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Year 2021, Volume 16, Issue , 117 - 131, 31.12.2021
https://doi.org/10.55549/epstem.1068566

Abstract

References

  • Al Lluhaybi, M., Tucker, A. & Yousef, L. (2018). The prediction of student failure using classification methods: a case study. Computer Science and Information Technology, 79-90. DOI: 10.5121/csit.2018.80506.
  • Andrews, J., & Higson, H. (2008). Graduate employability,‘soft skills’ versus ‘hard’business knowledge: A European study. Higher education in Europe, 33(4), 411-422.
  • Anuradha, C. & Velmurugan, T. (2016). fast boost decision tree algorithm: a novel classifier for the assessment of student performance in educational data. Printed in Portugal, 31(11), 139-155.
  • Ashraf, A., Anwer, S., & Khan, M. G. (2018). A Comparative study of predicting student’s performance by use of data mining techniques. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), 44(1), 122-136. Atkins, M. J. (1999). Oven‐ready and self‐basting: taking stock of employability skills. Teaching in higher education, 4(2), 267-280.
  • Aziz, A. A., Ismail, N. H., Ahmad, F., & Hassan, H. (2015). A framework for students’s academic performance analysis using naive bayes classifier. Jurnal Teknologi, 75(3), 13-19. DOI: 10.11113/jt.v75.5037.
  • Baradwaj, B. & Pal, S. (2011). Mining Educational Data to Analyze Students’ Performance. International Journal of Advanced Computer Science and Applications, 2(6), 63-69.
  • Bhat, A. (2020, January 24). Exploratory research, definition, methods, types and examples. https://www.questionpro.com/blog/exploratory-research/#Secondary_research_methods.
  • Billing, D. (2003). Generic cognitive abilities in higher education: An international analysis of skills sought by stakeholders. Compare: A Journal of Comparative and International Education, 33(3), 335-350.
  • Brown, BL. (2002). Generic skills in career and technical education: myths and realities. Educational Resources Information Centre.
  • Moya Clemente, I., Ribes Giner, G., & Sanahuja Vélez, G. (2020). Towards sustainability in university education. ımproving university graduates chances of employability by participation in a high achievement academic program. Sustainability, 12(2), 1-17. doi: 10.3390/su12020680.
  • Crebert, G., Bates, M., Bell, B., Patrick, C. J., & Cragnolini, V. (2004). Ivory tower to concrete jungle revisited. Journal of education and work, 17(1), 47-70.
  • Denila, P. G., Delima, A. J. P., & Vilchez, R. N. (2020). Analysis of IT graduates employment alignment using C4. 5 and Naïve Bayes algorithm. Int J Adv Trends ComputSciEng, 9(1), 745-752.
  • Durga, V. S., & Thangakumar, J. (2019). A complete survey on predicting performance of engineering students. Int. J. Civ. Eng. Technol, 10(2), 48-56. Edinyang, S.D., Odey, C., & Joseph, G. (2015). academic factors and graduate employability in Nigeria. Global Journal of Human Resource Management, 3(5), 9-17. Estrera, P.J., Natan, P., Rivera, B.F. & Colarte, Faith, B. (2017). International Journal of Engineering and Techniques, 2(5), 147-154. Funmilayo, O. & Ibukun, A. (2019).
  • Student’s performance prediction using multiple linear regression and decision tree. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 8(7), 256-268.
  • Gault, J., Leach, E., Duey, M. (2010). Effects of business internships on job marketability: the employers’ perspective. Journal of Education and Training, 52, 76-88.
  • Girase, M., Lad, S., & Pachpande, P. (2018). Student’s employability prediction using data mining. International Journal of Scientific & Engineering Research, 9(4), 27-29.
  • 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. https://doi.org/10.1111/j.1468-2389.2011.00560.x,
  • Hassanbeigi, A., Askari, J., Nakhjavani, M., Shirkhoda, S., Barzegar, K., Mozayyan, M. R., & Fallahzadeh, H. (2011). The relationship between study skills and academic performance of university students. Procedia-Social and Behavioral Sciences, 30, 1416-1424.
  • Imose, R., & Barber, L. K. (2015). Using undergraduate grade point average as a selection tool: A synthesis of the literature. The Psychologist-Manager Journal, 18(1), 1-11.
  • Jantawan, B. & Tsai, C. (2013). The application of data mining to build classification model for predicting graduate employment. (IJCSIS) International Journal of Computer Science and Information Security, 11(10), 1-7.
  • Jayaprakash, S., & Jaiganesh, V. (2018). A Survey on Academic Progression of Students in Tertiary Education using Classification Algorithms. International Journal of Engineering Technology Science and Research IJETSR, 5(2), 136-142.
  • Johansen, V. (2014). Entrepreneurship education and academic performance. Scandinavian Journal of Educational Research, 58(3), 300-314.
  • Kavyashree, K. R., & Laksmi, D. (2016). A review on mining students' data for performance prediction. International Journal of Advanced Research in Computer and Communication Engineering, 5(4), 1104-1106.
  • Kumar, M., & Salal, Y. K. (2019). Systematic review of predicting student's performance in academics. Int. J. of Engineering and Advanced Technology, 8(3), 54-61..
  • Kumar, M. & Singh, A.J. (2016). Predicting students’ performance using classification techniques in data mining. International Journal of Technology and Computing (IJTC), 2(10), 489-494.
