Email this article 
The Ahpsort II To Evaluate The High Level Instruction Performances
Abstract
This paper aims to propose a model for ranking Italian high schools based on the several performance outputs. In order to analyze the performance of Italian public High Schools we consider the students’ school performance and their academic achievements; also the school characteristics may influence the performance evaluation of high schools, although the importance of these aspects is certainly less than the results achieved by students.
Data are from Eduscopio and ScuolaInChiaro portals and refers to the 2019/20 school year. We analyze a sample of 263 high schools (HS) in all Italian Regions. For each school we consider nine outputs related to students' school and academic performance, and school characteristics. We assess the performance of high schools using a multi-criteria approach. Our analysis involves a high number of schools, so we apply the AHPSort II method which in addition to defining the ranking of schools also defines their classification. Our results show that scientific lyceums are all in the first class regardless the geographic area.
Keywords
Full Text:
PDFReferences
F. Abastante, S. Corrente, S. Greco, A. Ishizaka, and I. M. Lami. A new parsimonious ahp methodology: assigning priorities to many objects by comparing pairwise few reference objects. Expert Systems with Applications, 127:109– 120, 2019.
T. Agasisti and S. Murtinu. Perceived competition and performance in italian secondary school: new evidence from oecd-pisa 2006. British Educational Research Journal, 38(5):841–858, 2012.
C. Aina, E. Baici, and G. Casalone. Time to degree: Students’ abilities, university characteristics or something else? evidence from italy. Education Economics, 19(3):311–325, 2011.
C. Aina, M. Bratti, and E. Lippo. Ranking high schools using university student performance in italy. Economia Politica, 38:293–321, 2021.
C. A. Bana e Costa and M. D. Oliveira. A multicriteria decision analysis model for faculty evaluation. Omega, 40(4):424–436, 2012.
O. Blasco-Blasco, M. Liern-Garc´ıa, A. L´opez-Garc´ıa, and S. E. Parada-Rico. An academic performance indicator using flexible multi-criteria methods. Mathematics, 9(19):2396, 2021.
S. Burgess, D. Wilson, and J. Worth. A natural experiment in school accountability: the impact of school performance information on pupil progress. Journal of Public Economics, 106(C):57–67, 2013.
E. Eide and M.H. Showalter. The effect of school quality on student performance: A quantile regression approach. Economics Letters, 58:345–350, 1998.
G. Fattoruso, M. Barbati, A. Ishizaka, and M. Squillante. A hybrid ahpsort II and multi-objective portfolio selection method to support quality control in the automotive industry. Journal of the Operational Research Society, pages 1–16, 2022.
F. Giambona and M. Porcu. School size and students’ achievement: Empirical evidences from pisa survey data. Socio-Economic Planning Sciences, 64(C): 66–77, 2018.
C. Giannoulis and A. Ishizaka. A web-based decision support system with electre III for a personalised ranking of british universities. Decision Support Systems, 48(3):488–497, 2010.
K. Goztepe. Applying choquet integral approach for ranking high school innovative education. International Journal of Electrical Communication Engineering, 6(1):27–40, 2020.
C.M.D. Hart and D.N. Figlio. School accountability and school choice: effect on student selection across schools. National Tax Journal, 68(35):875–900, 2015.
A. Ishizaka, C. Pearman, and P. Nemery. Ahpsort: an ahp-based method for sorting problems. International Journal of Production Research, 50(17):4767– 4784, 2012.
A. Ishizaka, M. Tasiou, and L. Mart´ınez. Analytic hierarchy process-fuzzy sorting: An analytic hierarchy process–based method for fuzzy classification in sorting problems. Journal of the Operational Research Society, 71(6):928–947, 2020.
E. Jamelske. Measuring the impact of a university first-year experience program on student gpa and retention. Higher Education, 57(3):373–391, 2009.
A. Kelly and C. Downey. Value-added measures for schools in england: Looking inside the ‘black box’ of complex metrics. Educational Assessment, Evaluation and Accountability, 22(3):181–198, 2010.
C. Lauer. Family background, cohort and education: A french-german comparison based on a multivariate ordered probit model of educational attainment. Labour Economics, 10:231–251, 2003.
P. Mancini and G. Marcarelli. High school choice: how do parents make a choice. International Journal of the Analytic Hierarchy Process, 11(1):91–109, 2019.
P. Mancini and G. Marcarelli. School and academic performance for ranking high schools: some evidence from italy. International Journal of the Analytic Hierarchy Process, 14(2), 2022.
C. Masci, K. D. Witte, and T. Agasisti. The influence of school size, principal characteristics and school management practices on educational performance: An efficiency analysis of italian students attending middle schools. Socio- Economic Planning Sciences, 61:52–69, 2018.
L. Nunes, A. Reis, and C. Seabra. The publication of school rankings: A step toward increased accountability? Economics of Education Review, 49(C):15–23, 2018.
M. Stamenkovic, I. Anic, M. Petrovic, and N. Bojkovic. An electre approach for evaluating secondary education profiles: evidence from pisa survey in serbia. Annals of Operational Research, 245:337–358, 2016.
R. Yendra, K. Anwar, M. N. Muhaijir, A. N. Rahma, A. Fudholi, et al. Analysis of the best high school ranking determination with technique methods or others preference by similarity to ideal solution (topsis). International Journal of Mechanical Engineering and Technology (IJMET), 9(13):650–657, 2018.
DOI: http://dx.doi.org/10.23755/rm.v42i0.809
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 Gerarda Fattoruso, Paola Mancini, Gabriella Marcarelli
This work is licensed under a Creative Commons Attribution 4.0 International License.
Ratio Mathematica - Journal of Mathematics, Statistics, and Applications. ISSN 1592-7415; e-ISSN 2282-8214.