Evaluación económica de intervenciones educativas en la LOMLOE: propuestas de mejora con inteligencia artificial
DOI
10.22550/REP80-1-2022-09
Abstract
* Organic Law 3/2020, of 29 December, which amends Organic Law 2/2006, of 3 May, on Education (LOMLOE).
This research aims to demonstrate the need for an economic evaluation of the Organic Law that modifies the Organic Law of Education (LOMLOE), especially after the investment of EU Next Generation funds that open new opportunities that were lacking in the initial drafting of the law. The challenge for Public Administrations is to use this additional investment efficiently.
Our analysis shows that artificial intelligence models can predict whether educational support programmes will help increase the likelihood that students who lag behind will pass the 4th grade of ESO (Compulsory Secondary Education). In this way, we can calculate the social return of one of these programmes and contribute to their ex-ante design to achieve higher success rates for students.
To complement the models already used by Public Administrations, we use robust Machine Learning (ML) models such as CHAID decision trees and artificial neural networks to analyse the characteristics of the groups of students and the intervention they have been part of. The conclusions allow us to improve educational reinforcement programmes in the coming years to support students with lower chances of academic success.
Please, cite this article as follows: Ballestar, M. T., Sainz, J., & Sanz, I. (2022). Evaluación económica de intervenciones educativas en la LOMLOE: propuestas de mejora con inteligencia artificial |An economic evaluation of educational interventions in the LOMLOE*: Proposals for improvement with artificial intelligence. Revista Española de Pedagogía, 80 (281), 139-160. 10.22550/REP80-1-2022-09
Referencias | References
Aad, G., Abbott, B., Abdallah, J., Abdelalim, A. A., Abdesselam, A., Abdinov, O., Abi, B., Abolins, M., Abramowicz, H., Abreu, H., Acerbi, E., Acharya, B. S., Adams, D. L., Addy, T. N., Adelman, J., Aderholz, M., Adomeit, S., Adragna, P., Adye, T., ... Zwalinski, L. (2012). Performance of missing transverse momentum reconstruction in proton-proton collisions at√s = 7 TeV with atlas. European Physical Journal C, 72 (1), 1-35. 10.1140/epjc/s10052-011-1844-6
Akin, M., Eyduran, E., & Reed, B. M. (2017). Use of RSM and CHAID data mining algorithm for predicting mineral nutrition of hazelnut. Plant Cell, Tissue and Organ Culture (PCTOC), 128 (2), 303-316.
Ballestar, M. T., Doncel, L. M., Sainz, J., & Ortigosa-Blanch, A. (2019). A novel machine learn-ing approach for evaluation of public policies: An application in relation to the performance of university researchers. Technological Fore-casting and Social Change, 149. https://doi.org/10.1016/j.techfore.2019.119756
Ballestar, M. T., García-Lázaro, A., & Sainz, J. (2020). All roads lead to education: A first analysis of robotization, education and employ-ment. Papeles de Economía Española, 166, 33- 49.
Ballestar, M. T., Garcia-Lazaro, A., Sainz, J., & Sanz, I. (2022). Why is your company not robotic? The technology and human capital needed by firms to become robotic. Journal of Business Research, 142, 328-343. https://doi.org/10.1016/j.jbusres.2021.12.061
Ballestar, M. T., Grau-Carles, P., & Sainz, J. (2018). Customer segmentation in e-commerce: Applications to the cashback business model. Journal of Business Research, 88, 407-414. https:// doi.org/10.1016/j.jbusres.2017.11.047
Brunello, G., & Paola, M. De. (2014). The costs of early school leaving in Europe. IZA Journal of Labor Policy, 3 (1), 1-31. https://doi.org/10.1186/2193-9004-3-22In
Burgess, S. (2020). How we should deal with the lockdown learning loss in England’s schools. VOX Cepr Policy Portal. https://bit.ly/3Eom12M
Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial Intelligence trends in education: A narrative overview. Procedia Computer Science, 136, 16-24. https://doi. org/10.1016/j.procs.2018.08.233.
Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: a quantitative analysis using structural equation modelling. Education and Information Technologies, 25 (5), 3443-3463. https:// doi.org/10.1007/s10639-020-10159-7
Crescenzi, R., Giua, M., & Sonzogno, G. V. (2021). Mind the Covid-19 crisis: An evidence-based implementation of Next Generation EU. Journal of Policy Modeling, 43 (2), 278-297. https:// doi.org/10.1016/J.JPOLMOD.2021.03.002
De la Fuente, A., & Jimeno, J. F. (2009). The private and fiscal returns to schooling in the European Union. Journal of the European Economic Association, 7 (6), 1319-1360. https://doi. org/10.1162/JEEA.2009.7.6.1319
Doncel, L. M., Durá, P., Grau, P., & Sainz, J. (2014). Fiscal sustainability and immigration in the Madrid Region. International Migration, 52 (6), 180-196. 10.1111/imig.12004
Dželihodžić, A., & Jonko, D. (2016). Comparison of ensemble classification techniques and single classifiers performance for customer credit assessment. Modeling of Artificial Intelligence, 3, 140-150.