  • Kuncel, N. R., Ones, D. S., & Sackett, P. R. (2010). Individual differences as predictors of work, educational, and broad life outcomes. Personality And İndividual Differences, 49(4), 331-336.
  • Limbu, J. & Sah, S. (2019). Prediction on Student Academic Performance Using Hybrid Clustering. LEBF Research Journal of Science, Technology and Management, 1(1), 1-22.
  • Mishra, T., Kumar, D., & Gupta, S. (2016). Students’ employability prediction model through data mining. International Journal of Applied Engineering Research, 11(4), 2275-2282.
  • Mwita, K. M. (2018). Tanzania graduate employability: Perception of human resource management practitioners. International Journal of Human Resource Studies, 8(2), 263-272.
  • Oladapo, K.A. & Adedokun, A. (2019). Data mining technique for early detection of at-risk students. 2nd International Conference on Information Technology in Education and Development, Lagos, Nigeria.
  • Osmanbegovic, E., & Suljic, M. (2012). Data mining approach for predicting student performance. Economic Review: Journal of Economics and Business, 10(1), 3-12.
  • Othman, Z., Shan, S. W., Yusoff, I., & Kee, C. P. (2018). Classification techniques for predicting graduate employability. International Journal on Advanced Science, Engineering and Information Technology, 8(4-2), 1712-1720.
  • Pan, Y. J., & Lee, L. S. (2011). Academic performance and perceived employability of graduate students in business and management–an analysis of nationwide graduate destination survey. Procedia-Social and Behavioral Sciences, 25, 91-103.
  • Pıad, K. C. (2018). Determining the dominant attributes of information technology graduates employability prediction using data mining classification techniques. Journal of Theoretical & Applied Information Technology, 96(12). 3780-3790.
  • Piad, K. C., Dumlao, M., Ballera, M. A., & Ambat, S. C. (2016, July). Predicting IT employability using data mining techniques. In 2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC) (pp. 26-30). IEEE.
  • Pitan, O. S. (2016). Towards enhancing university graduate employability in Nigeria. Journal of Sociology and Social Anthropology, 7(1), 1-11. https://doi.org/10/1080/09766634.2016.11885696.
  • Santiago, P., Tremblay, K., Basri, E., & Arnal, E. (2008). Tertiary education for the knowledge society (Vol. 1). OECD. https://doi.org/10.1787/978926406351-hu/.
  • Sapaat, M. A., Mustapha, A., Ahmad, J., Chamili, K., & Muhamad, R. (2011, July). A classification-based graduates employability model for tracer study by MOHE. In International Conference on Digital Information Processing and Communications (pp. 277-287). Springer, Berlin, Heidelberg.
  • Sayana, TS. (2015). Prediction of students academic performance using data mining: analysis. International Journal of Engineering Research & Technology, 3(20), 1-4.
  • Shade, K., Goga, N., Awodele, & Okolie, S. (2013). Optimal algorithm for predicting students’ academic performance. Internal Journal of Computers & Technology, 4(1), 63-75.
  • Shahiri, A. M., & Husain, W. (2015). A review on predicting student's performance using data mining techniques. Procedia Computer Science, 72, 414-422. doi:10.1016/j.procs.2015.12.157, pp. 414-422. Sunday, K., Ocheja, P., Hussain, S., Oyelere, S., Samson, B., & Agbo, F. (2020). Analyzing student performance in programming education using classification techniques. International Journal of Emerging Technologies in Learning (iJET), 15(2), 127-144.
  • Tan, K., Abdul Rahman, NA., Lim, C. (2019). A comparative of predictive model of employability. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(8), 375-378.
  • Tentama, F., & Abdillah, M. H. (2019). Student Employability Examined from Academic Achievement and Self-Concept. International Journal of Evaluation and Research in Education, 8(2), 243-248.
  • Thakar, P., Mehta, A. & Manisha, D. (2015). Role of secondary attributes to boost the prediction accuracy of students’ employability via data mining. International Journal of Advanced Computer Science and Application. 6(11), 84-90.
  • Thakar, P., & Mehta, A. (2017). A unified model of clustering and classification to improve students’ employability prediction. International Journal of Intelligent Systems and Applications, 9(9), 10. DOI: 10.5815/ijisa.2017.09.02.
  • Tholen, G. (2014). Graduate employability and educational context: a comparison between Great Britain and the Netherlands. British Educational Research Journal, 40(1), 1-17. DOI: 10.1002/berj.3023.
  • Vasan, T. M., Sharma, K. J., & Chauhan, N. C. (2018). A comparative study of student’s employability prediction model using data mining technique. International Journal of Creative Research Thoughts (IJCRT), 6(1), 264-270.
  • Weible, R. & McClure, R. (2011). An exploration of the benefits of student internships to marketing departments. Marketing Education Review, 21(3), 229-240.
  • Yadav, S. K. & Pal. S. (2012). Data Mining: A prediction for performance improvement of engineering students using classification. World of Computer Science and Information Technology Journal (WCSIT), 2(2), 51-56.
  • Yusuf, A. & Lawan, A. (2018). Prediction of students’ academic performance using educational data mining technique: Literature review.
  • https://www.academia.edu/34009565/prediction_of_students_academic_performance_using_educational_datamining_technique_literature_review.

