Sustainable development is acquiring high attendance in higher education. In fact, one of the targets for the Sustainable Development Goals announced by the United Nations in September 2015 aims to ensure that all learners acquire the knowledge and skills needed to promote sustainable development, including, among others, thorough education on sustainable development. The current study focuses on the evaluation of individual works based on the sustainable development suggested to students in a subject of the Master's of Thermal Engineering at the University of Vigo. In addition, a sustainable holistic rubric is presented, which was used to analyze the ability of the students to incorporate sustainability principles in their work. The rubric was based on the 17 Sustainable Development Goals and the associated targets of the United Nations, more specifically on the Goals 7, 8, 12, and 13. A total of 10 works were evaluated. As a general conclusion, it was found that the students generally do not consider or consider to a lower extent the economic criteria opposite to the environmental, technical, and social dimensions. The environmental sub-criterion were applied to a greater extent in the development of the works. However, the technical and social dimensions were included to a greater or lesser extent depending on the type of work developed.

The existence of a high university dropout rate in Spain is undeniable. Data shows that approximately one out of five students drop out from college. During the economic expansion period (2001)(2002)(2003)(2004)(2005)(2006)(2007) more students abandoned their studies than during the crisis period (2007)(2008)(2009)(2010)(2011). This situation also affects unemployment rates due to the fact that higher education leads to lower unemployment rates. Galicia presented slightly higher dropout rates than Spain from 2006 to 2009; nevertheless during the 2009/2010 academic year this rate was lower. The reasons for dropout can be divided into six distinct factors: psycho educational, evolutionary, family, economic, institutional and social factors. The main drop-out motives are related, among others, to incompatibility with work, economy, university (environment, teachers and difficulties) and so on. According to the BBVA research, the dropout rate imposes a great cost on the entire economy reaching the sum of 5,772 euros per year and per student and exceeding the amount of 1,500 million euros if the student drops out during the first cycle. Due to the urgent need of reducing the dropout rate, the Ministry of Education developed an action plan to increase the number of young people enrolled in a higher education course; so that, consequently, Spain can redirect its economic growth model.

Previous meta‐analyses of the relationship between phonological awareness (PA) and reading have been conducted mostly in children who speak English, a language with an opaque writing system. In this study, we present a meta‐analysis that examined mean correlations between three PA tasks testing phonemic, syllabic, and intrasyllabic awareness and three reading tasks testing word reading, nonword reading, and reading comprehension in Spanish, a language with a near‐transparent writing system. A random‐effects model of 47 articles (N = 7,956) was used for a multiple correlation study that showed a significantly moderate correlation between some of the PA subcategories and the reading tasks in Spanish‐speaking children. The largest correlation values were found between syllabic awareness and reading, which highlights the importance of syllable recognition during reading acquisition. In addition, we found that intrasyllabic awareness also plays an important role in reading in Spanish. The findings are discussed from a cross‐linguistic perspective.

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Inclusive language focuses on using the vocabulary to avoid exclusion or discrimination, specially referred to gender. The task of finding gender bias in written documents must be performed manually, and it is a time-consuming process. Consequently, studying the usage of non-inclusive language on a document, and the impact of different document properties (such as author gender, date of presentation, etc.) on how many non-inclusive instances are found, is quite difficult or even impossible for big datasets. This research analyzes the gender bias in academic texts by analyzing a study corpus of more than 12,000 million words obtained from more than one hundred thousand doctoral theses from Spanish universities. For this purpose, an automated algorithm was developed to evaluate the different characteristics of the document and look for interactions between age, year of publication, gender or the field of knowledge in which the doctoral thesis is framed. The algorithm identified information patterns using a CNN (convolutional neural network) by the creation of a vector representation of the sentences. The results showed evidence that there was a greater bias as the age of the authors increased, who were more likely to use non-inclusive terms; it was concluded that there is a greater awareness of inclusiveness in women than in men, and also that this awareness grows as the candidate is younger. The results showed evidence that the age of the authors increased discrimination, with men being more likely to use non-inclusive terms (up to an index of 23.12), showing that there is a greater awareness of inclusiveness in women than in men in all age ranges (with an average of 14.99), and also that this awareness grows as the candidate is younger (falling down to 13.07). In terms of field of knowledge, the humanities are the most biased (20.97), discarding the subgroup of Linguistics, which has the least bias at all levels (9.90), and the field of science and engineering, which also have the least influence (13.46). Those results support the assumption that the bias in academic texts (doctoral theses) is due to unconscious issues: otherwise, it would not depend on the field, age, gender, and would occur in any field in the same proportion. The innovation provided by this research lies mainly in the ability to detect, within a textual document in Spanish, whether the use of language can be considered non-inclusive, based on a CNN that has been trained in the context of the doctoral thesis. A significant number of documents have been used, using all accessible doctoral theses from Spanish universities of the last 40 years; this dataset is only manageable by data mining systems, so that the training allows identifying the terms within the context effectively and compiling them in a novel dictionary of non-inclusive terms.