Gender Diversity in STEM Disciplines: A Multiple Factor Problem, Guías, Proyectos, Investigaciones de Matemáticas

¡Descarga Gender Diversity in STEM Disciplines: A Multiple Factor Problem y más Guías, Proyectos, Investigaciones en PDF de Matemáticas solo en Docsity! entropy Article Gender Diversity in STEM Disciplines: A Multiple Factor Problem Carmen Botella 1 , Silvia Rueda 1,* , Emilia López-Iñesta 2 and Paula Marzal 1 1 School of Engineering (ETSE-UV), Universitat de València, Av. De l’Universitat s/n, 46100 Valencia, Spain; carmen.botella@uv.es (C.B.); paula.marzal@uv.es (P.M.) 2 Department of Didactics of Mathematics (Faculty of Teaching), Universitat de València, Av. Tarongers, 4, 46022 Valencia, Spain; emilia.lopez@uv.es * Correspondence: silvia.rueda@uv.es; Tel.: +34-963-544-123 Received: 28 September 2018; Accepted: 3 January 2019; Published: 4 January 2019



 Abstract: Lack of diversity, and specifically, gender diversity, is one of the key problems that both technological companies and academia are facing these days. Moreover, recent studies show that the number of female students enrolled in science, technology, engineering and mathematics (STEM) related disciplines have been decreasing in the last twenty years, while the number of women resigning from technological job positions remains unacceptably high. As members of a higher education institution, we foresee that working towards increasing and retaining the number of female students enrolled in STEM disciplines can help to alleviate part of the challenges faced by women in STEM fields. In this paper, we first review the main barriers and challenges that women encounter in their professional STEM careers through different age stages. Next, we focus on the special case of the information theory field, discussing the potential of gendered innovation, and whether it can be applied in the Information Theory case. The working program developed by the School of Engineering at the University of Valencia (ETSE-UV), Spain, which aims at decreasing the gender diversity gap, is then presented and recommendations for practice are given. This program started in 2011 and it encompasses Bachelor, Master and PhD levels. Four main actions are implemented: Providing institutional encouragement and support, increasing the professional support network, promoting and supporting the leadership, and increasing the visibility of female role models. To assess the impact of these actions, a chi-square test of independence is included to evaluate whether there is a significant effect on the percentage of enrolled female students. The percentage of graduated female students in the information and Communications Technology Field is also positioned with respect to other universities and the Spanish reference value. This analysis establishes that, in part, this program has helped to achieve higher female graduation rates, especially among Bachelor students, as well as increasing the number of top-decision positions held by faculty women. Keywords: women in STEM; gendered innovation; gender diversity 1. Introduction The information and communications technology (ICT) sector has been growing at a quick pace for the last twenty years. This technological sector is highly dynamic and shows tremendous potential for innovation and for introducing changes impacting deeply on the society in the short-term. Traditionally, the ICT sector demands large numbers of graduates in science, technology, engineering, and mathematics (STEM) disciplines, and its employees are usually well paid. In this challenging environment, one may expect the sector to take advantage of as much brain power, creativity, and knowledge as possible. However, it fails to do so, since several studies indicate that it is a male-dominated sector in all stages of the professional development [1,2], with women facing a Entropy 2019, 21, 30; doi:10.3390/e21010030 www.mdpi.com/journal/entropy Entropy 2019, 21, 30 2 of 17 greater probability of working part-time, taking a break from their careers, or even resigning [2]. Women currently hold 21% of the high executive positions in the technology sector [1], while they represent only 13% of the highest positions if the engineering field is considered [3]. Of all ICT patents, 88% have been registered by all-male teams, and the gender pay gap is a reality, with women being paid from 18% to 22% less than men [4]. This situation is also reflected in academia and scientific research and development institutions, where women are under-represented in the top decision-making positions. In the case of the 28 countries comprising the European Union, for example, only 28% of scientific and administrative board members are women, and in 2014, women represented a mere 20% of the heads of higher education institutions [3]. Regarding the working conditions, 13% of women in research were part-time (2012) versus 8.5% of men. The gender pay gap is also visible here, with women’s earnings being 17.9% lower than those of men (2010) [3]. Women represent 33% of the researchers in the European Union, with a larger gender imbalance (less than 40% proportion) in the fields of engineering and technology and natural sciences [3]. Considering these numbers, it can be seen why increasing diversity in working teams in all professional stages has been a key issue for technological companies, as well as for academia. More precisely, gender diversity has been proved to increase the potential for innovation. In fact, defining teams with an equivalent composition in terms of women and men translates into increasing the creativity, the chances to experiment, the share of knowledge, and task fulfillment, with respect to building teams with other compositions [4]. Research on team composition states that the collective