Analysis and Prediction of Students’ Academic Performance and Employability Using Data Mining Techniques: A Research Travelogue

Year 2021, Volume 16, Issue , 117 - 131, 31.12.2021
https://doi.org/10.55549/epstem.1068566

Abstract

Higher education institutions (HEIs) handle tons of data to analyze and generate the most relevant information. Data mining is considered a useful tool to extract knowledge to predict future educational trends in this process. Hence, such a method is significant to HEIs to understand and predict students’ employability and other critical academic elements. A comprehensive and systematic literature review was conducted to identify data mining techniques, algorithms, and the various data sets that will lead to the smart prediction and accuracy of student employability. The same method was used to determine the relationship between academic achievement and the employability of students. According to the research findings, the most frequently used data mining techniques for determining students' academic achievement and employability are Classification techniques, specifically the J48(C4.5) algorithm, the Naïve Bayes algorithm, and the CHAID Decision Tree algorithm. The most frequently used data sets or attributes for predicting students' academic performance and employability are their cumulative grade point average (CGPA), gender, technical, communication, problem-solving, analytical, critical thinking, and decision-making skills, extracurricular activities, and age, as well as psychomotor factors such as behavior and attendance and training/internship placement. Academic performance is the primary determinant of employability. The application of data mining techniques in academia has demonstrated its value in enhancing the performance of higher education institutions (HEIs). As a result, more research is urgently needed to ascertain the efficacy of the approaches, algorithms, and data sets identified as predictors of students' employability. Moreover, automated approaches should be utilized to ascertain their accuracy.

