Development of Students’ Academic and Employability Model through Data Mining

Dr. Tripti Mishra

Articles 34th Edition of DTR Oct 2020 – Mar 2021

Development of Students’ Academic and Employability Model through Data Mining

Authors

DR

Dr. Tripti Mishra

The Research Scholar Dr. Mishra is presently Professor, Delhi Institute of Advanced Studies, Delhi.

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Published 2021-03-30

Pages 54-70

Abstract

The Abstract is of the Thesis “Development of Students' Academic and Employability Model through Data Mining” submitted by Dr-Tripti Mishra for the award of Ph.D. degree from Mewar University, Chittorgarh. The Supervisors were: Dr. C.D. Kumawat, Professor, Department of Computer Science, Mewar University, Chittorgarh and Dr. Sangeeta Gupta, Professor, Management Education and Research Institute, New Delhi. The University awarded the Doctorate to the research scholar in 2019.

References

i. Affendey, L. S., Paris, I. H. M., Mustapha, N., Sulaiman, M. N., & Muda, Z. (2010). Ranking of influencing factors in predicting students’ academic performance. Information Technology Journal, 9(4), 832–837.
ii. Bangsuk, J., Cheng-Fa.T (2013). The Application of Data Mining to Build classification Model for Predicting Graduate Employment. (IJCSIS) International Journal of Computer Science and Information Security, Vol. 11, No. 10.
iii. Bharadwaj, B.K. & Pal, S. (2011). Mining Educational Data to Analyse Students’ Performance. International Journal of Advanced Computer Science and Applications, 2(6), 63–69.
iv. Cheewaprakobkit, P. (2013). Study of Factors Analysis Affecting Academic Achievement of Undergraduate Students in International Program. In Proceedings of the International Multi-Conference of Engineers and Computer Scientists (Vol.1, pp. 13–15).
v. Chien, C. F., & Chen, L. F. (2008). Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications, 34(1), 280–290.
vi. Cortez, P. and Silva, P. (2008). Using data mining to predict secondary school student performance. Proc. 5th Annual Future Business Technology Conference, Porto, pp. 5–12.
vii. Gokuladas, V. K. (2010). Technical and non technical education and the employability of engineering graduates: an Indian case study. International Journal of Training and Development, 14(2), 130–143.
viii. Gokuladas, V. K. (2011). Predictors of employability of engineering graduates in campus recruitment drives of Indian software services companies. International Journal of Selection and Assessment, 19(3), 313–319.
ix. Guruler, H., Istanbullu, A., & Karahasan, M. (2010). A new student performance analysing system using knowledge discovery in higher educational databases. Computers & Education, 55(1), 247–254.
x. Han, J. (2012). Micheline Kamber & Jian Pei. Data Mining: Concepts and Techniques.
xi. Harvey, L. (2001). Defining and measuring employability. Quality in Higher Education, 7(2), 97–109.
xii. Huang, S. (2011). Predictive Modeling and Analysis of Student Academic Performance in an Engineering Dynamics Course.
xiii. Jantawan, B., & Tsai, C. F. (2013). The application of data mining to build classification model for predicting graduate employment. arXiv preprint arXiv:1312.7123.
xiv. Kabakchieva, D. (2013). Predicting student performance by using data mining methods for classification. Cybernetics and Information Technologies, 13(1), 61–72.
xv. Kahya, E. (2007). The effects of job characteristics and working conditions on job performance. International Journal of Industrial Ergonomics, 37(6), 515–523.
xvi. Kardan, S., & Conati, C. (2010, June). A framework for capturing distinguishing user interaction behaviors in novel interfaces. In Educational Data Mining 2011.
xvii. Kovačević, Z. J., & Green, J. S. (2010). Predictive working tool for early identification of at risk students.
xviii. Kovacic, Z. (2012). Early prediction of student success: Mining students’ enrollment data. Proc. Informing Science & IT Education Conference (InSITE), pp. 647–665.
xix. Nelson, D. B., Low, G. R., & Santos, M. D. D. (2004). Professional Manual.
xx. Nghe, N. T., Janecek, P., & Haddawy, P. (2007, October). A comparative analysis of techniques for predicting academic performance. In 37th Annual Frontiers in Education Conference, IEEE.
xxi. Ogor, E. N. (2007, September). Student academic performance monitoring and evaluation using data mining techniques. In CERMA 2007, pp. 354–359. IEEE.
xxii. Oladokun, V. O., Adebanjo, A. T., & Charles-Owaba, O. E. (2008). Predicting students’ academic performance using artificial neural network. The Pacific Journal of Science and Technology, 9(1), 72–79.
xxiii. Osmanbegovic, E., & Suljic, M. (2012). Data mining approach for predicting student performance. Economic Review, 10(1).
xxiv. Othman, Z., Musa, F., Mokhtar, N. H., Ya’acob, A., Latiff, R. A., & Hussin, H. (2010). Investigating University Graduates’ English Language Competency towards Employability. International Journal of Learning, 17(7).
xxv. Paris, I. H. M., Affendey, L. S., & Mustapha, N. (2010). Improving academic performance prediction using voting technique in data mining. World Academy of Science, Engineering and Technology, 62, 820–823.
xxvi. Pool, L., Dacre, P., & Qualter, P. (2013). Emotional Self Efficacy, graduate Employability and Career Satisfaction.
xxvii. Potgieter, I., & Coetzee, M. (2013). Employability attributes and personality preferences of postgraduate business management students. SA Journal of Industrial Psychology, 39(1), 1–10.
xxviii. Ramaswami, M., & Bhaskaran, R. (2009). A study on feature selection techniques in educational data mining. arXiv preprint arXiv:0912.3924.
xxix. Ramaswami, M., & Bhaskaran, R. (2010). A CHAID based performance prediction model in educational data mining. International Journal of Computer Science Issues, 7(1), 10–18.
xxx. Rees, C., Forbes, P., & Kubler, B. (2006). Student employability profiles. The Higher Education Academy, York.
xxxi. Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135–146.
xxxii. Sembiring, S., Zarlis, M., Hartama, D., Ramliana, S., & Wani, E. (2011). Prediction of student academic performance using data mining techniques.
xxxiii. Sen, Baha & Uçar, Emine & Delen, Dursun. (2012). Predicting and analyzing secondary education placement-test scores: A data mining approach.
xxxiv. Sapaat, M., Mustapha, A., Ahmad, J., Chamili, K., & Muhamad, R. (2011). A data mining approach to construct graduates employability model in Malaysia. International Journal of New Computer Architectures and their Applications (IJNCAA), 1(4), 1086–1098.
xxxv. Shafie, L. A., & Nayan, S. (2010). Employability awareness among Malaysian undergraduates. International Journal of Business and Management, 5(8), 119–123.
xxxvi. Shah, N. S. (2012). Predicting factors that affect students’ academic performance using data mining techniques. Pakistan Business Review, 13(4), 631–668.
xxxvii. Susila, M., Jaafar, M., & Nagaraj, S. (2013). Does Reputation Matter? Case Study of Undergraduate Choice at a Premier University. Asia-Pacific Education Researcher, 23, 451–462.
xxxviii. Yusoff, Y. M., Omar, M. Z., Zaharim, A., Mohamed, A., & Muhamad, N. (2012). Employability skills performance score for fresh engineering graduates in Malaysian industry. Asian Social Science, 8(16), 140.

