Student dropout is a major concern in studies investigating higher education retention strategies. However, studies investigating the optimal time to identify students who are at risk of withdrawal and the type of data to be used are scarce. Our study consists of a withdrawal prediction analysis based on classification trees using both sociodemographic and academic data from 935 first-year students at an engineering school in Spain. We build prediction models using information collected at three different moments throughout the first semester of the students’ first university year. Our results echo those of previous studies supporting the need for an early first-year intervention to prevent non-completion. In addition, academic performance data serve as a good predictor. Finally, academic monitoring throughout the first semester improves the prediction accuracy, challenging the demand for ‘as soon as possible’ identification of students who are at risk of dropout.

This paper studies the extent to which difficulties in teleworking continue to be encountered by civil servants of the Spanish General State Administration (AGE) who, after the mandatory lockdown decreed by the Spanish Government from March to May 2020, continue to carry out their work under this modality. The results obtained support that the main difficulties are the acquisition of bad posture and problems in separating work and family life, and the presence of different types of difficulties differs depending on gender, the structure of the household, or whether the employee belongs to the levels of positions where higher qualification is required. In addition, ambiguity is expressed regarding the provision of resources and lack of training provided by the AGE.

COVID-19 has inflicted unprecedented damage on the entire concept of work and has prompted a paradigm shift in workplace relations. Many work environments have faced a context in which on-site work has been restricted and telework has been the only viable option. In countries like Spain, the pandemic has forced the Public Administration to opt for this way of working in order to continue operating. Therefore, this study aims to determine the catalysts and blockers for Spanish civil servants wanting to telework. To achieve the study’s objectives, descriptive statistics were obtained using the multiple linear regression technique, with a model building system known as backward stepwise. The identification of these catalysts and blockers will help to facilitate human resource management based on teleworking in the public sector. This study’s findings could help to recommend possible actions to improve the employees’ teleworking experience at the Spanish Administración General del Estado. These actions include investing in equipment, providing training in information and communication technologies, and defining human resources policy to promote the work-life balance.

Gestión de la Calidad y Diseño Específico de los Procesos de Admisión en el Sistema Universitario Español: Estudio de Caso en una Universidad Privada

Student evaluations of teaching (SETs) have become a widely used tool for assessing teaching in higher education. However, numerous investigations have shown that SETs are subject to multiple biases, one of which is particularly relevant, namely, the area of knowledge to which the subject belongs. This article aims to replicate the article by Uttl & Smibert (2017, https://doi.org/10.7717/peerj.3299) in a different educational context to verify whether the negative bias toward instructors who teach quantitative courses found by the authors in the US also appears in the Spanish university system. The study was conducted at the Business and Law School of the Universidad Pontificia Comillas, a private Spanish university, using two different samples. First, we analyzed undergraduate courses using a sample of 80,667 SETs in which 2,885 classes (defined as a single semester-long course taught by an individual instructor to a specific group of students), 488 instructors, and 322 different courses were evaluated over a time period of four academic years (2016/2017–2019/2020). Second, in the same period, 16,083 SETs corresponding to master’s degree courses were analyzed, which involved the study of 871 classes, 275 instructors, and 155 different courses. All the data included in the analysis were obtained from official university surveys developed by a team of professionals specialized in teaching quality responsible for ensuring the reliability of the information. At the degree level, the results show that despite the considerable cultural and temporal difference between the samples, the results are very similar to those obtained by Uttl & Smibert (2017, https://doi.org/10.7717/peerj.3299); i.e., professors teaching quantitative courses are far more likely to obtain worse SETs than instructors in other areas. There are hardly any differences at the master’s degree level, regardless of whether nearly 75% of master’s degree instructors also teach at the undergraduate level. This leads us to three different conclusions. (1) Evidence suggests that the reason for these differences is not due to faculty teaching quantitative courses being less effective than faculty teaching in some other fields. Our results indicate that the same instructor is evaluated very differently depending on whether he or she teaches at the undergraduate or master’s level. (2) It is essential to avoid comparisons of SETs between different areas of knowledge, at least at the undergraduate level. (3) A significant change in the use and interpretation of SETs is imperative, or its replacement by other evaluation mechanisms should be considered. If this does not occur, it is possible that in the future, there will be an adverse selection effect among professors of quantitative methods; i.e., only the worst professionals in quantitative methods will opt for teaching since the good professionals will prefer other jobs.