References

  • Al Lluhaybi, M., Tucker, A. & Yousef, L. (2018). The prediction of student failure using classification methods: a case study. Computer Science and Information Technology, 79-90. DOI: 10.5121/csit.2018.80506.
  • Andrews, J., & Higson, H. (2008). Graduate employability,‘soft skills’ versus ‘hard’business knowledge: A European study. Higher education in Europe, 33(4), 411-422.
  • Anuradha, C. & Velmurugan, T. (2016). fast boost decision tree algorithm: a novel classifier for the assessment of student performance in educational data. Printed in Portugal, 31(11), 139-155.
  • Ashraf, A., Anwer, S., & Khan, M. G. (2018). A Comparative study of predicting student’s performance by use of data mining techniques. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), 44(1), 122-136. Atkins, M. J. (1999). Oven‐ready and self‐basting: taking stock of employability skills. Teaching in higher education, 4(2), 267-280.
  • Aziz, A. A., Ismail, N. H., Ahmad, F., & Hassan, H. (2015). A framework for students’s academic performance analysis using naive bayes classifier. Jurnal Teknologi, 75(3), 13-19. DOI: 10.11113/jt.v75.5037.
  • Baradwaj, B. & Pal, S. (2011). Mining Educational Data to Analyze Students’ Performance. International Journal of Advanced Computer Science and Applications, 2(6), 63-69.
  • Bhat, A. (2020, January 24). Exploratory research, definition, methods, types and examples. https://www.questionpro.com/blog/exploratory-research/#Secondary_research_methods.
  • Billing, D. (2003). Generic cognitive abilities in higher education: An international analysis of skills sought by stakeholders. Compare: A Journal of Comparative and International Education, 33(3), 335-350.
  • Brown, BL. (2002). Generic skills in career and technical education: myths and realities. Educational Resources Information Centre.
  • Moya Clemente, I., Ribes Giner, G., & Sanahuja Vélez, G. (2020). Towards sustainability in university education. ımproving university graduates chances of employability by participation in a high achievement academic program. Sustainability, 12(2), 1-17. doi: 10.3390/su12020680.
  • Crebert, G., Bates, M., Bell, B., Patrick, C. J., & Cragnolini, V. (2004). Ivory tower to concrete jungle revisited. Journal of education and work, 17(1), 47-70.
  • Denila, P. G., Delima, A. J. P., & Vilchez, R. N. (2020). Analysis of IT graduates employment alignment using C4. 5 and Naïve Bayes algorithm. Int J Adv Trends ComputSciEng, 9(1), 745-752.
  • Durga, V. S., & Thangakumar, J. (2019). A complete survey on predicting performance of engineering students. Int. J. Civ. Eng. Technol, 10(2), 48-56. Edinyang, S.D., Odey, C., & Joseph, G. (2015). academic factors and graduate employability in Nigeria. Global Journal of Human Resource Management, 3(5), 9-17. Estrera, P.J., Natan, P., Rivera, B.F. & Colarte, Faith, B. (2017). International Journal of Engineering and Techniques, 2(5), 147-154. Funmilayo, O. & Ibukun, A. (2019).
  • Student’s performance prediction using multiple linear regression and decision tree. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 8(7), 256-268.
  • Gault, J., Leach, E., Duey, M. (2010). Effects of business internships on job marketability: the employers’ perspective. Journal of Education and Training, 52, 76-88.
  • Girase, M., Lad, S., & Pachpande, P. (2018). Student’s employability prediction using data mining. International Journal of Scientific & Engineering Research, 9(4), 27-29.
  • 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. https://doi.org/10.1111/j.1468-2389.2011.00560.x,
  • Hassanbeigi, A., Askari, J., Nakhjavani, M., Shirkhoda, S., Barzegar, K., Mozayyan, M. R., & Fallahzadeh, H. (2011). The relationship between study skills and academic performance of university students. Procedia-Social and Behavioral Sciences, 30, 1416-1424.