European Comission (2010). EUROPE 2020: A strategy for smart, sustainable and inclusive growth. http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2010:2020:FIN:EN:PDF
Golden, G. (2020). OECD iLibrary | Education policy evaluation: Surveying the OECD landscape. (OECD Education Working Papers, 236). OECD Publishing. https://www.oecd-ilibrary.org/education/education-policy-evaluation_9f127490-en
Goos, M., Manning, A., & Salomons, A. (2009). Job polarization in Europe. American Economic Review, 99 (2), 58-63. 10.1257/ aer.99.2.58
Gouëdard, P., Pont, B., & Viennet, R. (2020). Education responses to COVID-19: Implementing a way forward. OECD Education Working Papers, 224. OECD Publishing. 10.1787/8e95f977-en
Gregory, T., Salomons, A., & Zierahn, U. (2019). Racing With or Against the Machine ? Evidence from Europe. IZA Institute of Labor Economics, 2019 (12063).
Hosmer Jr., D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression. John Wiley & Sons.
Kass, G. V. (1980). An exploratory technique for investigating large quantities of categorical data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 29 (2), 119-127. https:// doi.org/10.2307/2986296
Khosravi, P., Kazemi, E., Zhan, Q., Malmsten, J. E., Toschi, M., Zisimopoulos, P., Sigaras, A., Lavery, S., Cooper, L. A. D., & Hickman, C. (2019). Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization. NPJ Digital Medicine, 2 (1), 1-9.
Kraft, M. (2015, August 20). What If Every Struggling Student Had a Tutor? Education Week. https://www.edweek.org/education/opinion-what-if-every-struggling-student-had-a-tutor/2015/08
Li, Z., & Eastman, J. R. (2006). Commitment and typicality measurements for fuzzy ARTMAP neural network. Geoinformatics 2006: Geospatial Information Science, 6420. https://doi. org/10.1117/12.7129986420, 64201I.
Maimon, O., & Rokach, L. (2005). Data mining and knowledge discovery handbook. Springer.
Nickow, A., Oreopoulos, P., & Quan, V. (2020). The impressive effects of tutoring on prek-12 learning: A systematic review and meta-analysis of the experimental evidence. National Bureau of Economic Research Working Papers Series, 27476. 10.3386/w27476
OECD. (2018). Education policy outlook 2018: Putting student learning at the centre. OECD.
Organic Law 10/2002, of 23 December 2002, on the Quality of Education (LOCE). Spanish Official State Gazette, 307, of 24 December 2002, pp. 45188 to 45220. https://www.boe.es/eli/es/ lo/2002/12/23/10
Organic Law 2/2006, of 3 May, on Education (LOE). Spanish Official State Gazette, 106. https://www.boe.es/eli/es/lo/2006/05/03/2/con
Organic Law 8/2013, of 9 December, for the Improvement of the Quality of Education (LOMCE). Spanish Official State Gazette, 295, pages 97858 to 97921. https://www.boe.es/eli/es/ lo/2013/12/09/8
Organic Law 3/2020, of 29 December, which amends Organic Law 2/2006, of 3 May, on Education (LOMLOE). Spanish Official State Gazette, 340, 30 December 2020, pages 122868 to 122888. https://www.boe.es/diario_boe/txt. php?id=BOE-A-2020-17264
Paliwal, M., & Kumar, U. A. (2009). Neural networks and statistical techniques: A review of applications. Expert Systems with Applications, 36 (1), 2-17.
Pont, B., & Montt, G. (2014). Education policy outlook. Spain. OECD.
Porte, C. de la, & Jensen, M. D. (2021). The next generation EU: An analysis of the dimensions of conflict behind the deal. Social Policy & Administration, 55 (2), 388-402. https://doi. org/10.1111/SPOL.12709
Ramaswami, M., & Bhaskaran, R. (2010). A CHAID based performance prediction model in educational data mining. International Journal of Computer Science, 7 (1), 10-18.
Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity analysis in practice: a guide to assessing scientific models (Vol. 1). Wiley Online Library.
Schleicher, A. (2020). The impact of COVID-19 on education: Insights from education at a glance 2020. OECD Journal: Economic Studies. https://www.oecd.org/education/the-impact-of-covid-19-on-education-insights-education-at-a-glance-2020.pdf
Slavin, R. E. (2016). Evidence-Based Education Policies: Transforming Educational Practice and Research. Educational Researcher, 31 (7), 15-21. https://doi. org/10.3102/0013189X031007015
Wolszczak-Derlacz, J., & Parteka, A. (2011). Efficiency of European public higher education insti-tutions: A two-stage multicountry approach. Scientometrics, 89 (3), 887-917. https://doi. org/10.1007/s11192-011-0484-9
Yin, L., Ge, Y., Xiao, K., Wang, X., & Quan, X. (2013). Feature selection for high-dimensional imbalanced data. Neurocomputing, 105, 3-11.
Yusuf, A. (2007). Standard-Setting at UNESCO: Conventions, recommendations, declarations and charters adopted by UNESCO, 1948-2006 (Vol. 2). Martinus Nijhoff Publishers.
Citación recomendada | Recommended citation
Ballestar, M. T., Sainz, J. & Sanz, I. (2022). Evaluación económica de intervenciones educativas en la LOMLOE: propuestas de mejora con inteligencia artificial | An economic evaluation of educational interventions in the LOMLOE: Proposals for improvement with Artificial Intelligence. Revista Española de Pedagogía, 80 (281), 133-154. 10.22550/REP80-1-2022-09
Licencia Creative Commons | Creative Commons License
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License