Does grade retention affect achievement? Some evidence from PISA

Grade retention practices are at the forefront of the educational debate. In this paper, we use PISA 2009 data for Spain to measure the effect of grade retention on students achievement. One important problem when analyzing this question is that school outcomes and the propensity to repeat a grade are likely to be determined simultaneously. We address this problem by estimating a Switching Regression Model. We …find that grade retention has a negative impact on educational outcomes, but we confi…rm the importance of endogenous selection, which makes observed differences between repeaters and non-repeaters appear 14.6% lower than they actually are. The effect on PISA scores of repeating is much smaller (-10% of non-repeaters average) than the counterfactual reduction that non-repeaters would suffer had they been retained as repeaters (-24% of their average). Furthermore, those who repeated a grade during primary education suffered more than those who repeated a grade of secondary school, although the effect of repeating at both times is, as expected, much larger.Grade retention, educational scores, PISA

Does grade retention affect achievement? Some evidence from Pisa

Grade retention practices are at the forefront of the educational debate. In this paper, we use PISA 2009 data for Spain to measure the effect of grade retention on students’achievement. One important problem when analyzing this question is that school outcomes and the propensity to repeat a grade are likely to be determined simultaneously. We address this problem by estimating a Switching Regression Model. We find that grade retention has a negative impact on educational outcomes, but we confirm the importance of endogenous selection, which makes observed differences between repeaters and non-repeaters appear 14.6% lower than they actually are. The effect on PISA scores of repeating is much smaller (-10% of non-repeaters’average) than the counterfactual reduction that non-repeaters would suffer had they been retained as repeaters (-24% of their average). Furthermore, those who repeated a grade during primary education suffered more than those who repeated a grade of secondary school, although the effect of repeating at both times is, as expected, much larger.Grade retention, educational scores, PISA

Does grade retention affect achievement? Some evidence from Pisa

Grade retention practices are at the forefront of the educational debate. In this paper, we use PISA 2009 data for Spain to measure the effect of grade retention on students’achievement. One important problem when analyzing this question is that school outcomes and the propensity to repeat a grade are likely to be determined simultaneously. We address this problem by estimating a Switching Regression Model. We find that grade retention has a negative impact on educational outcomes, but we confirm the importance of endogenous selection, which makes observed differences between repeaters and non-repeaters appear 14.6% lower than they actually are. The effect on PISA scores of repeating is much smaller (-10% of non-repeaters’average) than the counterfactual reduction that non-repeaters would suffer had they been retained as repeaters (-24% of their average). Furthermore, those who repeated a grade during primary education suffered more than those who repeated a grade of secondary school, although the effect of repeating at both times is, as expected, much larger

Collaborative Multiobjective Evolutionary Algorithms in search of better Pareto Fronts. An application to trading systems

Technical indicators use graphic representations of data sets by applying various mathematical formulas to financial time series of prices. These formulas comprise a set of rules and parameters whose values are not necessarily known and depend on many factors: the market in which it operates, the size of the time window, and others. This paper focuses on the real-time optimization of the parameters applied for analyzing time series of data. In particular, we optimize the parameters of technical and financial indicators and propose other applications, such as glucose time series. We propose the combination of several Multi-objective Evolutionary Algorithms (MOEAs). Unlike other approaches, this paper applies a set of different MOEAs, collaborating to construct a global Pareto Set of solutions. Solutions for financial problems seek high returns with minimal risk. The optimization process is continuous and occurs at the same frequency as the investment time interval. This technique permits the application of non-dominated solutions obtained with different MOEAs simultaneously. Experimental results show that this technique increases the returns of the commonly used Buy \& Hold strategy and other multi-objective strategies, even for daily operations

Learning Difference Equations with Structured Grammatical Evolution for Postprandial Glycaemia Prediction

People with diabetes must carefully monitor their blood glucose levels, especially after eating. Blood glucose regulation requires a proper combination of food intake and insulin boluses. Glucose prediction is vital to avoid dangerous post-meal complications in treating individuals with diabetes. Although traditional methods, such as artificial neural networks, have shown high accuracy rates, sometimes they are not suitable for developing personalised treatments by physicians due to their lack of interpretability. In this study, we propose a novel glucose prediction method emphasising interpretability: Interpretable Sparse Identification by Grammatical Evolution. Combined with a previous clustering stage, our approach provides finite difference equations to predict postprandial glucose levels up to two hours after meals. We divide the dataset into four-hour segments and perform clustering based on blood glucose values for the twohour window before the meal. Prediction models are trained for each cluster for the two-hour windows after meals, allowing predictions in 15-minute steps, yielding up to eight predictions at different time horizons. Prediction safety was evaluated based on Parkes Error Grid regions. Our technique produces safe predictions through explainable expressions, avoiding zones D (0.2% average) and E (0%) and reducing predictions on zone C (6.2%). In addition, our proposal has slightly better accuracy than other techniques, including sparse identification of non-linear dynamics and artificial neural networks. The results demonstrate that our proposal provides interpretable solutions without sacrificing prediction accuracy, offering a promising approach to glucose prediction in diabetes management that balances accuracy, interpretability, and computational efficiency

