Machine Learning Predictive Model of Academic Achievement Efficiency based on Data Envelopment Analysis
Along the way with the changes in the education landscape nowadays, the grade is not the only determinant to predict the students' success. In the context of a student's academic performance, it is better to focus on measuring the efficiency of academic achievements that used multiple determinants of holistic outcome rather than just focus on the student grade. Data Analysis Envelopment (DEA) is a nonparametric method that widely used in many fields to measure performances efficiency but limited research has been reported on DEA in education domain. Acknowledging DEA time consuming issue when involving a huge size of data, recent research on deploying machine learning in DEA keeps on rapid progressing. This paper presents a new research framework of DEA and Auto-ML predictive model for the academic achievement efficiency. The framework includes variety options of machine learning to be compared from the conventional manual setting into the recent Auto-ML technique. The research framework will provide new insights into the decision-making process particularly in the education context.
 K. Gjicali and A. A. Lipnevich, “Got Math Attitude? (In)direct Effects of Student Mathematics Attitudes on Intentions, Behavioral Engagement, and Mathematics Performance in the U.S. PISA,” Contemp. Educ. Psychol., vol. 67, no. September, p. 102019, 2021, doi: 10.1016/j.cedpsych.2021.102019.
 W. A. Alamoudi et al., “Correction to: Why do students skip classroom lectures: A single dental school report (BMC Medical Education, (2021), 21, 1, (388), 10.1186/s12909-021-02824-3),” BMC Med. Educ., vol. 21, no. 1, pp. 1–11, 2021, doi: 10.1186/s12909-021-02866-7.
 S. F. Kong and M. M. Mohd Effendi, “Sikap Pelajar Terhadap Implementasi Sains , Teknologi , Kejuruteraan dan Matematik ( STEM ) dalam Pembelajaran,” J. Dunia Pendidik., vol. 2, no. 3, pp. 72–81, 2020.
 University Teknologi Mara Academic Affair Division, “Education 5 at UiTM - UiTM Academic Compass (3.5.2019).pdf.” p. 95, 2019.
 M. Al Fanah and M. A. Ansari, “Understanding E-learners’ behaviour using data mining techniques,” ACM Int. Conf. Proceeding Ser., pp. 59–65, 2019, doi: 10.1145/3322134.3322145.
 O. Kuzminska, M. Mazorchuk, N. Morze, V. Pavlenko, and A. Prokhorov, “Digital competency of the students and teachers in Ukraine: Measurement, analysis, development prospects,” CEUR Workshop Proc., vol. 2104, pp. 366–379, 2018.
 X. Wang, Z. Wang, Q. Wang, W. Chen, and Z. Pi, “Supporting digitally enhanced learning through measurement in higher education: Development and validation of a university students’ digital competence scale,” J. Comput. Assist. Learn., vol. 37, no. 4, pp. 1063–1076, 2021, doi: 10.1111/jcal.12546.
 G. I. Farantos, “The Data Envelopment Analysis Method and the influence of a phenomenon in organizational Efficiency: A literature review and the Data Envelopment Contrast Analysis new application,” Data Envel. Anal. Decis. Sci., vol. 2015, no. 2, pp. 101–117, 2015, doi: 10.5899/2015/dea-00098.
 H. Scheel, “EMS : Efficiency Measurement System User ’ s Manual,” pp. 1–12, 2000, [Online]. Available: http://www.holger-scheel.de/ems/ems.pdf.
 W. D. Cook and J. Zhu, “Classifying inputs and outputs in data envelopment analysis,” Eur. J. Oper. Res., vol. 180, no. 2, pp. 692–699, 2007, doi: 10.1016/j.ejor.2006.03.048.
 W. Lio and B. Liu, “Uncertain data envelopment analysis with imprecisely observed inputs and outputs,” Fuzzy Optim. Decis. Mak., vol. 17, no. 3, pp. 357–373, 2018, doi: 10.1007/s10700-017-9276-x.
 K. Zhong, Y. Wang, J. Pei, S. Tang, and Z. Han, “Super efficiency SBM-DEA and neural network for performance evaluation,” Inf. Process. Manag., vol. 58, no. 6, p. 102728, 2021, doi: 10.1016/j.ipm.2021.102728.
 H. Waheed, S. U. Hassan, N. R. Aljohani, J. Hardman, S. Alelyani, and R. Nawaz, “Predicting academic performance of students from VLE big data using deep learning models,” Comput. Human Behav., vol. 104, no. October 2019, p. 106189, 2020, doi: 10.1016/j.chb.2019.106189.
