The gender gap in STEM-related job positions is a fact, and it is closely related to the low percentage of women studying STEM degrees. This poses a problem because Europe, as well as the United States and the rest of the developed countries, keep demanding the best engineers and scientists to continue developing innovative products. This problem can thus be approached by answering, firstly, the following question: Why are women not studying STEM degrees? In this paper, we summarize the factors, found in literature, that influence students—both boys and girls—to not study STEM, particularly engineering, computer sciences and technology. We study these influence factors in a sample of N = 338 students from a secondary school placed in the south of Spain; we carry out a survey in order to find out if those students fill out the same answers other researchers have found and published in the related literature. Our main conclusions are as follows: The results confirm that the number of women in technical courses decreases when the level of the course increases; the lack of role models is not an impediment for girls to feel comfortable; unlike boys, girls will not choose engineering, even if their scoring in STEM is good; and we found that girls and women see themselves as not capable of studying an engineering degree more than boys and men do. These results contribute to a deeper understanding of the situation regarding the gender gap in STEM fields in ages in which both girls and boys must choose their future studies.

Previous research has raised concerns that equity may be compromised in content and language integrated learning (CLIL) education, creating schisms in otherwise fairly egalitarian education systems. In Andalusia (southern Spain), where bilingual education has expanded, this article aims to analyze the difference between CLIL bilingual education and traditional monolingual education in terms of student equity indicators. A sample of over 3,800 students representing the four socioeconomic status (SES) levels (SES 1–4), selected by stratified random sampling, was analyzed with correlational statistics to determine their performance levels at CLIL and non-CLIL schools, according to their competence in Spanish L1, English L2, and history. Results point to certain egalitarian effects of CLIL education: while a staircase pattern is constantly present in the performance of non-CLIL students (with those from higher social classes obtaining better results), all CLIL students seem to obtain equally high results regardless of their SES.

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Se presenta en este trabajo los resultados de un estudio realizado sobre las creencias y concepciones de los profesores de educación básica chilenos en ejercicio, sobre las matemáticas, su enseñanza y aprendizaje. Dichas creencias han sido puestas de manifiesto a través de las respuestas a un cuestionario cerrado en el que se presentan diez preguntas, cada una de ellas presenta respuestas a valorar en una escala Likert. La muestra consta de 418 sujetos. Dentro de los resultados destaca la importancia que le otorgan los docentes a enseñar contenidos que sean útiles para la vida real y desarrollar actividades que destaquen por su utilidad y conexión con situaciones reales. A su vez, manifiestan que las dificultades en la enseñanza de las matemáticas no se encuentran en la materia y tampoco en los estudiantes.

In most computer games as in life, the outcome of a match is uncertain due to several reasons: the characters or assets appear in different initial positions or the response of the player, even if programmed, is not deterministic; different matches will yield different scores. That is a problem when optimizing a game-playing engine: its fitness will be noisy, and if we use an evolutionary algorithm it will have to deal with it. This is not straightforward since there is an inherent uncertainty in the true value of the fitness of an individual, or rather whether one chromosome is better than another, thus making it preferable for selection. Several methods based on implicit or explicit average or changes in the selection of individuals for the next generation have been proposed in the past, but they involve a substantial redesign of the algorithm and the software used to solve the problem. In this paper we propose new methods based on incremental computation (memory-based) or fitness average or, additionally, using statistical tests to impose a partial order on the population; this partial order is considered to assign a fitness value to every individual which can be used straightforwardly in any selection function. Tests using several hard combinatorial optimization problems show that, despite an increased computation time with respect to the other methods, both memory-based methods have a higher success rate than implicit averaging methods that do not use memory; however, there is not a clear advantage in success rate or algorithmic terms of one method over the other.