intelligence of the team is not a direct translation from the intelligence level of individual team members. Actually, a larger number of women in the teams can be related to higher levels of collective intelligence [4]. In parallel, research shows that an increased participation of women, not only in STEM fields, positively impacts the economy as a whole, as stated for example in Reference [5], which addresses the correlation of women empowerment and economic growth, or Reference [6], focusing on women’s roles and parliament presence for decreasing corruption. Reality shows that the number of women enrolling in STEM related disciplines, and therefore, the chance to increase their numbers also in the job market and academia, is decreasing despite all efforts. For example, Reference [1] indicates that in 1991, 37% of computer science graduates were women, while this number went down to 26% in 2014 and to 18% in 2018. In the European Union [3], 25% of PhD graduates in engineering were women (2012), with 21% graduating in computing. In the case of Spain, for example, women represent 22% of the graduates in computing (2012), and 29% in engineering (2012) [3]. The Organization for Economic Co-operation and Development (OECD) states that less than a 20% of women register in STEM PhD programs, and only 18% start engineering studies, being 20% the average for computer science [7]. Similar numbers are given by the United Nations Educational, Scientific and Cultural Organization (UNESCO) in their report [8]. This document states that women stand for 35% of all students enrolled in STEM related disciplines in higher education. The lowest percentage (world average) is found in the areas of ICT (3%), engineering, manufacturing, and construction (8%), and natural science, mathematics and statistics (5%). What is more, women abandon in enormous numbers their higher education STEM studies, as well as during the transition to the job market and during their professional STEM career. Interestingly, Reference [7] defines the point where the career path starts to diverge before 15 years old: On average, at that age, boys are more than twice as likely as girls to expect to work as scientists or engineers. Actually, less than 0.5% of girls would like to be working in the ICT sector, while this percentage increases to 5% of boys. With this perspective, some studies even suggest that the STEM gender gap starts from inborn differences between sexes regarding STEM aptitudes. However, this stereotype has been thoroughly revised in Reference [9], where the authors have analyzed possible gender differences in mathematical cognition from 500 children aged from six months to eight years. This analysis found that boys and girls do not show any difference in terms of quantitative or mathematical ability, thus they have the same aptitudes in terms of mathematical Entropy 2019, 21, 30 5 of 17 In the following section, we will review the particular case of the Information Theory field, discussing the potential application of the gendered innovation initiative. In Section 3, we will introduce the working program towards decreasing the gender diversity gap carried out by the School of Engineering at the University of Valencia (ETSE-UV), Spain, targeting Bachelor, Master, and PhD levels of ICT-related disciplines. Section 4 presents then an exploratory analysis of the impact of these actions, evaluating a set of objective metrics with the aim of showcasing the efficacy of the program and supporting our recommendations for practice. Finally, conclusions are presented in Section 5. 2. The Information Theory Field: Impact of Gendered Innovations The Information Theory field has been traditionally a male-predominant environment. In our opinion, one of the main reasons is the lack of visibility of women already working in the topic, discouraging other women to enter the field. This trend decreases the percentage of women, which also reduces their support network and can cause workplace dissatisfaction to arise in the end. A side factor contributing to this scenario may be the lack of practical applications of Information Theory from young girls’ perspective. Although results related to Information Theory have been fundamental for technology progress, their impact is not well-understood out of the field. Report [8] and Reference [23] reflect that interest, motivation, and altruistic values and attitudes are fundamental for increasing the number of students in STEM. An analysis carried out over 40 years focusing on occupational interests [24], showed that, independently of the time period and age window, men prefer working with things, and women prefer working with people. References [25–27] explore the goal congruity perspective, which frames the influence of social roles in motivational processes. Recently, in Reference [27], the STEM gender gap is addressed under this perspective, analyzing the impact of the perception of STEM fields as not achieving communal opportunities to work or help others. As the authors point out, understanding and specifically, transmitting the opportunities for achieving a communal goal offered by STEM disciplines can be seminal for decreasing the gender gap. Aligned with these pieces of evidence, we believe that highlighting the applicability is key to engage students in certain disciplines, as it is the case of Information Theory, where the final link to people needs to be emphasized outside the field. More precisely, two actions can boost the enrollment of women in the Information Theory field: Increasing the visibility of research in areas such as machine learning and data science and including the gender/sex analysis and gender diversity in the research dimension, which is known as gendered innovations [28]. In the actual massive