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Dr. Tripti Mishra . (2021). Development of Students’ Academic and Employability Model through Data Mining. DIAS Technology Review, 17(2), 54-70.

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[1] i. Affendey, L. S., Paris, I. H. M., Mustapha, N., Sulaiman, M. N., & Muda, Z. (2010). Ranking of influencing factors in predicting students’ academic performance. Information Technology Journal, 9(4), 832–837.

[2] ii. Bangsuk, J., Cheng-Fa.T (2013). The Application of Data Mining to Build classification Model for Predicting Graduate Employment. (IJCSIS) International Journal of Computer Science and Information Security, Vol. 11, No. 10.

[3] iii. Bharadwaj, B.K. & Pal, S. (2011). Mining Educational Data to Analyse Students’ Performance. International Journal of Advanced Computer Science and Applications, 2(6), 63–69.

[4] iv. Cheewaprakobkit, P. (2013). Study of Factors Analysis Affecting Academic Achievement of Undergraduate Students in International Program. In Proceedings of the International Multi-Conference of Engineers and Computer Scientists (Vol.1, pp. 13–15).

[5] v. Chien, C. F., & Chen, L. F. (2008). Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications, 34(1), 280–290.

[6] vi. Cortez, P. and Silva, P. (2008). Using data mining to predict secondary school student performance. Proc. 5th Annual Future Business Technology Conference, Porto, pp. 5–12.

[7] vii. Gokuladas, V. K. (2010). Technical and non technical education and the employability of engineering graduates: an Indian case study. International Journal of Training and Development, 14(2), 130–143.

[8] viii. Gokuladas, V. K. (2011). Predictors of employability of engineering graduates in campus recruitment drives of Indian software services companies. International Journal of Selection and Assessment, 19(3), 313–319.

[9] ix. Guruler, H., Istanbullu, A., & Karahasan, M. (2010). A new student performance analysing system using knowledge discovery in higher educational databases. Computers & Education, 55(1), 247–254.

[10] x. Han, J. (2012). Micheline Kamber & Jian Pei. Data Mining: Concepts and Techniques.

[11] xi. Harvey, L. (2001). Defining and measuring employability. Quality in Higher Education, 7(2), 97–109.