 A. Habók, A. Magyar, M. B. Németh, and B. Csapó, “Motivation and self-related beliefs as predictors of academic achievement in reading and mathematics: Structural equation models of longitudinal data,” Int. J. Educ. Res., vol. 103, no. June, p. 101634, 2020, doi: 10.1016/j.ijer.2020.101634.
 S. Nabizadeh, S. Hajian, Z. Sheikhan, and F. Rafiei, “Prediction of academic achievement based on learning strategies and outcome expectations among medical students,” BMC Med. Educ., vol. 19, no. 1, pp. 1–11, 2019, doi: 10.1186/s12909-019-1527-9.
 C. R. Wibrowski, W. K. Matthews, and A. Kitsantas, “The Role of a Skills Learning Support Program on First-Generation College Students’ Self-Regulation, Motivation, and Academic Achievement: A Longitudinal Study,” J. Coll. Student Retent. Res. Theory Pract., vol. 19, no. 3, pp. 317–332, 2017, doi: 10.1177/1521025116629152.
 A. Quilez-Robres, A. González-Andrade, Z. Ortega, and S. Santiago-Ramajo, “Intelligence quotient, short-term memory and study habits as academic achievement predictors of elementary school: A follow-up study,” Stud. Educ. Eval., vol. 70, 2021, doi: 10.1016/j.stueduc.2021.101020.
 T. Gatzka, “Aspects of openness as predictors of academic achievement,” Pers. Individ. Dif., vol. 170, no. September 2020, p. 110422, 2021, doi: 10.1016/j.paid.2020.110422.
 A. Petersen, V. V Ajanovski, and C. Messom, “Predicting Academic Performance : A Systematic Literature Review,” pp. 175–199, 2018.
 J. Johnes, “Operational research in education,” Eur. J. Oper. Res., vol. 243, no. 3, pp. 683–696, 2015, doi: 10.1016/j.ejor.2014.10.043.
 J. F. Chizmar and T. A. Zak, “Canonical estimation of joint educational production functions,” Econ. Educ. Rev., vol. 3, no. 1, pp. 37–43, 1984, doi: 10.1016/0272-7757(84)90006-2.
 J. Doyle and R. Green, “Data envelopment analysis and multiple criteria decision making,” Omega, vol. 21, no. 6, pp. 713–715, 1993, doi: 10.1016/0305-0483(93)90013-B.
 J. Alcaraz, L. Anton-Sanchez, J. Aparicio, J. F. Monge, and N. Ramón, “Russell Graph efficiency measures in Data Envelopment Analysis: The multiplicative approach,” Eur. J. Oper. Res., vol. 292, no. 2, pp. 663–674, 2021, doi: 10.1016/j.ejor.2020.11.001.
 K. Pliakos, S. H. Joo, J. Y. Park, F. Cornillie, C. Vens, and W. Van den Noortgate, “Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems,” Comput. Educ., vol. 137, no. June 2018, pp. 91–103, 2019, doi: 10.1016/j.compedu.2019.04.009.
 Y. Liu and M. Schumann, “Data mining feature selection for credit scoring models,” J. Oper. Res. Soc., vol. 56, no. 9, pp. 1099–1108, 2005, doi: 10.1057/palgrave.jors.2601976.
 A. Nandy and P. K. Singh, “Application of fuzzy DEA and machine learning algorithms in efficiency estimation of paddy producers of rural Eastern India,” Benchmarking, vol. 28, no. 1, pp. 229–248, 2021, doi: 10.1108/BIJ-01-2020-0012.
 V. S. Özsoy and H. H. Örkcü, “Structural and operational management of Turkish airports: a bootstrap data envelopment analysis of efficiency,” Util. Policy, vol. 69, no. December 2019, 2021, doi: 10.1016/j.jup.2021.101180.
 A. Tayal, U. Kose, A. Solanki, A. Nayyar, and J. A. M. Saucedo, “Efficiency analysis for stochastic dynamic facility layout problem using meta-heuristic, data envelopment analysis and machine learning,” Comput. Intell., vol. 36, no. 1, pp. 172–202, 2020, doi: 10.1111/coin.12251.
 Y. Xu, Y. S. Park, and J. D. Park, “Measuring the Response Performance of U.S. States against COVID-19 Using an Integrated DEA, CART, and Logistic Regression Approach,” 2021.
