A. Alwadain, et al., "Evaluation of postgraduate academic performance using artificial intelligence models," Alexandria Engineering Journal, vol. 61, pp. 9867-9878, 2022.
S. Farhana, "Classification of Academic Performance for University Research Evaluation by Implementing Modified Naive Bayes Algorithm," Procedia Computer Science, vol. 194, pp. 224-228, 2021.
P. Arun and S. Jain, "Use of smart phone among students with intellectual and developmental disability," Journal of Psychosocial Rehabilitation and Mental Health, vol. 9, pp. 447-452, 2022.
T. B. Fariba, "Academic performance of virtual students based on their personality traits, learning styles and psychological well being: A prediction," Procedia-Social and Behavioral Sciences, vol. 84, pp. 112-116, 2013.
S. S. Thakur and R. B. Roy, "Predicting mental health using smart-phone usage and sensor data," Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 9145-9161, 2021.
J. Lee and W. Kim, "Prediction of problematic smartphone use: A machine learning approach," International journal of environmental research and public health, vol. 18, p. 6458, 2021.
S. Poudyal, M. J. Mohammadi-Aragh, and J. E. Ball, "Prediction of student academic performance using a hybrid 2D CNN model," Electronics, vol. 11, p. 1005, 2022.
E. Lau, L. Sun, and Q. Yang, "Modelling, prediction and classification of student academic performance using artificial neural networks," SN Applied Sciences, vol. 1, p. 982, 2019.
B. K. Francis and S. S. Babu, "Predicting academic performance of students using a hybrid data mining approach," Journal of medical systems, vol. 43, p. 162, 2019.
M. Yağcı, "Educational data mining: prediction of students' academic performance using machine learning algorithms," Smart Learning Environments, vol. 9, p. 11, 2022.
S. D. A. Bujang, A. Selamat, R. Ibrahim, O. Krejcar, E. Herrera-Viedma, H. Fujita, et al., "Multiclass prediction model for student grade prediction using machine learning," IEEE Access, vol. 9, pp. 95608-95621, 2021.
L. Zhao, K. Chen, J. Song, X. Zhu, J. Sun, B. Caulfield, et al., "Academic performance prediction based on multisource, multifeature behavioral data," IEEE Access, vol. 9, pp. 5453-5465, 2020.
G. Feng, M. Fan, and Y. Chen, "Analysis and prediction of students’ academic performance based on educational data mining," IEEE Access, vol. 10, pp. 19558-19571, 2022.
T. Nguyen-Huy, R. C. Deo, S. Khan, A. Devi, A. A. Adeyinka, A. A. Apan, et al., "Student performance predictions for advanced engineering mathematics course with new multivariate copula models," IEEE access, vol. 10, pp. 45112-45136, 2022.
A. Alshanqiti and A. Namoun, "Predicting student performance and its influential factors using hybrid regression and multi-label classification," Ieee Access, vol. 8, pp. 203827-203844, 2020.
A. Rafique, M. S. Khan, M. H. Jamal, M. Tasadduq, F. Rustam, E. Lee, et al., "Integrating learning analytics and collaborative learning for improving student’s academic performance," IEEE Access, vol. 9, pp. 167812-167826, 2021.
S.-M. N. Okpara, "Smartphone addiction avoidance via inherent ethical mechanisms and influence on academic performance," Online Journal of Communication and Media Technologies, vol. 13, p. e202318, 2023.
N. Abuzinadah, M. Umer, A. Ishaq, A. Al Hejaili, S. Alsubai, A. A. Eshmawi, et al., "Role of convolutional features and machine learning for predicting student academic performance from MOODLE data," Plos one, vol. 18, p. e0293061, 2023.
T. Tülübaş, T. Karakose, and S. Papadakis, "A holistic investigation of the relationship between digital addiction and academic achievement among students," European Journal of Investigation in Health, Psychology and Education, vol. 13, pp. 2006-2034, 2023.
S. F. A. Hossain, "Smartphone-based teacher-student interaction and teachers’ helping behavior on academic performance," Computers in Human Behavior Reports, vol. 10, p. 100292, 2023.
V. Alinejad, N. Parizad, M. Yarmohammadi, and M. Radfar, "Loneliness and academic performance mediates the relationship between fear of missing out and smartphone addiction among Iranian university students," BMC psychiatry, vol. 22, p. 550, 2022.
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