Predicting key educational outcomes in academic trajectories: a machine-learning approach

Predicting and understanding different key outcomes in a student's academic trajectory such as grade point average, academic retention, and degree completion would allow targeted intervention programs in higher education. Most of the predictive models developed for those key outcomes have been based on traditional methodological approaches. However, these models assume linear relationships between variables and do not always yield accurate predictive classifications. On the other hand, the use of machine-learning approaches such as artificial neural networks has been very effective in the classification of various educational outcomes, overcoming the limitations of traditional methodological approaches. In this study, multilayer perceptron artificial neural network models, with a backpropagation algorithm, were developed to classify levels of grade point average, academic retention, and degree completion outcomes in a sample of 655 students from a private university. Findings showed a high level of accuracy for all the classifications. Among the predictors, learning strategies had the greatest contribution for the prediction of grade point average. Coping strategies were the best predictors for degree completion, and background information had the largest predictive weight for the identification of students who will drop out or not from the university programs.Fil: Musso, Mariel Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental "Dr. Horacio J. A. Rimoldi". Grupo Vinculado CIIPME - Entre Ríos - Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental "Dr. Horacio J. A. Rimoldi"; ArgentinaFil: Rodríguez Hernández, Carlos Felipe. Katholikie Universiteit Leuven; BélgicaFil: Cascallar, Eduardo C.. Katholikie Universiteit Leuven; Bélgic

Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation

The applications of artificial intelligence in education have increased in recent years. However, further conceptual and methodological understanding is needed to advance the systematic implementation of these approaches. The first objective of this study is to test a systematic procedure for implementing artificial neural networks to predict academic performance in higher education. The second objective is to analyze the importance of several well-known predictors of academic performance in higher education. The sample included 162,030 students of both genders from private and public universities in Colombia. The findings suggest that it is possible to systematically implement artificial neural networks to classify students’ academic performance as either high (accuracy of 82%) or low (accuracy of 71%). Artificial neural networks outperform other machine-learning algorithms in evaluation metrics such as the recall and the F1 score. Furthermore, it is found that prior academic achievement, socioeconomic conditions, and high school characteristics are important predictors of students’ academic performance in higher education. Finally, this study discusses recommendations for implementing artificial neural networks and several considerations for the analysis of academic performance in higher education.Fil: Rodríguez Hernández, Carlos Felipe. Katholikie Universiteit Leuven; BélgicaFil: Musso, Mariel Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental Dr. Horacio J. A. Rimoldi; ArgentinaFil: Kyndt, Eva. Swinburne University Of Technology; Australia. Universiteit Antwerp; BélgicaFil: Cascallar, Eduardo. Katholikie Universiteit Leuven; Bélgic

Comparative study between delivery modalities in higher education during emergency remote teaching due to COVID-19

Despite the difficulties faced during Emergency Remote Teaching (ERT) because of the COVID-19 pandemic, it is also true that such a situation has left a series of learnings that educational institutions around the world should capitalize on. Under this scenario, interest arose in studying three delivery modalities (face-to-face, hybrid, and remote) at the university level, aiming to compare the students’ learning level and their perceptions of each delivery modality. The present study was developed in a private university in Mexico, following a quantitative methodological approach involving 360 students and 14 professors from various schools and geographical locations. Data were collected through pre-and post-tests and a perception questionnaire for students. Findings suggest that the students’ learning level in every modality varies by school and that students positively perceive the three delivery modalities, albeit identifying factors that foster and hinder their learning process in each one. The results of this study contribute to strengthening the research field on teaching during ERT, allowing educational institutions to make better decisions regarding the quality of the educational offer

Myo1f has an essential role in γδT intraepithelial lymphocyte adhesion and migration

γδT intraepithelial lymphocyte represents up to 60% of the small intestine intraepithelial compartment. They are highly migrating cells and constantly interact with the epithelial cell layer and lamina propria cells. This migratory phenotype is related to the homeostasis of the small intestine, the control of bacterial and parasitic infections, and the epithelial shedding induced by LPS. Here, we demonstrate that Myo1f participates in the adhesion and migration of intraepithelial lymphocytes. Using long-tailed class I myosins KO mice, we identified the requirement of Myo1f for their migration to the small intestine intraepithelial compartment. The absence of Myo1f affects intraepithelial lymphocytes’ homing due to reduced CCR9 and α4β7 surface expression. In vitro, we confirm that adhesion to integrin ligands and CCL25-dependent and independent migration of intraepithelial lymphocytes are Myo1f-dependent. Mechanistically, Myo1f deficiency prevents correct chemokine receptor and integrin polarization, leading to reduced tyrosine phosphorylation which could impact in signal transduction. Overall, we demonstrate that Myo1f has an essential role in the adhesion and migration in γδT intraepithelial lymphocytes

Vacas gordas y vacas flacas: la política fiscal y el balance estructural en México, 1990-2009

This study estimates the structural budget balance for the Mexican economy adjusting for both the business cycle and the international oil price for the period 1990-2009. Consistent with earlier studies, our results suggest that fiscal policy has been characterized as procyclical during most of the period. However, we find that it has been countercyclical since late 2008, although to a lesser degree than what indicates the traditional balance. Moreover, we simulate counterfactual scenarios where it is analyzed what would have happened if a fiscal rule were applied on both the structural and the traditional budget balances over the period under study.public balance, structural balance, business cycles

