Almost 217 million secondary school students (60% of the world’s adolescents) do not reach minimum levels in reading proficiency at the end of secondary school, according to objective 4.1 of the UN’s Sustainable Development Goals. Therefore, the early and efficient identification of this disadvantage and implementation of remedial strategies is critical for economies. In 2018, the Programme for International Student Assessment (PISA) assessed the reading skills of 15-year-old students in 80 countries and economies. This work introduces a methodology that uses PISA’s data to build logistic regression models to identify the main factors contributing to students’ underperforming reading skills. Results showed that socioeconomic status (SES), metacognition strategies, Information and Communication Technology (ICT) skills, and student–teacher relationships are the most important contributors to low reading abilities.

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He received a B.S. degree in Industrial Engineering from the Mexico National Autonomous University, a M.S. degree in Quality Engineering from Queretaro University in Mexico, and is a Candidate for the Doctor of Engineering degree at the same University. His research interests in engineering education in expertise acquisition and student's persistence. In the application of engineering he is also working in the use of statistical models for problem solving in industry and for quantitative research of social aspects in the education of engineers. Prior to joining the University of Queretaro, Mr. Huerta spent several years working in manufacturing, leading medium size manufacturing plants of automotive components and industrial goods. Coordinated the startup of two new plants in central Mexico and implemented lean manufacturing and advanced quality systems. Mr. Huerta is member of the American Society for Engineering Education, the American Society for Quality and the American Statistical Association. He is a Certified Six Sigma Black Belt and serves as consultant for medium and small technology based industrial firms.

This article describes the data related to co-enrollment density (CD), a new network clustering index, that can predict persistence and graduation. The data hold the raw results and charts obtained with the algorithm for CD introduced in ``Co-Enrollment Density Predicts Engineering Students' Persistence and Graduation: College Networks and Logistic Regression Analysis.'' There are data for eight institutions that show CD as a predictor for graduation at four years, graduation at six years, and ever graduated. The files were processed using R to estimate CD at one, two, three, and four years. Logistic regression models, receiver operating characteristic curves, specificity, sensitivity, and cut-off points were estimated for each model. The R code to reproduce the metanalysis for the summary data is included. The displays for the logistic regression models, receiver operating characteristic curves, density curves for classes, models, and parameters are included.

El frijol común, Phaseolus vulgaris L. es un alimento importante por su aporte nutricional y nutraceútico. La aplicación de fertilizantes sintéticos es común para nutrir los suelos, y como consecuencia de sus efectos adversos, se han estudiado fuentes alternativas de fertilización. El pimiento morrón (Capsicum annuum) de desecho (PMD) posee nutrientes de interés, por lo que se puede utilizar para producir biofertilizantes. En esta investigación se produjo un biofertilizante a través de la fermentación del PMD con el hongo Aspergillus niger y se evaluó su aplicación (0 %, 50 %, 100 % y 200 %) sobre el desarrollo de plantas de Flor de junio León y Flor de Mayo Eugenia, los días 10 y 25. El diámetro de tallo es semejante entre los tratamientos y cultivares. El alto de la planta presenta resultados significativos en el cultivar flor Eugenia a 25 días, el largo de la hoja en el tratamiento de 100 %, y el ancho de hoja destaca en el cultivar flor de mayo Eugenia. El biofertilizante elaborado demuestra tener efectos significativos en los parámetros evaluados en el desarrollo de la planta de frijol.