 B. Montoneri, “Teaching Improvement Model Designed with DEA Method and Management Matrix,” IAFOR J. Educ., vol. 2, no. 1, pp. 125–155, 2014, doi: 10.22492/ije.2.1.05.
 S. K. Parahoo, M. I. Santally, Y. Rajabalee, and H. L. Harvey, “Designing a predictive model of student satisfaction in online learning,” J. Mark. High. Educ., vol. 26, no. 1, pp. 1–19, 2016, doi: 10.1080/08841241.2015.1083511.
 A. L. M. Anouze and I. Bou-Hamad, “Data envelopment analysis and data mining to efficiency estimation and evaluation,” Int. J. Islam. Middle East. Financ. Manag., vol. 12, no. 2, pp. 169–190, 2019, doi: 10.1108/IMEFM-11-2017-0302.
 N. A. A. Aziz, R. M. Janor, and R. Mahadi, “Comparative Departmental Efficiency Analysis within a University: A DEA Approach,” Procedia - Soc. Behav. Sci., vol. 90, no. InCULT 2012, pp. 540–548, 2013, doi: 10.1016/j.sbspro.2013.07.124.
 Y. Xiaoming, C. J. Shieh, and W. C. Wu, “Measuring distance learning performance with data envelopment analysis,” Eurasia J. Math. Sci. Technol. Educ., vol. 10, no. 6, pp. 559–564, 2014, doi: 10.12973/eurasia.2014.1217a.
 M. Toloo, B. Ebrahimi, and G. R. Amin, “New data envelopment analysis models for classifying flexible measures: The role of non-Archimedean epsilon,” Eur. J. Oper. Res., vol. 292, no. 3, pp. 1037–1050, 2021, doi: 10.1016/j.ejor.2020.11.029.
 N. R. Nataraja and A. L. Johnson, “Guidelines for using variable selection techniques in data envelopment analysis,” Eur. J. Oper. Res., vol. 215, no. 3, pp. 662–669, 2011, doi: 10.1016/j.ejor.2011.06.045.
 A. Charnes, W. W. Cooper, and E. Rhodes, “Measuring the efficiency of decision making units,” Eur. J. Oper. Res., vol. 2, no. 6, pp. 429–444, 1978, doi: 10.1016/0377-2217(78)90138-8.
 A. Charnes, W. W. Cooper, and E. Rhodes, “Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through,” Manage. Sci., vol. 27, no. 6, pp. 668–697, 1981, doi: 10.1287/mnsc.27.6.668.
 J. E. Beasley, “Comparing university departments,” Omega, vol. 18, no. 2, pp. 171–183, 1990, doi: 10.1016/0305-0483(90)90064-G.
 N. Zhu, C. Zhu, and A. Emrouznejad, “A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies,” J. Manag. Sci. Eng., no. xxxx, pp. 1–14, 2021, doi: 10.1016/j.jmse.2020.10.001.
 M. Gabriela, M. Peixoto, M. Andreotti, and M. C. Ang, “Performance management in hospital organizations from the prespective of Principle Component Analysis and Data Envelopment Analysis:the case of Federal University Hospitals in Brazil,” Comput. Ind. Eng., vol. 150, no. May, 2020, doi: 10.1016/j.cie.2020.106873.
 E. M. Vallespín, “An application of the Data Envelopment Analysis Methodology in the performance assessment of the Zaragoza University Departments,” Doc. Trab. (Universidad Zaragoza. Fac. Econ. y Empres., no. 6, pp. 1–19, 2003, [Online]. Available: https://dialnet.unirioja.es/servlet/articulo?codigo=1370473%0Ahttps://dialnet.unirioja.es/servlet/citart?info=link&codigo=1370473&orden=57286.
 S. Pal, “
 R. Hasan, S. Palaniappan, S. Mahmood, A. Abbas, K. U. Sarker, and M. U. Sattar, “Predicting student performance in higher educational institutions using video learning analytics and data mining techniques,” Appl. Sci., vol. 10, no. 11, 2020, doi: 10.3390/app10113894.
 G. Ramaswami, T. Susnjak, A. Mathrani, J. Lim, and P. Garcia, “Using educational data mining techniques to increase the prediction accuracy of student academic performance,” Inf. Learn. Sci., vol. 120, no. 7–8, pp. 451–467, 2019, doi: 10.1108/ILS-03-2019-0017.