Does grade retention affect achievement? Some evidence from PISA

Grade retention practices are at the forefront of the educational debate. In this paper, we use PISA 2009 data for Spain to measure the effect of grade retention on students' achievement. One important problem when analyzing this question is that school outcomes and the propensity to repeat a grade are likely to be determined simultaneously. We address this problem by estimating a Switching Regression Model. We find that grade retention has a negative impact on educational outcomes, but we confirm the importance of endogenous selection, which makes observed differences between repeaters and non-repeaters appear 14.6% lower than they actually are. The effect on PISA scores of repeating is much smaller (-10% of non-repeaters' average) than the counterfactual reduction that non-repeaters would suffer had they been retained as repeaters (-24% of their average). Furthermore, those who repeated a grade during primary education suffered more than those who repeated a grade of secondary school, although the effect of repeating at both times is, as expected, much larger

Patterns Detection in Glucose Time Series by Domain Transformations and Deep Learning

People with diabetes have to manage their blood glucose level to keep it within an appropriate range. Predicting whether future glucose values will be outside the healthy threshold is of vital importance in order to take corrective actions to avoid potential health damage. In this paper we describe our research with the aim of predicting the future behavior of blood glucose levels, so that hypoglycemic events may be anticipated. The approach of this work is the application of transformation functions on glucose time series, and their use in convolutional neural networks. We have tested our proposed method using real data from 4 different diabetes patients with promising results.Comment: 7 pages, 7 figures, 3 table

Probabilistic Fitting of Glucose Models with Real-Coded Genetic Algorithms

[EN] Type 1 Diabetes patients have to control their blood glucose levels using insulin therapy. Numerous factors (such as carbohydrate intake, physical activity, time of day, etc.) greatly complicate this task. In this article we propose a modeling method that will allow us to make predictions of blood glucose level evolution with a time horizon of 24 hours. This may allow the adjustment of insulin doses in advance and could help to improve the living conditions of diabetes patients. Our approach starts from a system of finite difference equations that characterizes the interaction between insulin and glucose (in the field, this is known as a minimal model). This model has several parameters whose values vary widely depending on patient characteristics and time. Thus, in the first phase of our strategy, We will enrich the patient¿s historical data by adding white Gaussian noise, which will allow us to perform a probabilistic fitting with a 95% confidence interval. Then, the model¿s parameters are adjusted based on the history of each patient using a genetic algorithm and dividing the day into 12 time intervals. In the final stage, we will perform a whole-day forecast from an ensemble of the models fitted in the previous phase. Th e validity of our strategy will be tested using the Parkers¿ error grid analysis. Our experimental results based on data from real diabetic patients show that this technique is capable of robust predictions that take into account all the uncertainty associated with the interaction between insulin and glucose.We acknowledge support from Spanish Ministry of Economy and Competitiveness under project RTI2018-095180- B-I00 and Madrid Regional Goverment - FEDER grants B2017/BMD3773 (GenObIA-CM) and Y2018/NMT-4668 (Micro-Stress- MAP-CM). Devices for adquiring data from patients were adquired with the support of Fundacion Eugenio Rodriguez Pascual 2019 grant - Desarrollo de sistemas adaptativos y bioinspirados para el control glucemico con infusores subcutaneos continuos de insulina y monitores continuos de glucosa (Development of adaptive and bioinspired systems for glycaemic control with continuous subcutaneous insulin infusors and continuous glucose monitors).Cervigón, C.; Velasco, JM.; Burgos-Simon, C.; Villanueva Micó, RJ.; Hidalgo, JI. (2021). Probabilistic Fitting of Glucose Models with Real-Coded Genetic Algorithms. IEEE. 736-743. https://doi.org/10.1109/CEC45853.2021.9504836S73674