[12] xii. Huang, S. (2011). Predictive Modeling and Analysis of Student Academic Performance in an Engineering Dynamics Course.

[13] xiii. Jantawan, B., & Tsai, C. F. (2013). The application of data mining to build classification model for predicting graduate employment. arXiv preprint arXiv:1312.7123.

[14] xiv. Kabakchieva, D. (2013). Predicting student performance by using data mining methods for classification. Cybernetics and Information Technologies, 13(1), 61–72.

[15] xv. Kahya, E. (2007). The effects of job characteristics and working conditions on job performance. International Journal of Industrial Ergonomics, 37(6), 515–523.

[16] xvi. Kardan, S., & Conati, C. (2010, June). A framework for capturing distinguishing user interaction behaviors in novel interfaces. In Educational Data Mining 2011.

[17] xvii. Kovačević, Z. J., & Green, J. S. (2010). Predictive working tool for early identification of at risk students.

[18] xviii. Kovacic, Z. (2012). Early prediction of student success: Mining students’ enrollment data. Proc. Informing Science & IT Education Conference (InSITE), pp. 647–665.

[19] xix. Nelson, D. B., Low, G. R., & Santos, M. D. D. (2004). Professional Manual.

[20] xx. Nghe, N. T., Janecek, P., & Haddawy, P. (2007, October). A comparative analysis of techniques for predicting academic performance. In 37th Annual Frontiers in Education Conference, IEEE.

[21] xxi. Ogor, E. N. (2007, September). Student academic performance monitoring and evaluation using data mining techniques. In CERMA 2007, pp. 354–359. IEEE.

[22] xxii. Oladokun, V. O., Adebanjo, A. T., & Charles-Owaba, O. E. (2008). Predicting students’ academic performance using artificial neural network. The Pacific Journal of Science and Technology, 9(1), 72–79.

[23] xxiii. Osmanbegovic, E., & Suljic, M. (2012). Data mining approach for predicting student performance. Economic Review, 10(1).

[24] xxiv. Othman, Z., Musa, F., Mokhtar, N. H., Ya’acob, A., Latiff, R. A., & Hussin, H. (2010). Investigating University Graduates’ English Language Competency towards Employability. International Journal of Learning, 17(7).

[25] xxv. Paris, I. H. M., Affendey, L. S., & Mustapha, N. (2010). Improving academic performance prediction using voting technique in data mining. World Academy of Science, Engineering and Technology, 62, 820–823.

[26] xxvi. Pool, L., Dacre, P., & Qualter, P. (2013). Emotional Self Efficacy, graduate Employability and Career Satisfaction.

[27] xxvii. Potgieter, I., & Coetzee, M. (2013). Employability attributes and personality preferences of postgraduate business management students. SA Journal of Industrial Psychology, 39(1), 1–10.

[28] xxviii. Ramaswami, M., & Bhaskaran, R. (2009). A study on feature selection techniques in educational data mining. arXiv preprint arXiv:0912.3924.

[29] xxix. Ramaswami, M., & Bhaskaran, R. (2010). A CHAID based performance prediction model in educational data mining. International Journal of Computer Science Issues, 7(1), 10–18.

[30] xxx. Rees, C., Forbes, P., & Kubler, B. (2006). Student employability profiles. The Higher Education Academy, York.

[31] xxxi. Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135–146.

[32] xxxii. Sembiring, S., Zarlis, M., Hartama, D., Ramliana, S., & Wani, E. (2011). Prediction of student academic performance using data mining techniques.

[33] xxxiii. Sen, Baha & Uçar, Emine & Delen, Dursun. (2012). Predicting and analyzing secondary education placement-test scores: A data mining approach.

[34] xxxiv. Sapaat, M., Mustapha, A., Ahmad, J., Chamili, K., & Muhamad, R. (2011). A data mining approach to construct graduates employability model in Malaysia. International Journal of New Computer Architectures and their Applications (IJNCAA), 1(4), 1086–1098.

[35] xxxv. Shafie, L. A., & Nayan, S. (2010). Employability awareness among Malaysian undergraduates. International Journal of Business and Management, 5(8), 119–123.

[36] xxxvi. Shah, N. S. (2012). Predicting factors that affect students’ academic performance using data mining techniques. Pakistan Business Review, 13(4), 631–668.

[37] xxxvii. Susila, M., Jaafar, M., & Nagaraj, S. (2013). Does Reputation Matter? Case Study of Undergraduate Choice at a Premier University. Asia-Pacific Education Researcher, 23, 451–462.

[38] xxxviii. Yusoff, Y. M., Omar, M. Z., Zaharim, A., Mohamed, A., & Muhamad, N. (2012). Employability skills performance score for fresh engineering graduates in Malaysian industry. Asian Social Science, 8(16), 140.