  • Imose, R., & Barber, L. K. (2015). Using undergraduate grade point average as a selection tool: A synthesis of the literature. The Psychologist-Manager Journal, 18(1), 1-11.
  • Jantawan, B. & Tsai, C. (2013). The application of data mining to build classification model for predicting graduate employment. (IJCSIS) International Journal of Computer Science and Information Security, 11(10), 1-7.
  • Jayaprakash, S., & Jaiganesh, V. (2018). A Survey on Academic Progression of Students in Tertiary Education using Classification Algorithms. International Journal of Engineering Technology Science and Research IJETSR, 5(2), 136-142.
  • Johansen, V. (2014). Entrepreneurship education and academic performance. Scandinavian Journal of Educational Research, 58(3), 300-314.
  • Kavyashree, K. R., & Laksmi, D. (2016). A review on mining students' data for performance prediction. International Journal of Advanced Research in Computer and Communication Engineering, 5(4), 1104-1106.
  • Kumar, M., & Salal, Y. K. (2019). Systematic review of predicting student's performance in academics. Int. J. of Engineering and Advanced Technology, 8(3), 54-61..
  • Kumar, M. & Singh, A.J. (2016). Predicting students’ performance using classification techniques in data mining. International Journal of Technology and Computing (IJTC), 2(10), 489-494.
  • Kuncel, N. R., Ones, D. S., & Sackett, P. R. (2010). Individual differences as predictors of work, educational, and broad life outcomes. Personality And İndividual Differences, 49(4), 331-336.
  • Limbu, J. & Sah, S. (2019). Prediction on Student Academic Performance Using Hybrid Clustering. LEBF Research Journal of Science, Technology and Management, 1(1), 1-22.
  • Mishra, T., Kumar, D., & Gupta, S. (2016). Students’ employability prediction model through data mining. International Journal of Applied Engineering Research, 11(4), 2275-2282.
  • Mwita, K. M. (2018). Tanzania graduate employability: Perception of human resource management practitioners. International Journal of Human Resource Studies, 8(2), 263-272.
  • Oladapo, K.A. & Adedokun, A. (2019). Data mining technique for early detection of at-risk students. 2nd International Conference on Information Technology in Education and Development, Lagos, Nigeria.
  • Osmanbegovic, E., & Suljic, M. (2012). Data mining approach for predicting student performance. Economic Review: Journal of Economics and Business, 10(1), 3-12.
  • Othman, Z., Shan, S. W., Yusoff, I., & Kee, C. P. (2018). Classification techniques for predicting graduate employability. International Journal on Advanced Science, Engineering and Information Technology, 8(4-2), 1712-1720.
  • Pan, Y. J., & Lee, L. S. (2011). Academic performance and perceived employability of graduate students in business and management–an analysis of nationwide graduate destination survey. Procedia-Social and Behavioral Sciences, 25, 91-103.
  • Pıad, K. C. (2018). Determining the dominant attributes of information technology graduates employability prediction using data mining classification techniques. Journal of Theoretical & Applied Information Technology, 96(12). 3780-3790.
  • Piad, K. C., Dumlao, M., Ballera, M. A., & Ambat, S. C. (2016, July). Predicting IT employability using data mining techniques. In 2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC) (pp. 26-30). IEEE.
  • Pitan, O. S. (2016). Towards enhancing university graduate employability in Nigeria. Journal of Sociology and Social Anthropology, 7(1), 1-11. https://doi.org/10/1080/09766634.2016.11885696.