Particle swarm grammatical evolution for energy demand estimation

[EN] Grammatical Swarm is a search and optimization algorithm that belongs to the more general Grammatical Evolution family, which works with a set of solutions called individuals or particles. It uses the Particle Swarm Optimization algorithm as the search engine in the evolution of solutions. In this paper, we present a Grammatical Swarm algorithm for total energy demand estimation in a country from macroeconomic variables. Each particle in the Grammatical Swarm encodes a different model for energy demand estimation, which will be decoded by a predefined grammar. The parameters of the model are also optimized by the proposed algorithm, in such a way that the model is adjusted to a training set of real energy demand data, selecting the more appropriate variables to appear in the model. We analyze the performance of the Grammatical Swarm evolution in two real problems of one-year ahead energy demand estimation in Spain and France. The proposal is compared with previous approaches with competitive results.Spanish Ministerial Commission of Science and Technology (MICYT), Grant/Award Number: TIN2017-85887-C2-2-P; Ministerio de Ciencia, Innovacion y Universidades, Grant/Award Number: PGC2018-095322-B-C22 and RTI2018-095180-B-I00; Comunidad de Madrid y Fondos Estructurales de la Union Europea, Grant/Award Number: S2018/TCS-4566 and Y2018/NMT-4668; GenObIA-CM, Grant/Award Number: S2017/BMD-3773; Ministerio de Economia, Industria y Competitividad, Grant/Award Number: MTM2017-89664-PMartínez-Rodríguez, D.; Colmenar, JM.; Hidalgo, JI.; Villanueva Micó, RJ.; Salcedo-Sanz, S. (2020). Particle swarm grammatical evolution for energy demand estimation. Energy Science & Engineering. 8(4):1068-1079. https://doi.org/10.1002/ese3.568S1068107984Safarzyńska, K., & van den Bergh, J. C. J. M. (2017). Integrated crisis-energy policy: Macro-evolutionary modelling of technology, finance and energy interactions. Technological Forecasting and Social Change, 114, 119-137. doi:10.1016/j.techfore.2016.07.033Li, F., Song, Z., & Liu, W. (2014). China’s energy consumption under the global economic crisis: Decomposition and sectoral analysis. Energy Policy, 64, 193-202. doi:10.1016/j.enpol.2013.09.014Salcedo-Sanz, S., Muñoz-Bulnes, J., Portilla-Figueras, J. A., & Del Ser, J. (2015). One-year-ahead energy demand estimation from macroeconomic variables using computational intelligence algorithms. Energy Conversion and Management, 99, 62-71. doi:10.1016/j.enconman.2015.03.109Sánchez-Oro, J., Duarte, A., & Salcedo-Sanz, S. (2016). Robust total energy demand estimation with a hybrid Variable Neighborhood Search – Extreme Learning Machine algorithm. Energy Conversion and Management, 123, 445-452. doi:10.1016/j.enconman.2016.06.050Ceylan, H., & Ozturk, H. K. (2004). Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach. Energy Conversion and Management, 45(15-16), 2525-2537. doi:10.1016/j.enconman.2003.11.010Shaik, S., & Yeboah, O.-A. (2018). Does climate influence energy demand? A regional analysis. Applied Energy, 212, 691-703. doi:10.1016/j.apenergy.2017.11.109United Nations Climate Change Conference.The Paris Agreement. UNTC XXVII 7.d.Suganthi, L., & Samuel, A. A. (2012). Energy models for demand forecasting—A review. Renewable and Sustainable Energy Reviews, 16(2), 1223-1240. doi:10.1016/j.rser.2011.08.014Geem, Z. W., & Roper, W. E. (2009). Energy demand estimation of South Korea using artificial neural network. Energy Policy, 37(10), 4049-4054. doi:10.1016/j.enpol.2009.04.049Ünler, A. (2008). Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025. Energy Policy, 36(6), 1937-1944. doi:10.1016/j.enpol.2008.02.018Kıran, M. S., Özceylan, E., Gündüz, M., & Paksoy, T. (2012). Swarm intelligence approaches to estimate electricity energy demand in Turkey. Knowledge-Based Systems, 36, 93-103. doi:10.1016/j.knosys.2012.06.009Kıran, M. S., Özceylan, E., Gündüz, M., & Paksoy, T. (2012). A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey. Energy Conversion and Management, 53(1), 75-83. doi:10.1016/j.enconman.2011.08.004Yu, S., Wei, Y.-M., & Wang, K. (2012). A PSO–GA optimal model to estimate primary energy demand of China. Energy Policy, 42, 329-340. doi:10.1016/j.enpol.2011.11.090Yu, S., Zhu, K., & Zhang, X. (2012). Energy demand projection of China using a path-coefficient analysis and PSO–GA approach. Energy Conversion and Management, 53(1), 142-153. doi:10.1016/j.enconman.2011.08.015Yu, S., & Zhu, K. (2012). A hybrid procedure for energy demand forecasting in China. Energy, 37(1), 396-404. doi:10.1016/j.energy.2011.11.015Geng, Z., Zeng, R., Han, Y., Zhong, Y., & Fu, H. (2019). Energy efficiency evaluation and energy saving based on DEA integrated affinity propagation clustering: Case study of complex petrochemical industries. Energy, 179, 863-875. doi:10.1016/j.energy.2019.05.042Han, Y., Long, C., Geng, Z., Zhu, Q., & Zhong, Y. (2019). A novel DEACM integrating affinity propagation for performance evaluation and energy optimization modeling: Application to complex petrochemical industries. Energy Conversion and Management, 183, 349-359. doi:10.1016/j.enconman.2018.12.120Han, Y., Wu, H., Jia, M., Geng, Z., & Zhong, Y. (2019). Production capacity analysis and energy optimization of complex petrochemical industries using novel extreme learning machine integrating affinity propagation. Energy Conversion and Management, 180, 240-249. doi:10.1016/j.enconman.2018.11.001Colmenar, J. M., Hidalgo, J. I., & Salcedo-Sanz, S. (2018). Automatic generation of models for energy demand estimation using Grammatical Evolution. Energy, 164, 183-193. doi:10.1016/j.energy.2018.08.199O’Neill, M., & Brabazon, A. (2006). Grammatical Swarm: The generation of programs by social programming. Natural Computing, 5(4), 443-462. doi:10.1007/s11047-006-9007-7O’Neill, M., & Ryan, C. (2001). Grammatical evolution. IEEE Transactions on Evolutionary Computation, 5(4), 349-358. doi:10.1109/4235.942529KennedyJ EberhartR.Particle swarm optimization. Vol.4. Proceedings of ICNN'95 - International Conference on Neural Networks.Perth WA;1995:1942‐1948.https://doi.org/10.1109/ICNN.1995.488968Tsoulos, I. G., Gavrilis, D., & Glavas, E. (s. f.). Neural network construction using grammatical evolution. Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, 2005. doi:10.1109/isspit.2005.1577206Beni, G., & Wang, J. (1993). Swarm Intelligence in Cellular Robotic Systems. Robots and Biological Systems: Towards a New Bionics?, 703-712. doi:10.1007/978-3-642-58069-7_38Krause, J., Ruxton, G. D., & Krause, S. (2010). Swarm intelligence in animals and humans. Trends in Ecology & Evolution, 25(1), 28-34. doi:10.1016/j.tree.2009.06.016Marini, F., & Walczak, B. (2015). Particle swarm optimization (PSO). A tutorial. Chemometrics and Intelligent Laboratory Systems, 149, 153-165. doi:10.1016/j.chemolab.2015.08.020Ling, C. X. (1995). Overfitting and generalization in learning discrete patterns. Neurocomputing, 8(3), 341-347. doi:10.1016/0925-2312(95)00050-