 H. Yazici, S. Seyis, and F. Altun, “Emotional intelligence and self-efficacy beliefs as predictors of academic achievement among high school students,” Procedia - Soc. Behav. Sci., vol. 15, pp. 2319–2323, 2011, doi: 10.1016/j.sbspro.2011.04.100.
 D. Choe, “Parents’ and adolescents’ perceptions of parental support as predictors of adolescents’ academic achievement and self-regulated learning,” Child. Youth Serv. Rev., vol. 116, no. January, p. 105172, 2020, doi: 10.1016/j.childyouth.2020.105172.
 Z. Ahmed, M. Asim, and J. Pellitteri, “Emotional intelligence predicts academic achievement in Pakistani management students,” Int. J. Manag. Educ., vol. 17, no. 2, pp. 286–293, 2019, doi: 10.1016/j.ijme.2019.04.003.
 H. Abuhassna, W. M. Al-Rahmi, N. Yahya, M. A. Z. M. Zakaria, A. B. M. Kosnin, and M. Darwish, “Development of a new model on utilizing online learning platforms to improve students’ academic achievements and satisfaction,” Int. J. Educ. Technol. High. Educ., vol. 17, no. 1, 2020, doi: 10.1186/s41239-020-00216-z.
 K. Thaker, V. Charles, A. Pant, and T. Gherman, “A DEA and random forest regression approach to studying bank efficiency and corporate governance,” J. Oper. Res. Soc., vol. 0, no. 0, pp. 1–28, 2021, doi: 10.1080/01605682.2021.1907239.
 E. D. La Hoz, R. Zuluaga, and A. Mendoza, “Assessing and classification of academic efficiency in engineering teaching programs,” J. Effic. Responsib. Educ. Sci., vol. 14, no. 1, pp. 41–52, 2021, doi: 10.7160/ERIESJ.2021.140104.
 N. Aydin and G. Yurdakul, “Assessing countries’ performances against COVID-19 via WSIDEA and machine learning algorithms,” Appl. Soft Comput. J., vol. 97, p. 106792, 2020, doi: 10.1016/j.asoc.2020.106792.
 B. Singpai and D. Wu, “Using a DEA–Auto-ML approach to track SDG achievements,” Sustain., vol. 12, no. 23, pp. 1–26, 2020, doi: 10.3390/su122310124.
 N. Almaskati, R. Bird, D. Yeung, and Y. Lu, “A horse race of models and estimation methods for predicting bankruptcy,” Adv. Account., vol. 52, p. 100513, 2021, doi: 10.1016/j.adiac.2021.100513.
 P. Appiahene, Y. M. Missah, and U. Najim, “Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks,” Adv. Fuzzy Syst., vol. 2020, 2020, doi: 10.1155/2020/8581202.
 M. Rahmanidoust and J. Zheng, “Evaluation of Factors Affecting Employees' Performance Using Artificial Neural Networks Algorithm: The Case Study of Fajr Jam,” Int. Bus. Res., vol. 12, no. 10, p. 86, 2019, doi: 10.5539/ibr.v12n10p86.
 A. Gupta, M. Kohli, and N. Malhotra, “Classification based on Data Envelopment Analysis and supervised learning: A case study on energy performance of residential buildings,” 1st IEEE Int. Conf. Power Electron. Intell. Control Energy Syst. ICPEICES 2016, pp. 1–5, 2017, doi: 10.1109/ICPEICES.2016.7853706.
 N. Misiunas, A. Oztekin, Y. Chen, and K. Chandra, “DEANN: A healthcare analytic methodology of data envelopment analysis and artificial neural networks for the prediction of organ recipient functional status,” Omega (United Kingdom), vol. 58, pp. 46–54, 2016, doi: 10.1016/j.omega.2015.03.010.
 A. Alinezhad, “An Integrated DEA and Data Mining Approach for Performance Assessment,” Iran. J. Optim. Iran. J. Optim. Iran. J. Optim., vol. 8, no. 82, pp. 59–69, 2016.
 H. Y. Kao, T. K. Chang, and Y. C. Chang, “Classification of hospital web security efficiency using data envelopment analysis and support vector machine,” Math. Probl. Eng., vol. 2013, 2013, doi: 10.1155/2013/542314.
 A. Emrouznejad and E. Shale, “A combined neural network and DEA for measuring efficiency of large scale datasets,” Comput. Ind. Eng., vol. 56, no. 1, pp. 249–254, 2009, doi: 10.1016/j.cie.2008.05.012.