  • Santiago, P., Tremblay, K., Basri, E., & Arnal, E. (2008). Tertiary education for the knowledge society (Vol. 1). OECD. https://doi.org/10.1787/978926406351-hu/.
  • Sapaat, M. A., Mustapha, A., Ahmad, J., Chamili, K., & Muhamad, R. (2011, July). A classification-based graduates employability model for tracer study by MOHE. In International Conference on Digital Information Processing and Communications (pp. 277-287). Springer, Berlin, Heidelberg.
  • Sayana, TS. (2015). Prediction of students academic performance using data mining: analysis. International Journal of Engineering Research & Technology, 3(20), 1-4.
  • Shade, K., Goga, N., Awodele, & Okolie, S. (2013). Optimal algorithm for predicting students’ academic performance. Internal Journal of Computers & Technology, 4(1), 63-75.
  • Shahiri, A. M., & Husain, W. (2015). A review on predicting student's performance using data mining techniques. Procedia Computer Science, 72, 414-422. doi:10.1016/j.procs.2015.12.157, pp. 414-422. Sunday, K., Ocheja, P., Hussain, S., Oyelere, S., Samson, B., & Agbo, F. (2020). Analyzing student performance in programming education using classification techniques. International Journal of Emerging Technologies in Learning (iJET), 15(2), 127-144.
  • Tan, K., Abdul Rahman, NA., Lim, C. (2019). A comparative of predictive model of employability. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(8), 375-378.
  • Tentama, F., & Abdillah, M. H. (2019). Student Employability Examined from Academic Achievement and Self-Concept. International Journal of Evaluation and Research in Education, 8(2), 243-248.
  • Thakar, P., Mehta, A. & Manisha, D. (2015). Role of secondary attributes to boost the prediction accuracy of students’ employability via data mining. International Journal of Advanced Computer Science and Application. 6(11), 84-90.
  • Thakar, P., & Mehta, A. (2017). A unified model of clustering and classification to improve students’ employability prediction. International Journal of Intelligent Systems and Applications, 9(9), 10. DOI: 10.5815/ijisa.2017.09.02.
  • Tholen, G. (2014). Graduate employability and educational context: a comparison between Great Britain and the Netherlands. British Educational Research Journal, 40(1), 1-17. DOI: 10.1002/berj.3023.
  • Vasan, T. M., Sharma, K. J., & Chauhan, N. C. (2018). A comparative study of student’s employability prediction model using data mining technique. International Journal of Creative Research Thoughts (IJCRT), 6(1), 264-270.
  • Weible, R. & McClure, R. (2011). An exploration of the benefits of student internships to marketing departments. Marketing Education Review, 21(3), 229-240.
  • Yadav, S. K. & Pal. S. (2012). Data Mining: A prediction for performance improvement of engineering students using classification. World of Computer Science and Information Technology Journal (WCSIT), 2(2), 51-56.
  • Yusuf, A. & Lawan, A. (2018). Prediction of students’ academic performance using educational data mining technique: Literature review.
  • https://www.academia.edu/34009565/prediction_of_students_academic_performance_using_educational_datamining_technique_literature_review.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Maria Elisa Linda Taeza CRUZ This is me
University of Nizwa
Oman


Riah Elcullada ENCARNACION This is me
Oman Tourism College
Oman

Publication Date December 31, 2021
Published in Issue Year 2021, Volume 16, Issue

Cite

APA Cruz, M. E. L. T. & Encarnacıon, R. E. (2021). Analysis and Prediction of Students’ Academic Performance and Employability Using Data Mining Techniques: A Research Travelogue . The Eurasia Proceedings of Science Technology Engineering and Mathematics , 16 , 117-131 . DOI: 10.55549/epstem.1068566