The coronavirus disease 2019 (COVID-19) pandemic caused a worldwide unexpected interruption of face-to-face teaching and a sudden conversion to emergency remote teaching (ERT). In this exploratory study, a sample of 244 secondary mathematics teachers was considered to analyze their perception of their readiness to ERT during the COVID-19 pandemic based on their technological pedagogical content knowledge (TPCK), their previous training in digital teaching tools, their level of digital competence for teaching mathematics, and their adaptation to ERT. An online questionnaire was applied, and the answers were quantitatively analyzed. Given the use of a large number of digital resources and the high percentage of self-developed materials using educational software, secondary mathematics teachers reflected adequate digital competence and TPCK for teaching mathematics. The sudden transition to ERT forced teachers to slow down the pace of teaching and to reduce the content taught. Significant differences were observed based on gender and age with respect to teachers’ perception of their adaptation to ERT. Despite the positive influence of previous training on their perception of readiness for ERT, in general, teachers recognized that they need more training. The demand for preparation for video editing and online quiz composition can be considered for the design of future training programs.

Initial teacher education programmes provide student teachers with the desired competences to develop themselves as teachers. Although a generic framework for teaching competences is available covering all school subjects in Spain, the initial teacher education programmes curriculum does not specify which competences secondary mathematics student teachers should acquire during their initial teacher education. This explains why this study aimed at developing and validating a competence framework for secondary mathematics student teachers in Spain. Building on existing models and international frameworks, a preliminary list of competences was drawn up. The validation process was based on a Delphi method. In addition experts expressed their opinions about the formulation of the chosen competences. A comprehensive framework of 33 competences for secondary mathematics student teachers could then be validated.

The emergency caused by coronavirus disease 2019 (COVID-19) has revealed significant deficiencies in citizens’ statistical and probabilistic knowledge and in people’s understanding of mathematical and, particularly, stochastic models, which may lead to wrong personal or institutional choices, with critical consequences for the entire population. Mathematics teachers play an essential role in ensuring citizens’ statistical and probabilistic literacy. This study aimed at analyzing the pedagogical content knowledge that teachers utilized to teach statistics and probability through considering contextualized situations. In order to accomplish this purpose, fourteen secondary mathematics teachers participated in a formative and evaluative activity that was designed using the transformational professional competence model. During each evaluative phase, a group discussion was conducted. Participants were asked to reflect on their actions when promoting statistical and probabilistic literacy by considering a range of topics (data science, didactic resources, and methodological approaches) that were addressed during the training phase. A mixed, quantitative–qualitative methodological design was used for the data collection and analysis, which involved open-ended, multiple-choice, or scale-type questions that were moderated by the Metaplan^® approach and the Mentimeter^® software. The main ideas that emerged from the results indicated the need to reinforce the use of real data, technological resources to handle the visualization of information, the elaboration of different types of graphs besides the classical ones, and the formulation of hypotheses. The initial diagnosis will continue within a research and practice community made up of teachers and researchers. Therefore, a working proposal based on examples and models contextualized within the COVID-19 crisis is presented in order to enhance secondary mathematics teachers’ pedagogical content knowledge.

In the realm of K-12 Education, the growing significance of Computational Thinking has sparked extensive inquiry into its nature and instructional methodologies. Despite a wealth of literature on the subject, ongoing debates persist regarding its fundamental components. Across global educational landscapes, Computational Thinking is being incorporated into curricula with varying degrees of emphasis, ranging from robotics to computer coding to broader algorithmic problem-solving approaches. This article aims to reassess established definitions of Computational Thinking and propose a nuanced understanding that highlights the necessity and importance of data in contemporary problem-solving contexts. Additionally, we present a comprehensive framework aimed at cultivating Computational Thinking skills in students, encompassing technical competencies alongside cognitive and metacognitive processes. Subsequently, we offer (1) a refined definition emphasizing the importance of data, (2) a comprehensive framework outlining essential components to foster Computational Thinking skills in students, and (3) a discussion of these concepts within the context of K-12 education.

ResumenEl abandono de los estudios superiores es un fenómeno cuya magnitud se ha visto incrementada a la par que el número de estudiantes en la universidad. Supone un importante desaprovechamiento de recursos que trasciende a la persona y cobra especial relevancia en el actual contexto de crisis económica. Por su parte, la implantación del Espacio Europeo de Educación Superior demanda una mejora de resultados educativos que fomenta la implementación de medidas con dicho objetivo. Sin embargo, la escasez de investigaciones sobre abandono desarrolladas en este contexto supone una dificultad añadida, a cuya superación el presente trabajo pretende contribuir.Se ha desarrollado una investigación bajo un diseño ex post facto para el estudio de la primera cohorte de plena implantación del Espacio Europeo de Educación Superior, ascendiendo la muestra a 1.055 estudiantes.La información fue recogida mediante la extracción de datos sociodemográficos y de expediente académico de la aplicación de gestión académica de la Universidad de Oviedo, y complementada a través de la aplicación de un cuestionario diseñado ad hoc.Los resultados subrayan la importancia del rendimiento académico previo, junto con la dedicación del alumno y el tipo de relación establecida con el profesorado, en la decisión final del alumno respecto a su permanencia.AbstractHigher education drop-out is a phenomenon that has been increasing at the same time as the number of students increases in universities. This leads to a significant waste of resources that goes beyond the student and has special relevance in the context of the current economic crisis.Meanwhile, the implementation of the European Higher Education Area demands an improvement of educational outcomes, which encourages the implementation of measures with that aim. However, the little research had been carried out in this context is an added difficulty. The present work aims to contribute to overcoming this handicap.An ex post facto design was applied to 1,055 freshman students from the academic year 2010-2011. The information was collected by extracting socio-demographic information and academic records from the academic management software of the University of Oviedo, and supplemented by the application of a survey designed for this purpose.The results underline the importance of previous academic performance, student engagement, and type of relationship established with faculty staff, on the final decision of the student on whether to continue studies

University dropout is a growing problem with considerable academic, social and economic consequences. Conclusions and limitations of previous studies highlight the difficulty of analyzing the phenomenon from a broad perspective and with bigger data sets. This paper proposes a new, machine-learning based method, able to examine the problem using a holistic approach. Advantages of this method include the lack of strong distribution hypothesis, the capacity for handling bigger data sets and the interpretability of the results. Results are consistent with previous research, showing the influence of personal and contextual variables and the importance of academic performance in the first year, but other factors are also highlighted with this model, such as the importance of dedication (part or full time), and the vulnerability of the students with respect to their age. Additionally, a comprehensive graphic output is included to make it easier to interpret the discovered rules.