data era, we are facing problems that require accessing loads of data, processing them to apply knowledge extraction techniques, and designing algorithms to analyze the data. On the other hand, it is important to note that the performance of machine learning algorithms highly depends on the relevant information contained in the data. Indeed, entropy is the core concept in Information Theory that provides a measure of the information content or the information gain widely used in Machine Learning and Data Science [29]. Machine Learning algorithms are being used in different applications to make important decisions that influence our daily lives: from health insurances contracts and their coverages, research spending prioritization, college admissions, to the way people find jobs or get loan applications approved. However, the models being used today sometimes do not take into account context and they have gender bias reinforcing discrimination conditions [30]. Given this situation, institutions such as the European Commission are working on gendered innovation, which aims at including the gender/sex analysis and gender diversity among research and innovation and is one of the Responsible Research and Innovation (RRI) indicators in the field of Science and Technology considered by the European Union. In fact, ensuring the incorporation of the gender perspective supposes a crosscutting issue for the European Commission in its Horizon 2020 funding program in every stage of the research process, where not only diversity in research teams needs to be fulfilled but also diversity in research methods and in research questions [31]. Entropy 2019, 21, 30 6 of 17 Taking this into consideration, in the last years some projects have emerged such as the European COST action “GenderSTE” (The international European Cooperation in Science and Technology (COST) network GenderSTE (Gender, Science, Technology and Environment) http://www.cost.eu/about_ cost/strategy/targeted_networks/genderste) [32] that supports networking and the dissemination of knowledge with a gender perspective, the “GEECCO” (Gender Equality in Engineering through Communication and Commitment https://www.genderste.eu/) H2020 project focused on Engineering, or the initiative “gendered innovations” [28] led by Professor Londa Schiebinger and funded by the US National Science Foundation and the European Commission. Schiebinger states that gendered innovation perspective can benefit excellence in research, policy, and practice in many fields, from Health and Medicine or Social Sciences to Engineering and Technology [18]. In particular, Machine Learning and Data Science areas suppose new opportunities to include gendered innovation in Information Theory because they can be applied to many different domains, and there is an increase in demand for new data scientists. Data Science jobs have an important consulting component, beginning with a business context understanding phase to define goals and create a project plan. As it has been reviewed in the introduction, the participation of women in work teams as consultants, designers, or producers of technology is key for achieving high levels of creativity and collective intelligence, helping to avoid biases in research related to a lack of diversity and gender perspective. Some effects of not taking into account gender and sex are described in References [18,28,33–40], in the context of Machine Translation and Natural Language Processing algorithms, software development, smart cities, or robotics. 3. Decreasing the Gender Diversity Gap: Working Program In this Section we present the ETSE-UV working program, which is organized around four main actions, illustrated in Figure 1: (1) to provide institutional encouragement and support, (2) to increase the professional support network, (3) to promote and support the leadership and (4) to increase the visibility of female role models. Entropy 2019, 21, x FOR PEER REVIEW 6 of 17 funded by the US Natio al Science Foundation and the European Commission. Schiebinger states that gen ered innovation perspective can benefit excellence in research, policy, and practice in many fields, from Health and Medicine or Social Sciences to Engineering and Technology [18]. I artic lar, ac i e ear i a ata Scie ce areas s ose e o ort ities to i cl e ge ere i o atio i I for atio eory beca se t ey ca be a lie to a y iffere t o ai s, a t ere is a i crease i e a f r e ata scie tists. ata Scie ce jobs a e a i orta t co s lti g co o e t, begi i g it a b si ess co text ersta i ase t efi e als a create a project plan. s it as ee re ie e i t e i tr cti , t e artici ati f e i r tea s as c s lta ts, esi ers, r r cers f tec l is e f r ac ie i i le els f creati it a c llecti e i telli e ce, el i t a i iases i researc relate t a lac f i ersit a e er erspective. e effects of not taking into account gender and sex are described in References [18,28,33–40], in the context of Machine Tra slation and Natural Langua e Processing algorithms, s ftware development, smart cities, or robotics. 3. Decreasing the Gender Diversity Gap: Working Program In this Section we present the ETSE-UV working program, which is organized around four main actions, illustrated in Figure 1: (1) to provide institutional encouragement and support, (2) to increase the professional support network, (3) to promote and support the leadership and (4) to increase the visibility of female role models. Figure 1. ETSE-UV main actions targeting the gender diversity problem. 