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Article

Psychosocial Differences Between Female and Male Students in Learning Patterns and Mental Health-Related Indicators in STEM vs. Non-STEM Fields

by
María Natividad Elvira-Zorzo
1,*,
Miguel Ángel Gandarillas
2,* and
Mariacarla Martí-González
3
1
Department of Social Psychology and Anthropology, University of Salamanca, 37008 Salamanca, Spain
2
Department of Social, Work, and Differential Psychology, Complutense University of Madrid, 28223 Pozuelo de Alarcón, Spain
3
Department of Anthropology and Social Psychology, Complutense University of Madrid, 28223 Pozuelo de Alarcón, Spain
*
Authors to whom correspondence should be addressed.
Soc. Sci. 2025, 14(2), 71; https://doi.org/10.3390/socsci14020071
Submission received: 10 December 2024 / Revised: 17 January 2025 / Accepted: 23 January 2025 / Published: 29 January 2025
(This article belongs to the Section Gender Studies)
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Abstract

:
This study explores psychosocial differences in mental health indicators and learning patterns between male and female students to promote inclusion and equality in university classrooms, focusing on STEM and non-STEM fields. Using a holistic approach, five learning dimensions comprising a diversity-in-learning (DinL) construct were analyzed: Coping with Difficulties, Effort, Autonomy, Understanding/Career Interest, and Social Context. Psychosocial predictors, including paternal and maternal parenting patterns, were also examined. Data were collected through a questionnaire completed by 2443 students from 83 undergraduate and postgraduate programs. Results indicated that male students reported higher levels of autonomy, while female students reported experiencing more mental health difficulties related to learning. Gender differences in learning dimensions were more pronounced in non-STEM fields. Parental influences also differed, with fathers and mothers shaping male and female students’ learning patterns in ways that reflect traditional gender roles. The findings highlight the lasting effects of gender roles on learning habits and psychological challenges in higher education. The study underscores the importance of addressing gender patterns in parenting and education to support more women in pursuing STEM fields, ultimately fostering greater equality and inclusion in academia.

1. Introduction

This study examines gender differences in learning patterns and mental health-related indicators in science, technology, engineering, and mathematics (STEM) vs. non-STEM fields, as well as investigates the mediating roles of paternal and maternal parenting practices. The aim is to comprehend the role of sex/gender in the different learning styles, habits, strategies, psychological difficulties, and psychosocial contexts (understood here as diversity in learning, DinL), with the goal of promoting inclusion and equality in the university classroom. Research on the relationship between education and gender has a long history, highlighting gender disparities in learning styles, habits, strategies, and mental health (e.g., Aldosari et al. 2018; Alonso et al. 1995; ALMasaudi 2021; Berlin et al. 2012; Boson et al. 2019; Camarero Suárez et al. 2000; Casad et al. 2017; Castillo Riquelme et al. 2021; Entwistle et al. 1979; Garber et al. 2017; Graves et al. 2021; Ketchen Lipson et al. 2015; Naz et al. 2020; Meyer 1995; Meyerson et al. 2017; Poorolajal et al. 2017; Pedrelli et al. 2015; Rodrigo et al. 2018; Cvencek et al. 2021; Yu 2019). The study is also focused on the relationship of parenting styles of fathers and mothers with the students’ DinL as they show differential influence on students’ learning patterns and mental health. Paternal and maternal differential rearing practices shape critical learning dimensions in their sons and daughters regarding effort, autonomy, and coping with difficulties, which are central to understanding gender differences in education (Bully et al. 2019; Nerín et al. 2014; Graves et al. 2021; Gunderson et al. 2012; Masud et al. 2019; Moss-Racusin et al. 2012; Richardson and King 1991).
The concept of mental health encompasses biological, psychological, and social well-being (Vera-Villarroel et al. 2016; World Health Organization 2001, 2014). Mental health is of particular importance for learning and overall well-being in education, with university students increasingly facing mental health challenges (Francis and Horn 2017; Gao and Wei 2023; Lattie et al. 2018; Lipson et al. 2019; Shan et al. 2022; Worsley et al. 2022). Given the pivotal role of STEM fields in the Fourth Industrial Revolution and the Sustainable Development Goals, it is crucial to gain insights into the mental health status and learning patterns of female students, as compared to male students, in these fields and on the psychosocial factors that may affect them (Arredondo Trapero et al. 2019; UNESCO 2019), considering the lower proportion of females, their inclusion levels and inequality in these key educational and professional fields (Cheyan et al. 2017; Sáinz 2020; TIMSS 2015; Raabe et al. 2019; UNESCO 2019).
The central framework of this study is the integrative construct of Diversity-in-Learning (DinL), which encompasses psychological processes, styles, habits, strategies, difficulties, and social contextual factors that influence students’ ways of learning in a classroom. By addressing DinL, this study aims to foster psychosocial inclusion and equality in education while promoting creative, active, and collaborative learning. The use of a DinL scale, including its 5 dimensions (Effort, Autonomy, Coping with Difficulties, Learning by Understanding/Career Interest, and Social Context), may facilitate a more global understanding of gender differences in higher education.

