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Published Dec 10, 2019
Monica de Castro-Pardo Concepcion de la Fuente-Cabrero Pilar Laguna-Sanchez Fernando Perez-Rodriguez

Abstract

The Analytical Hierarchy Process is a very common method used in Multi-Criteria Decision Making (MCDM) to analyze participative assessments. However, due to the qualitative nature of this methodology, a high percentage of inconsistencies need to be addressed when analyzing user preferences. This work analyzes the efficiency of the Goal Programming model in order to reduce inconsistencies with pairwise comparisons when working with inexpert participants and time limitations. A case study has been carried out that assesses online courses in higher education with the Analytical Hierarchy Process in order to understand the usefulness and feasibility of the method. Evaluation of four e-learning tools (collaboration tools, content tools, tutorial sessions and evaluation tools) used in an online business degree were collected from 72 students through a ‘Saaty-type’ survey, and the model was applied to improve the consistency of these results. This model has been able to minimize the inconsistencies of individual preferences while avoiding the loss of primary information.

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Keywords

Goal Programming, Inconsistencies, e-learning, Analytical Hierarchy Process, Participative Decision Making

References
Anggrainingsih R, Umam MZ, Setiadi H (2018) Determining e-learning success factor in higher education based on user perspective using Fuzzy AHP. MATEC Web of Conferences, 154. doi: 10.1051/matecconf/201815403011
Belton V, Stewart T (2002) Multiple criteria decision analysis: an integrated approach. Kluwer, Boston: Springer Science and Business Media.
Bozkurt A, Akgun-Ozbek E, Yilmazel S et al. (2015) Trends in distance education research: A content analysis of journals 2009-2013. The International Review of Research in Open and Distributed Learning 16(1): 330-363. doi: http://dx.doi.org/10.19173/irrodl.v16i1.1953
Brunelli M (2017) Studying a set of properties of inconsistency indices for pairwise comparisons. Annals of Operations Research 248(1-2): 143-161. doi: 10.1007/s10479-016-2166-8
Chen K, Kou G, Li C (2018) A linear programming model to reduce rank violations while eliciting preference from pairwise comparison matrix. Journal of the Operational Research Society 69(1-12). doi: 10.1080/01605682.2017.1409156
de Castro M, de la Fuente-Cabrero C, Laguna Sánchez MDP (2017) Assessment of Autonomous Learning Skill Through Multi-criteria Analysis for Online ADE Students in Moodle. In: M. Peris-Ortiz, J. Gómez, J. Merigó-Lindahl, and C. Rueda-Armengot (Eds) Entrepreneurial Universities. Exploring the Academic and Innovative Dimensions of Entrepreneurship in Higher Education (pp. 197-2013). Washington, DC, USA: Springer.
García-Peñalvo FJ, Seoane-Pardo AM (2015) Una revisión actualizada del concepto de eLearning. Décimo Aniversario Education in the Knowledge Society 16(1): 119-144. doi: 10.14201/eks2015161119144
González-Pachón J, Romero C (2004) A method for dealing with inconsistencies in pairwise comparisons. European Journal of Operational Research 158(2): 351-361. doi: 10.1016/j.ejor.2003.06.009
Ho W (2008) Integrated analytic hierarchy process and its applications–A literature review. European Journal of Operational Research 186(1): 211-228. doi: 10.1016/j.ejor.2007.01.004
Jeong HY, Yeo SS (2014) The quality model for e-learning system with multimedia contents: a pairwise comparison approach. Multimedia Tools and Applications 73(2): 887-900. doi: 10.1007/s11042-013-1445-5
Li HL, Ma LC (2007) Detecting and adjusting ordinal and cardinal inconsistences through a graphical and optimal approach in AHP models. Computers and Operations Research 34(3): 780-798. doi: 10.1016/j.cor.2005.05.010
Lin TC, Ho HP, Chang CT (2014) Evaluation model for applying an e-learning system in a course: an analytic hierarchy process-Multi-Choice Goal programming approach. Journal of Educational Computing Research 50(1): 135-157.doi: https://doi.org/10.2190/EC.50.1.g
Martínez-Caro E, Cegarra-Navarro JG, Cepeda-Carrion G (2015) An application of the performance-evaluation model for e-learning quality in higher education. Total Quality Management and Business Excellence 26(5-6): 632-647. doi: 10.1080/14783363.2013.867607
Mohammed HJ, Kasim MM, Shaharanee IN (2018) Evaluation of E-Learning Approaches Using AHP-TOPSIS Technique. Journal of Telecommunication, Electronic and Computer Engineering (JTEC) 10(1-10): 7-10.
Owen D (2015) Collaborative decision making. Decision Analysis 12(1): 29-45. doi: 10.1287/deca.2014.0307
Romero C (1991) Handbook of critical issues in Goal Programming. Oxford,
United Kingdom: Pergamon Press.
Saaty TL (1990) How to make a decision: the analytic hierarchy process. European Journal of Operational Research 48(1): 9-26. doi: 10.1016/0377-2217(90)90057-I
Saaty TL, Vargas L (2001) Models, methods, concepts and applications of the Analytic Hierarchy Process. London, United Kingdom: Kuwer’s Academic Publishers.
Shee DY, Wang YS (2008) Multi-criteria evaluation of the web-based e-learning system: A methodology based on learner satisfaction and its applications. Computers and Education 50 (3): 894-905. doi: 10.1016/j.compedu.2006.09.005
Strother JB (2002) An assessment of the effectiveness of e-learning in corporate training programs. The International Review of Research in Open and Distributed Learning 3(1).
Tamiz M, Jones D, Romero C (1998) Goal programming for decision making: An overview of the current state-of-the-art. European Journal of Operational Research 111(3): 569-581. doi: 10.1016/S0377-2217(97)00317-2
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