3.1. ETSE-UV: Academic Environment and Support for the Program The ETSE-UV concentrates the engineering studies of the University of Valencia, namely, Chemical Engineering, Computer Science Engineering, Industrial Electronic Engineering, Multimedia Engineering, Telecommunications Electronic Engineering, Telematics Engineering, and Data Science. In this sense, the ETSE-UV faces a considerable challenge when dealing with the reduction of the gender gap in STEM both at the student and academic and professional levels due to the broad-spectrum of the offered degrees. Specialized faculties or schools focusing on a given degree, on the other hand, can also apply for this program with a considerably smaller effort. The ETSE-UV, as a higher education institution, can contribute to the gender gap reduction in STEM in two ways: First, interacting with secondary schools with the aim of fighting stereotypes— with female role models—and highlighting the applicability of the different STEM branches, and secondly, retaining and engaging female students once they have accessed the studies. This second action needs to conjugate the use of female role models and activities helping the students to build strong support networks during their studies. In a third axis, the institution should try to help as much as possible its graduates to find support networks across their professional careers. The program of the ETSE-UV targets some of the barriers stated in Reference [1], and it is aligned with the interventions proposed by UNESCO in Reference [8], specifically the extra-curricular student engagement, the mentorship, and role models. The four main actions of the program illustrated in Figure 1 are supported by conclusions or evidence arising from the long literature on Figure 1. ETSE-UV main actions targeting the gender diversity problem. . . - : e i ir e t rt f r t e r r - tr t t i ri t i s f t i rsit f l i , el , i l Engineering, Computer Science E gineering, Industrial Electronic ngineering, Multimedia Engineering, Telecommunications Electronic Engineeri g, Telematics Engineering, a d Data Science. In this sense, the ETSE-UV faces a considerable challenge wh n dealing with the reduction of the gender gap in STEM both at the student and academic and profession l levels due to the broad-sp ctrum of the offered egrees. Specialized faculties or schools focusing on a given degree, on the other hand, can also apply for this progr m with a considerably sm ller effort. - , i e ti i tit ti , t i t t t ti i in two ways: First, interacting with secondary schools with the aim of fighting stereotypes—with Entropy 2019, 21, 30 7 of 17 female role models—and highlighting the applicability of the different STEM branches, and secondly, retaining and engaging female students once they have accessed the studies. This second action needs to conjugate the use of female role models and activities helping the students to build strong support networks during their studies. In a third axis, the institution should try to help as much as possible its graduates to find support networks across their professional careers. The program of the ETSE-UV targets some of the barriers stated in Reference [1], and it is aligned with the interventions proposed by UNESCO in Reference [8], specifically the extra-curricular student engagement, the mentorship, and role models. The four main actions of the program illustrated in Figure 1 are supported by conclusions or evidence arising from the long literature on the topic, such as in References [19–21,23,41–46]. As UNESCO points out in Reference [8], promoting more female role models in STEM fields, more precisely, female students and faculty members in higher education, is an important strategy to attract women and girls into STEM fields. Report [8] also highlights the need for support programs and initiatives for female STEM professionals. Please note that the program presented in this paper is an exploratory initiative that started in 2011, while countries such as the United States have already implemented STEM intervention programs on several college campuses during the last years. These programs are designed to increase the participation of under-represented students in STEM fields, which includes women. We refer the reader to Reference [41] for a description of several programs currently working at different colleges, focusing on strategies such as academic advising, faculty mentorship, tutoring, internship opportunities, and career and skill development. The program evaluated in Reference [41] hypothesized that perceived benefits of STEM intervention programs should be related to an engineering major confidence, which at the end relates to the persistence in the STEM field. Basically, they focused on evaluating the impact of the program on the self-confidence or self-efficacy of the female students, which has been proved to be a fundamental indicator of STEM performance and perseverance [42,43]. Reference [42] found out that even if some programs were not designed to increase STEM self-efficacy, there is was a direct correlation between increased performance and an increase in self-efficacy. In Reference [42], some interventions are recommended to increase the self-efficacy at different educational levels and contexts, namely, the use of mastery experience, vicarious experience, social persuasion, and the consideration of physiological reactions. Finally, Reference [44] also provides evidence-based recommendations for best practices to improve STEM diversity, and, interestingly, highlights that there is still a need to understand how to translate research in this field into evidence-based interventions. As a future research direction, the authors of Reference [44] recommend targeting not only women’s attitudes toward STEM but also family, and STEM faculty and employers. To the best of our knowledge, this program is a unique initiative in Spain in the sense that (i) it started with the beginning of Bologna compliant bachelor degrees (2011) and (ii) it goes beyond the formal requirement of an equality plan definition, encompassing the three dimensions where the ETSE-UV (or a higher education institution) can apply some influence: Students in the years prior to the University, students already enrolled in STEM disciplines, and new graduates in STEM branches. The problem of equality plans at the global institution level (the University of Valencia, in our case) is that they give general recommendations, whereas reports such as in Reference [8] are not specifically addressing the higher education ecosystem (as we have previously identified, the primary target group is located in the range of 6 to 15 years old). 