Stevensite-based geofilter for the retention of tetracycline from water

The antibiotic tetracycline, is considered a contaminant of emerging concern due to its presence in wastewater effluents, surface waters and groundwaters. Adsorption of tetracycline on soils and clays has been extensively studied to remove the contaminant from the water. A decreasing adsorption as the pH increases is normally reported in the pH range 3–9. However, adsorption isotherms performed on a commercial stevensite presented increasing adsorption with the increasing pH, in the pH range 2–8. This is very interesting since the pH in natural and wasterwaters are normally in the range 6–8. A laboratory design of a geofilter using a mixture of sand and stevensite was tested against an inflow solution of tetracycline 1 g/L, NaNO3 0.1 M and pH = 7 in an advective transport cell experiment. The number of tetracycline molecules exceed by >3 times the number exchangeable positions in the stevensite geofilter. Under these conditions, the TC adsorption on the geofilter reaches 590 mg/g, surpassing the retention capacity of most adsorbents found in literature. Besides, the tetracycline is completely desorbed by the inflow of a saline solution (Mg(NO3)2 0.5 M, at pH = 2) with capacity to replace the exchangeable positions, thus, recovering the geofilter and the tetracyclineThis work has been financially supported by the Spanish Ministry of Economy and Competitiveness through the AGL2016-78490-R projec

Ingeniería Geológica en Terrenos Volcánicos. Métodos, Técnicas y Experiencias en las Islas Canarias

La presente obra es un compendio de conceptos, metodologías y técnicas útiles para acometer proyectos y obras en terrenos volcánicos desde el punto de vista de la ingeniería geológica y la geotecnia. El libro se presenta en tres partes diferenciadas. La primera es conceptual y metodológica, con capítulos que tratan sobre la clasificación de las rocas volcánicas con fines geotécnicos, la caracterización geomecánica, los problemas geotécnicos y constructivos asociados a los distintos materiales, y una guía metodológica para la redacción de informes geotécnicos para la edificación. La segunda parte aborda las aplicaciones a obras de ingeniería, incluyendo deslizamientos, obras subterráneas,infraestructuras marítimas y obras públicas. La tercera parte recoge capítulos dedicados a describir distintos casos prácticos de obras y proyectos en los que la problemática geotécnica en terrenos volcánicos ha tenido un papel relevante. Los capítulos han sido elaborados por técnicos y científicos de reconocido prestigio en el campo de la ingeniería geológica en terrenos volcánicos, que han plasmado en ellos sus conocimientos y experiencias en la materia.Los editores y autores de parte de los capítulos del libro, los Doctores Luis E. Hernández Gutiérrez (Geólogo) y Juan Carlos Santamarta Cerezal (Ingeniero de Montes, Civil y Minas), son los responsables del grupo de investigación INGENIA (Ingeniería Geológica, Innovación y Aguas). Su actividad investigadora comprende más de 200 publicaciones en el área de la ingeniería geológica, la geotecnia, medio ambiente y el aprovechamiento del agua en islas y terrenos volcánicos. En relación a la docencia han impartido y dirigido más de 90 seminarios y cursos de especialización a nivel nacional e internacional, incluyendo la organización de 4 congresos internacionales. Fueron premiados por la Universidad de La Laguna en los años 2012, 2013 y 2014 por su calidad docente e innovación universitaria, y son pioneros en los laboratorios virtuales para la enseñanza de la ingeniería. Participan activamente como profesores colaboradores e investigadores en varias universidades e instituciones españolas e internacionales. Todas sus publicaciones están disponibles en internet, con libre acceso. Ingeniería geológica en terrenos volcánicos, es una obra de gran interés para, consultores, técnicos de administraciones públicas, proyectistas y demás profesionales implicados en obras y proyectos de infraestructuras en terrenos volcánicos; también es útil para académicos y estudiantes de ingeniería o ciencias geológicas que quieran investigar o iniciarse en las singularidades que presentan los materiales volcánicos en la edificación o en la ingeniería civil y minera

Meckel's diverticulum: analysis of 27 cases in an adult population

BackgroundMeckel's diverticulum is a rare congenital pathology among newborns. Nevertheless, it is an uncommon abdominal pathology in the adult population. Therefore, we aim to provide a detailed account of our surgical approach in treating 27 cases of Meckel's diverticulum.MethodsThis study is a cross-sectional analysis that utilized a database with prospectively collected data from 2004 to 2022. All patients under the age of 18 were excluded from the population. We described the population’s demographic characteristics, symptoms, anatomopathological study, surgical technique, complications, morbidity, and mortality. A subgroup analysis was performed between the symptomatic and asymptomatic patients.ResultsA total of 27 patients who underwent surgical resection for a posteriorly diagnosed Meckel's diverticulum were included. The male population accounted for 81.4% (n = 22) of the sample size. The symptomatic group consisted of 18 male and four female patients. Abdominal pain was the predominant symptom in 85% of the patients. Out of the 22 symptomatic patients, only 9% had a positive perioperative diagnosis of Meckel's diverticulum. All 27 patients with diverticulum diagnosis received the resection through diverticulectomy (n = 6), small bowel resection with end-to-end anastomosis (n = 6), and small bowel resection with lateral to lateral anastomosis (n = 15). The mean distance between the diverticulum and the ileocecal valve was 63.4 cm. The symptomatic group had an average diverticulum length of 3.54 cm, with an average base width of 2.47 cm. In the other group, the values were 2.75 and 1.61 cm. The average length of hospital stay in the symptomatic group was 7.3 days.ConclusionsMeckel's diverticulum is a rare pathology in the adult population. Its presentation varies from asymptomatic to symptomatic patients, and surgery is the cornerstone treatment for this pathology