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Supporting academic decision making at higher educational institutions using machine learning-based algorithms

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Abstract

Decisions made by deans and university managers greatly impact the entire academic community as well as society as a whole. In this paper, we present survey results on which academic decisions they concern and the variables involved in them. Using machine learning algorithms, we predicted graduation rates in a real case study to support decision making. Real data from five undergraduate engineering programs at District University Francisco Jose de Caldas in Colombia illustrate our results. The comparison between support vector machine and artificial neural network is held using the confusion matrix and the receiver operating characteristic curve. The algorithm methods and architecture are presented.

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References

  • Abdahllah M (2015) A decision support model for long-term course planning. Decis Support Syst 74:33–45

    Article  Google Scholar 

  • Aguiar E, Lakkaraju H, Bhanpuri N, Miller D, Yuhas B, Addison KL (2015) Who, when, and why: a machine learning approach to prioritizing students at risk of not graduating high school on time. In: Proceedings of the Fifth International Conference on Learning Analytics And Knowledge–LAK ’15, pp. 93–102

  • Alptekin E, Ertugrul K (2010) An integrated decision framework for evaluating and selecting e-learning products. Appl Soft Comput 11:2990–2998

    Article  Google Scholar 

  • Arnold KE, Hall Y, Street SG, Lafayette W, Pistilli MD (2012) Course signals at purdue? using learning analytics to increase student success. Learn Anal Knowl 2012:2–5

    Google Scholar 

  • Baker RSJD (2010) Data mining for education, International Encyclopedia of Education. Elsevier, Amsterdam

    Google Scholar 

  • Barlas Y, Dicker V (2000) A dynamic simulation game for Strategic University Management (UNIGAME)

  • Bishop CM (1995) Neural networks for pattern recognition. J Am Stat Assoc 92:482

    MathSciNet  MATH  Google Scholar 

  • Chatti MA, Dyckhoff AL, Schroeder U, Thüs H (2012) A reference model for learning analytics. Int J Technol Enhanc Learn 4(5/6):1–22

    Article  Google Scholar 

  • Czibula G, Gergely I, Gaceanu R (2014) A support vector machine model for intelligent selection fo data representations. Appl Soft Comput 18:70–81

    Article  Google Scholar 

  • Dawson S, Heathcote E (2010) SNAPP?: Realising the affordances of real-time SNA within networked learning environments. In: International Conference on Networked learning, pp. 125–133

  • Delen D, Zaim H, Kusey C (2013) A comparative analysis of machine learning systems for measuring the impact of knowledge management practices. Decis Support Syst 54:1150–1160

    Article  Google Scholar 

  • Deng X, Liu Q, Deng Y, Mahadevan S (2016) An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Inf Sci (Ny) 340–341:250–261

    Article  Google Scholar 

  • Dewberry C (2004) Statistical methods for organizational research. Routledge, New York

    Book  Google Scholar 

  • Dyckhoff AL, Zielke D, Bültmann M, Chatti MA (2012) Design and implementation of a learning analytics toolkit for teachers. Educ Technol Soc 15:58–76

    Google Scholar 

  • Dyckhoff AL, Lukarov V, Muslim A, Chatti MA, Schroeder U (2013) Supporting action research with learning analytics. Learn Anal Knowl 2013:220–229

    Google Scholar 

  • Fischetti M (2016) Fast training of support vector machines with Gaussian kernel. Discret Optim 22:183–194

    Article  MathSciNet  MATH  Google Scholar 

  • Góes ART, Arns Steiner MT, Steiner Neto PJ (2014) Education quality measured by the classification of school performance using quality labels. Appl Mech Mater 670:1675–1683

    Article  Google Scholar 

  • Gonzalez C, Elhariri E, El-Bendary N, Fernandez A (2016) Machine learning based classification approach for predicting students performance in blended learning. Adv. Intell. Syst. Comput. 407:47–56

    Google Scholar 

  • Hackeling G (2014) Mastering Machine Learning with scikit-learn

  • Hagan MT, Demuth HB, Beale MH (2014) Neural network design, 2nd ed

  • Heaton J (2008) Introduction to neural networks with Java, vol 99, 2nd edn. Heaton Research Inc, St. Louis

  • Hoffait A, Schyns M (2017) Early detection of university students with potential difficulties. Decis Support Syst 101:1–11. https://doi.org/10.1016/j.dss.2017.05.003