3.2. Actions and Recommendations for Practice In this subsection, we describe a set of activities per action which are straightforward to implement with basic budget allocation, and which allow motivating and retaining female students, as well as integrating their male colleagues. In the ETSE-UV case, a budget of 1500 euros are allocated each year for the promotion of student activities. In addition to this primary funding, faculty members and Entropy 2019, 21, 30 10 of 17 equilibrium threshold), engaging and supporting female faculties in the development of leading roles. Following the Statutes of the University of Valencia, an equality committee has been established, dealing with all aspects related to gender equality and diversity. D. Increasing the Visibility of Female Role Models The objective of this action is to recognize and disseminate the achievements of women in STEM fields. The main activities attempting this objective are the establishment of recognitions to pioneering women in the institution and giving awards to female students with outstanding final degree projects. 4. ETSE-UV: Case Study In this Section, quantitative and qualitative indicators supporting the ETSE-UV working plan are presented. The evaluation of a program of this type is a research field itself, and not many references can be found providing indicators based on objective metrics. For example, Reference [21] examines the gendered experiences of women graduated in engineering both in academia and in companies. They established a set of participants, from different fields, and realized a series of interviews which were used to search for patterns. In Reference [49], a research-based leadership development program is presented, designed with the aim of increasing and retaining women in STEM professions. This program targets women which are already in professional environments and its outcomes are evaluated based on the feedback from the participants. In Reference [41], evaluation of the hypothesis is done via cohort surveys at different stages of the program until student’s graduation. Report [50] analyzes the evolution of the number of registered female students in ICT-related engineering (mainly Computer Science and similar degrees), considering the case of Spain. The authors also face the problem of quantifying the impact of affirmative actions towards promoting the enrollment of female students. In their paper, they establish a set of metrics to quantify: Evolution and percentages of female students, possible discontinuity points, percentages from other universities and trends in the results. Note that this methodology was also used in Reference [51]. To evaluate the ETSE-UV case, metrics have been obtained following Reference [50], as well as registering qualitative outcomes directly related to the different actions performed over time. Starting with the Bachelor level, Table 2 compares the evolution of the percentage of enrolled female students in ICT-related degrees. The second column collects the aggregated data for the Pre-Bologna degrees at the ETSE-UV, namely, Computer Science Engineering, Electronic Engineering, Telecommunications Electronic Engineering and Telematics Engineering, while the fourth and fifth columns show the aggregated data for the Bologna compliant degrees at the Spanish and ETSE-UV levels, respectively. The program presented in this paper started in 2011, targeting the ETSE-UV’s Bologna compliant degrees (Computer Science Engineering, Multimedia Engineering, Industrial Electronic Engineering, Telecommunications Electronic Engineering, and Telematics Engineering). Therefore, Table 2 shows aggregated data and the pre and post program’s situation. Spanish data for the ICT category reflects that the percentage of enrolled female students decreases year after year. In general, ETSE-UV is positioned above the Spanish reference value both on average and each year. This is also supported in Reference [50], which states that the ETSE-UV is one of the Spanish Engineering Schools with a larger percentage of female students in the ICT field. Actually, it is the third University in Spain according to the percentage of women enrolled in ICT studies, and the second one in terms of the trend of this indicator. Disaggregating the data into the different degrees, Figure 2 shows the variation (%) of the percentage of enrolled female students with respect to the average percentage of female students enrolled in Pre-Bologna degrees (period 2006–2010). The reference average percentage values for the period 2006–2010 stand in a 14.53% in Telematics Engineering, 13.88% in Computer Science Engineering, 10.97% in Telecommunications Electronic Engineering, and 5.63% in Electronic Engineering, according to Reference [22]. Data of Computer Science Engineering and Multimedia Engineering have been Entropy 2019, 21, 30 11 of 17 aggregated in the 2010–2018 period to establish a fair comparison, following the categories established in Reference [52]. Table 2. Comparison of the pre/post percentage (%) of enrolled female students in the ETSE-UV. The second column shows the evolution in Pre-Bologna studies [22], while the fourth and fifth columns show the evolution in Spain [52] and at the ETSE-UV [22], respectively, for Bologna compliant degrees. Year ETSE-UV 1 Year ICT (Spain) ETSE-UV 2 2006–2007 12.96 2010–2011 13.36 13.55 2007–2008 12.53 2011–2012 13.33 15.28 2008–2009 12.57 2012–2013 13.04 13.49 2009–2010 12.31 2013–2014 12.89 14.69 2014–2015 12.6 14.52 2015–2016 12.00 14.12 2016–2017 12.06 14.56 2017–2018 Not available 15.02 Average 12.59 Average 12.75 14.41 1 Aggregated data for Computer Science Engineering, Electronic Engineering, Telecommunications Electronic Engineering, and Telematics Engineering. 