La malacofauna marina de las fases holocenas en la Cueva del Toll (Moià, Barcelona): nuevas aportaciones para el Neolítico nororiental

[EN]: The entrance to the South Gallery of the Toll Cave was discovered in the 40's of the last century. Since then, several works have highlighted the importance of the archaeo-pale-ontological site at both regional and peninsular levels. Focusing on the Holocene levels, the research suggests prolonged use of it, at least from Epicardial times to the late Bronze Age. Among the vast amount of material recovered, we highlight, to the interest of our study, several marine shells. Under the current research project, new specimens have been recovered. Tax-onomic identification shows that they are marine gastropods. The technology, use-wear and chemical analysis suggests that they were anthropically modified by making holes for use as hanging elements. The shells were being tinted with red pigment. The spatial and stratigraphic position of the specimens lead, us to interpret them as part of a single bead, deposited at the bottom of a Neolithic structure.[ES]: La entrada a la Galería Sur de la Cueva del Toll (Moià, Barcelona) fue descubierta en los años 40 del pasado siglo. Desde entonces, las distintas intervenciones realizadas han puesto de manifiesto la importancia del yacimiento arqueo-paleontológico tanto a nivel regional como peninsular. Centrándonos en los niveles holocenos de la cavidad, las investigaciones realizadas indican un uso prolongado de la misma, al menos desde el Neolítico Antiguo, hasta finales del Bronce inicial. Entre la variada cantidad de materiales recuperados destacan por el interés del presente estudio, varios restos de malacofauna marina. En el marco del actual proyecto de investigación, han sido recuperados nuevos ejemplares pertenecientes a las especies Columbella rustica y Nassarius cuvieri. El análisis tecnológico, traceológico y químico de las mismas sugiere que fueron modificadas antrópicamente mediante la realización de perforaciones para su uso, siendo tintadas con pigmentos rojizos. La posición estratigráfica y espacial de los ejemplares nos lleva a interpretar las mismas como parte de un único abalorio, depositado en el fondo de una estructura neolítica.El proyecto de intervención arqueológica esta cofinanciado por el Departamento de Cultura y Medios de Comunicación de la Generalitat de Cataluña.Peer Reviewe