    Article  Google Scholar 

  • Hsu Chih-Wei, Chang Chih-Chung, L C-J (2016) A practical guide to support vector classification. BJU Int. 101(1):1396–400

  • Karsoliya S (2012) Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. Int J Eng Trends Technol 3(6):714–717

    Google Scholar 

  • Lakkaraju H et al (2015) A machine learning framework to identify students at risk of adverse academic outcomes. Int Conf Knowl Dis Data Min KDD 2015:1909–1918

    Google Scholar 

  • Liu C, Wang W, Wang M, Lv F, Konan M (2017) An efficient instance selection algorithm to reconstruct training set for support vector machine. Knowl-Based Syst 116:58–73

    Article  Google Scholar 

  • Muklason A, Parkes A, Ozcan E (2017) Fairness in examination timetabling: student preferences and extended formulations. Appl Soft Comput 55:302–318

    Article  Google Scholar 

  • Murray WS, Le Blanc LA (1995) A decision support system for academic advising

  • Nieto Y, Montenegro C (2014) System architecture based on learning analytcis to educational decision Makers Toolkit. Adv Comput Sci Eng 13(2):89–105

    Google Scholar 

  • Nieto Y, Montenegro C (2015) Towards a decision support system based on learning analytics. Adv Inf Sci Serv Sci 7(1):01–12

    Google Scholar 

  • Nieto Y, Diaz V, Montenegro C (2016) Academic Decision Making Model for Higher Education Institutions using Learning Analytics. In: Computational and Business Intelligence (ISCBI), 2016 4th International Symposium, pp. 27–32

  • Oztekin A, Delen D, Turkylmaz A (2013) A Machine learning-based usability evaluation method for eLearning systems. Decis Support Syst 56:66–73

    Article  Google Scholar 

  • Ramchoun H, Amine M, Idrissi J, Ghanou Y, Ettaouil M (2016) Multilayer perceptron: architecture optimization and training. Int J Interact Multimed Artif Intell 4(1):26

    Google Scholar 

  • Rodriguez V, Sanchez M, Chica M (2015) Machine learning predictive models for mineral prospectivity? an evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geol Rev 71:804–818

    Article  Google Scholar 

  • Skyrius R, Kazakevičienė G, Bujauskas V (2013) From management information systems to business intelligence: the development of management information needs. Int J Interact Multimed Artif Intell 2(3):31

    Google Scholar 

  • Stimpson AJ, Cummings ML, Member S (2014) Assessing intervention timing in computer-based education using machine learning algorithms. IEEE Access 2:78–87

    Article  Google Scholar 

  • Stoean C, Stoean R (2014) Post-evolution of variable-length class prototypes to unlock decision majing within support vector machines. Appl Soft Comput 25:159–173

    Article  MATH  Google Scholar 

  • Tan M, Shao P (2015) Prediction of student dropout in E-learning program through the use of machine learning method. Int J Emerg Technol Learn 10(1):11–17

    Article  MathSciNet  Google Scholar 

  • Vo TNC, Nguyen HP (2012) A Knowledge-Driven Educational Decision Support System. In: 2012 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future, pp. 1–6

  • Wang X, Huang F, Cheng Y (2016) Computational performance optimization of support vector machine based on support vectors. Neurocomputing 211:66–71

    Article  Google Scholar 

  • Werghi N, Kamoun FK (2010) A decision-tree-based system for student academic advising and planning in information systems programmes. Int J Bus Inf Syst 5:1

    Google Scholar 

  • White CC (1990) A survey on the integration of decision analysis and expert systems for decision support. IEEE Trans Syst Man Cybern 20(2):358–364

    Article  Google Scholar 

  • Witten IH, Frank E, Hall Ma (2011) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kauffman Publishers, San Francisco

    Google Scholar 

  • Xiao-YanLiu (2015) Private colleges teachers evaluation system based on support vector machine (SVM). In: International Conference on Applied Science and Engineerin Innovation ASEI 2015, no. Asei, pp. 1918–1921

  • Zorrilla ME, García D, Álvarez E (2010) A decision support system to improve e-learning environments. In: Proceedings of the EDBT/ICDT Workshops, pp. 1–8

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Correspondence to Rubén González Crespo.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by V. Loia.

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Nieto, Y., García-Díaz, V., Montenegro, C. et al. Supporting academic decision making at higher educational institutions using machine learning-based algorithms. Soft Comput 23, 4145–4153 (2019). https://doi.org/10.1007/s00500-018-3064-6

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