2 Aggregated data for Computer Science Engineering, Multimedia Engineering, Industrial Electronic Engineering, Telecommunications Electronic Engineering, and Telematics Engineering. Entropy 2019, 21, x FOR PEER REVIEW 11 of 17 Engineering, 10.97% in Telecommunications El ctronic Engineering, and 5.63% in Electronic Engineering, according to Reference [22]. Data of Computer Science Engineering and Multimedia Engine ri have been aggregated in the 2010–2018 period to establish a fair comparison, following the categories established in Reference [52]. Figure 2. Comparison of the pre/post program situation. Variation in % of the percentage of enrolled female students with respect to the average percentage of female students enrolled in Pre-Bologna degrees for the period 2006–2010 (14.53% in Telematics Engineering, 13.88% in Computer Science Engineering, 10.97% in Telecommunications Electronic Engineering and 5.63% in Electronic Engineering). To understand results from Figure 2, one needs to bear in mind that the initial window 2010– 2011/2012–2013 can be regarded as a transient period in the implantation of the Bologna compliant degrees. Regarding the pre/post situation, Figure 2 proves that, in general, the percentage of female students is above the reference value (Pre-Bologna) during the evaluated period. Electronic, Computer Science and Multimedia Engineering show steady or positive trends, while the oscillations presented by Telematics Engineering indicate that a larger effort is needed in this degree. In any case, this exploratory analysis would benefit from the availability of a larger data window. We have also performed a chi-square (2) test of independence in order to evaluate whether there is a significant association between the categories of the variables Sex (female, male) and Program (pre, post) at a 0.05 significance level, which is summarized in Table 3. Results indicate that the relation between these variables was significant when considering the aggregated values of all ETSE-UV’s degrees ((1) = 10.274, p < 0.01), as well as for Computer Science and Multimedia Engineering ((1) = 10.929 , p < 0.001), Telematics Engineering ((1) = 5.3308 , p < 0.05) and Electronic Engineering ((1) = 6.7095, p < 0.01). Data show evidence that the percentages of female students vary between the different levels of the variable Program, with the exception of Industrial Electronic Engineering, which was not significant despite the increments shown in Figure 2 and Table 3. Evidence proves that these degrees are male-dominated, although a positive and significant effect is observed in the post program time period. 2010- 2011 2011- 2012 2012- 2013 2013- 2014 2014- 2015 2015- 2016 2016- 2017 2017- 2018 Telematics 4.54 15.54 -6.76 23.16 23.95 10.49 15.21 -1.68 Computer Science & Multimedia 40.27 29.31 21.29 24.49 21.68 11.97 22.21 35.74 Electronic -4.04 34.22 25.73 24.63 25.62 40.24 28.77 29.25 Ind. Electronic -60.53 31.57 -13.36 17.11 29.18 51.46 60.35 61.47 -65 -45 -25 -5 15 35 55 Variation in % with respect to Pre-Bologna Figure 2. Comparison of the pre/ st r r sit ti . Variation in % of the percentage of enrolled female students with respect to the average percentage of female students enrolled in Pre-Bologna degrees for the period 2006–2010 (14.53% in Telematics Engineering, 13.88% in Computer Science Engineering, 10.97% in Telecommunications Electronic Engineering and 5.63% in Electronic Engineering). To understand results from Figure 2, one needs to bear in mind that the initial window 2010–2011/2012–2013 can be regarded as a transient period in the implantation of the Bologna compliant degrees. Regarding the pre/post situation, Figure 2 proves that, in general, the percentage of female students is above the reference value (Pre-Bologna) during the evaluated period. Electronic, Computer Science and Multimedia Engineering show steady or positive trends, while the oscillations presented by Telematics Engineering indicate that a larger effort is needed in this degree. In any case, this exploratory analysis would benefit from the availability of a larger data window. We have also performed a chi-square (χ2) test of independence in order to evaluate whether there is a significant association between the categories of the variables Sex (female, male) and Program (pre, post) at a 0.05 significance level, which is summarized in Table 3. Results indicate that the relation between these variables was significant when considering the aggregated values of Entropy 2019, 21, 30 12 of 17 all ETSE-UV’s degrees (χ2(1) = 10.274, p < 0.01), as well as for Computer Science and Multimedia Engineering (χ2(1) = 10.929, p < 0.001), Telematics Engineering (χ2(1) = 5.3308, p < 0.05) and Electronic Engineering (χ2(1) = 6.7095, p < 0.01). Data show evidence that the percentages of female students vary between the different levels of the variable Program, with the exception of Industrial Electronic Engineering, which was not significant despite the increments shown in Figure 2 and Table 3. Evidence proves that these degrees are male-dominated, although a positive and significant effect is observed in the post program time period. Table 3. Relation between sex and program intervention. Raw numbers as well as % of female and male students are included in the pre and post columns. Degrees Pre (%) Program Post (%) p-Value a All Degrees <0.05 Female 712 (12.6%) 1126 (14.5%) Male 4938 (87.4%) 6623 (85.5%) Comp. Sci. & Mult. <0.001 Female 293 (13.9%) 520 (17.3%) Male 1814 (86.1%) 6623 (82.7%) Electronic <0.01 Female 208 (11.0%) 247 (13.8%) Male 1687 (89%) 1545 (86.2%) Ind. Electronic 0.1582 Female 18 (5.6%) 118 (7.9%) Male 301 (94.4%) 1368 (92.1%) Telematics <0.05 Female 193 (14.5%) 245 (17.8%) Male 1136 (85.5%) 1132 (82.2%) a p-value calculated by the chi-square test of independence, where text in bold indicates a statistically significant difference with a p-value less than 0.05. Regarding the percentage of graduated female students, statistics obtained from Reference [22] show that aggregating the values for all the Bologna compliant degrees and focusing on the 2013–2014/2017–2018 window, the ETSE-UV achieves an average value of 15.12% graduated female students. Note that this time period corresponds to the years when the first Bologna graduates were obtained, since Bologna compliant degrees establish a four year’s duration. To benchmark this value, data has been collected from universities with similar characteristics (both from the Mediterranean region) [52]. The first one is the Universitat Politècnica de València (UPV), a technical University also located in the city of Valencia. However, in this case, each faculty or school is specialized in a given degree. The second one is the Universitat Autònoma de Barcelona (UAB), located in Barcelona. This University also focuses the engineering studies in a technical school, facing similar challenges to the ETSE-UV as it has to compete for students with other universities placed in Barcelona. Note that data from UPV and UAB were not disaggregated by sex until 2014, and that data for 2017–2018 are not available yet [52], thus the actual comparison window spans from 2014–2015 till 2016–2017. Figure 3 shows, on the one hand, the evolution of the percentage of graduated female students for the ETSE-UV, UPV and UAB cases, as well as the Spanish value as a baseline. On the other hand, Figure 3 also includes the total number of graduated students (male and female) in the four cases, showing that the ETSE-UV, apart from exceeding UPV, UAB and the Spanish reference values, is at least maintaining the percentage of graduated female students. In order to establish a fair comparison, only data corresponding to Computer Science and Multimedia Engineering is considered in this evaluation. Entropy 2019, 21, 30 15 of 17 13. Heilman, M.E.; Okimoto, T.G. Motherhood: A potential source of bias in employment decisions. J. Appl. Pshychol. 2008, 93, 189–198. [CrossRef] [PubMed] 14. Heilman, M.E.; Okimoto, T.G. Why are women penalized for success at male tasks? The implied communality deficit. J. Appl. Pshycol. 2007, 92, 81–92. [CrossRef] 15. Eagly, A.H.; Wood, W. Social role theory. In Handbook of Theories in Social Psychology, 1st ed.; Van Lange, P.A.M., Kruglanski, A.W., Higgins, E.T., Eds.; Sage Publications: London, UK, 2012; Volume 2, pp. 458–476. 16. Weisgram, E.S.; Bigler, R.S.; Liben, L.S. Gender, values, and occupational interests among children, adolescents, and adults. Child Dev. 2010, 81, 778–796. [CrossRef] 17. Let Toys be Toys—For Boys and Girls. Available online: http://lettoysbetoys.org.uk/toy-catalogues-2017/ (accessed on 24 September 2018). 18. Buitendijk, S.; Maes, K. Gendered Research and Innovation: Integrating Sex and Gender Analysis into the Research Process; Technical Report; LERU: Leuven, Belgium, 2015; Available online: https://www.leru.org/publications/gendered-research-and-innovation-integrating-sex-and-gender- analysis-into-the-research-process (accessed on 28 September 2018). 19. Heilman, M.E.; Block, C.J.; Lucas, J.A. Presumed incompetent? Stigmatization and affirmative action efforts. J. Appl. Psychol. 1992, 77, 536–544. [CrossRef] 20. Heilman, M.E.; Block, C.J.; Stathatos, P. The affirmative action stigma of incompetence: Effects of performance information ambiguity. Acad. Manag. J. 1997, 40, 603–625. [CrossRef] 21. Smith, K.N.; Gaston Gayles, J. “Girl power”: Gendered academic and workplace experiences of college women in Engineering. Soc. Sci. 2018, 7, 11. [CrossRef] 22. Transparency Portal. Annual UV Data Book Report. Available online: https://www.uv.es/uvweb/uv- transparency/en/people/pdi/statistiscal-data-1285924386828.html (accessed on 21 September 2018). 23. Weisgram, E.S.; Bigler, R.S. Girls and science careers: The role of altruistic values and attitudes about scientific tasks. J. Appl. Dev. Pshychol. 2006, 27, 326–348. [CrossRef] 24. Su, R.; Rounds, J.; Armstrong, P.I. Men and things, women and people: A meta-analysis of sex differences in interests. Psychol. Bull. 2009, 135, 859. [CrossRef] 25. Diekman, A.B.; Eagly, A.H. Of men, women, and motivation: A role congruity account. In Handbook of Motivation Science, 1st ed.; Shah, J.Y., Gardner, W.L., Eds.; Guilford Press: New York, NY, USA, 2008; pp. 434–447. 26. Diekman, A.B.; Brown, E.R.; Johnston, A.M.; Clark, E.K. Seeking congruity between goals and roles: A new look at why women opt out of science, technology, engineering, and mathematics careers. Psychol. Sci. 2010, 21, 1051–1057. [CrossRef] [PubMed] 27. Diekman, A.B.; Steinberg, M.; Brown, E.R.; Belanger, A.L.; Clark, E.K. A goal congruity model of role entry, engagement, and exit: Understanding communal goal processes in STEM gender gaps. Pers. Soc. Pshychol. Rev. 2017, 21, 142–175. [CrossRef] [PubMed] 28. What Is Gendered Innovations? Available online: http://genderedinnovations.stanford.edu (accessed on 21 September 2018). 29. Jordan, M.I.; Mitchell, T.M. Machine learning: Trends, perspectives, and prospects. Science 2015, 349, 255–260. [CrossRef] [PubMed] 30. O’Neil, C. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, 1st ed.; Broadway Books, Crown Publishing Group: New York, NY, USA, 2016; ISBN 9780553418811. 31. Nielsen, M.W.; Bloch, C.W.; Schiebinger, L. Making gender diversity work for scientific discovery and innovation. Nat. Hum. Behav. 2018, 2, 726–734. [CrossRef] 32. De Madariaga, I.S. Advancing gender equality in research and innovation in Europe and beyond: COST Network genderSTE. J. Res. Gender Stud. 2013, 3, 131–143. 33. Magallón Portolés, C. Science from women’s lives. Better Science? How gendered studies improve science and lives. Mètode SSJ 2017, 7, 105–111. [CrossRef] 34. Valles, N.; Palmen, R. Guía Práctica Para la Inclusión de la Perspectiva de Género en los Contenidos de la Investigación; Caprile, M., Ed.; CIREM Fundación: Madrid, España, 2012; Available online: http://www.idi.mineco.gob.es/stfls/MICINN/Ministerio/FICHEROS/UMYC/Guia_ practica_genero_en_las_investigaciones.pdf (accessed on 23 September 2018). Entropy 2019, 21, 30 16 of 17 35. Bolukbasi, T.; Chang, K.W.; Zou, J.Y.; Saligrama, V.; Kalai, A.T. Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In Advances in Neural Information Processing Systems (NIPS) 29, Proceedings of the Neural Information Processing Systems Conference, Barcelona, Spain, 5–10 December 2016; Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R., Eds.; Curran Associates Inc.: New York, NY, USA, 2016; pp. 4349–4357. 36. Schiebinger, L. Interview by J. V. Bernabeu Mètode SSJ (She Science Monograph). 2016. Available online: https://metode.org/issues/monographs/londa-schiebinger.html (accessed on 23 September 2018). 37. Schiebinger, L.; Klinge, I. Gendered Innovations: Mainstreaming Sex and Gender Analysis into Basic and Applied Research; Technical Report for European Commission: Brussels, Belgium, 2010. Available online: http://www.eif.gov.cy/mlsi/dl/genderequality.nsf/0/7CE1FD0F091FAD76C22580F9002FBC4E/ $file/Mainstreaming%20sex%20and%20gender%20analysis%20%20into%20basic%20and%20applied% 20research.pdf (accessed on 23 September 2018). 38. Faulkner, W.; Lie, M. Gender in the Information Society: Strategies of inclusion. Gend. Technol. Dev. 2007, 11, 157–177. [CrossRef] 39. Female Interaction Strategy. Available online: http://www.infinit.dk/download/Aktiviteter/klaus_ schroeder.pdf (accessed on 24 September 2018). 40. Advisory Group for Gender. For a Better Integration of the Gender Dimension in the Horizon 2020 Work Programme 2018–2020; Technical Report for European Commission: Brussels, Belgium, 2016; Available online: http://ec.europa.eu/transparency/regexpert/index.cfm?do=groupDetail.groupDetailDoc&id=28824& no= (accessed on 18 September 2018). 41. Morton, C.S.; Beverly, M.S. Can I really do this? Perceived benefits of a STEM intervention program and women’s engineering self-efficacy. In Proceedings of the ASEE Annual Conference & Exposition, Columbus, OH, USA, 25–28 June 2017; Available online: https://www.asee.org/public/conferences/78/ papers/19036/view (accessed on 3 November 2018). 42. Rittmayer, A.D.; Beier, M.E. Overview: Self-efficacy in STEM. In Applying Research to Practice (ARP) Resources; Bogue, B., Cady, E., Eds.; 2009; Available online: https://www.engr.psu.edu/awe/secured/director/ assessment/Literature_Overview/PDF_overviews/ARP_SelfEfficacy_Overview_122208.pdf (accessed on 3 November 2018). 43. Vogt, C.M. Faculty as a critical juncture in student retention and performance in engineering programs. J. Eng. Educ. 2008, 97, 27–36. [CrossRef] 44. Wang, M.T.; Degol, J.L. Gender gap in Science, Technology, Engineering, and Mathematics (STEM): Current knowledge, implications for practice, policy, and future directions. Educ. Psychol. Rev. 2017, 29, 119–140. [CrossRef] 45. Stout, J.G.; Dasgupta, N.; Hunsinger, M.; McManus, M.A. STEMing the tide: Using ingroup experts to inoculate women’s self-concept in science, technology, engineering, and mathematics (STEM). J. Pers. Soc. Psychol. 2011, 100, 255–270. [CrossRef] 46. Rabenberg, T.A. Middle school girls’ STEM education: Using teacher influences, parent encouragement, peer influences, and self efficacy to predict confidence and interest in math and science. Doctoral Dissertation, Drake University, Des Moines, IA, USA, 2013. 47. Elstad, E.; Turmo, A. The influence of the teacher’s sex on high school students’ engagement and achievement in science. GST 2009, 1, 83–104. Available online: http://genderandset.open.ac.uk/index.php/ genderandset/article/view/41 (accessed on 2 November 2018). 48. Mulji, N. The Role of Teacher Gender on Students’ Academic Performance. Master’s Thesis, Lund University, Lund, Sweden, 2016. 49. Van Oosten, E.B.; Buse, K.; Bilimoria, D. The leadership lab for women: Advancing and retaining women in STEM through professional development. Front. Psychol. 2017, 8, 1–5. [CrossRef] 50. Merelo Gervós, J.J.; Merelo Molina, C. Evolución de la Matrícula Femenina en el Grado de Informática en Universidades Públicas Españolas. Available online: https://search.datacite.org/works/10.13140/RG.2.2. 19608.08969 (accessed on 2 November 2018). 51. Galpin, V.; Sanders, I. Gender imbalances in computer science at the University of the Witwatersrand. ACM SIGCSE Bull. 1993, 25, 2–4. [CrossRef] Entropy 2019, 21, 30 17 of 17 52. Spanish Ministry of Education. Statistics and Reports: University Statistics. Available online: http://www. educacionyfp.gob.es/servicios-al-ciudadano-mecd/estadisticas/educacion/universitaria.html (accessed on 12 December 2018). 53. Romero, V. What It Takes to Succeed as a Woman in a STEM Field (Report). Available online: http://theinstitute.ieee.org/ieee-roundup/blogs/blog/what-it-takes-to-succeed-as-a-woman-in-a-stem- field (accessed on 21 September 2018). © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).