1.1. Male-Female Differences in Learning Dimensions: Theoretical Framework

Research literature on learning styles, habits, and strategies points to male-female differences in key learning dimensions:
Effort. Effort and perseverance are crucial factors influencing academic performance (ALMasaudi 2021; Naz et al. 2020). Saura et al. (2014) identified gender differences in the attribution of success. Girls were found to be more inclined to attribute academic success to effort, whereas boys were more likely to attribute success to their skills and failure to lack of effort. Fortin et al. (2015) observed greater effort among female high school students, with improved academic performance and inclination towards pursuing a university education.
Autonomy. High autonomy, self-determination, and self-regulation appear to be related to greater achievement in mathematics (León et al. 2015). Regarding gender differences, autonomy support seems to benefit male more than female students in their learning performance (Lietaert et al. 2015). Female students showed lower levels of perceived self-efficacy within the self-regulated learning cycle than male students (Blackmore et al. 2021).
Information processing and in-depth understanding. Severiens and Ten Dam (1994) reported male students using abstract concepts more frequently than female students. Additionally, males appeared inclined to process information at greater depth and demonstrated greater extrinsic and achievement motivation compared to females. Different studies report female students using strategies for information acquisition and retrieval more often than their male counterparts (Fuente et al. 1994; Lugo et al. 2012; Del Buey and Suárez 2001). Females also appear to adopt better strategies for organizing information through codification, schemes, summaries, and keywords (Rivera 2010; Inzunza Melo et al. 2020; Aguado 2011; Carreño et al. 2011). Traits such as Openness to Experience, Agreeableness, and Conscientiousness, which are associated with academic success, were found to be higher in female students (Steinmayr and Kessels 2017), whereas males tend to employ theoretical styles and critical thinking more frequently (Camarero Suárez et al. 2000; Inzunza Melo et al. 2020; Severiens and Ten Dam 1994).
Social learning context. Rivera (2010) and Aguado (2011) found that females prefer teamwork and use better affective strategies compared to males. Henderlong Corpus and Lepper (2007) noted that the intrinsic motivation of females increased with product and process praise but not with person praise, unlike males. This suggests that females may have poorer self-esteem, relying more on academic achievements. Other studies report that female students generally show higher social motivation and emotional intelligence (Herrera et al. 2017; Rodrigo et al. 2018), adopt a more superficial approach to learning (Severiens and Ten Dam 1994), and experience greater fear of failure (Clark 1986; Kolb 1984; Miller et al. 1990).

1.2. Male-Female Differences in Mental Health in Education

Gender differences exist in mental health, with females being generally more vulnerable to affective disorders such as depression and anxiety, as compared to males (e.g., Castillo Riquelme et al. 2021; Domes et al. 2010; Gaibor-González and Moreta-Herrera 2020; Garnefski et al. 2004; Nolen-Hoeksema 2012; Kelly et al. 2008; Kessler 2003; Kessler et al. 2007; Steel et al. 2014; Stevens and Hamann 2012; Colorado and Bisval 2015; Whittle et al. 2011; Witkin and Goodenough 1981). Studies consistently show that female students experience higher levels of school anxiety than males (Essau et al. 2000; Lopez et al. 2008; Méndez et al. 2002; Steinhausen et al. 2008; Puskar et al. 2003). Similar results have been reported at the university level (Álvarez et al. 2012; Caballo et al. 2010; Chaplin and Aldao 2013; Dell’Osso et al. 2015; Fernández-Castillo 2009; Latas et al. 2010; Lund and Ross 2017). These findings do not seem to affect gender differences in achievement tests, especially since female students showed higher grades than their male counterparts in several studies (Mammadov 2022; Spinath et al. 2014; Voyer and Voyer 2014).
Graves et al. (2021) found that female students reported higher levels of perceived stress and adopted more socialization and emotion-focused coping strategies, while male students tended to focus on instrumental problem resolution or diversion. Other studies report that male students demonstrate better results in coping with anxiety, depression and stress control (Rivera 2010; Aguado 2011; Inzunza Melo et al. 2020; Perchtold et al. 2019; Rodrigo et al. 2018; Doná et al. 2010; Tran et al. 2017). Herrera et al. (2020) found cross-cultural consistency in male-female differences in stress management and adaptability, with boys exhibiting higher levels of adaptability in primary education. Male students often use meaning-focused and reappraisal strategies, while female students tend to seek more support during stressful situations (González Cabanach et al. 2013; Martínez et al. 2019). Male-female differences in emotional regulation may be associated with gender differences in a variety of psychopathologies (Whittle et al. 2011; Zimmermann and Iwanski 2014).
Nevertheless, both genders experience mental health issues, which can lead to academic underachievement and risky behaviours (Baader et al. 2014; Eisenberg et al. 2013; Khodarahimi and Fathi 2016; Micin and Bagladi 2011; Said et al. 2013; Saleh et al. 2017). Factors such as exam stress, increased academic demands, and financial crises contribute to rising mental health problems among university students (Beltrán 1993; Caballero et al. 2007; Eisenberg et al. 2013; Gili et al. 2014; Hu and Yeo 2020; Leppink et al. 2016; Leupold et al. 2020; Mayorga-Lascano and Moreta-Herrera 2019; San Gregorio et al. 2003). Overall, the field of mental health differences between female and male students presents some inconclusive and contradictory findings, warranting further investigation, particularly in STEM fields.

1.3. Gender and STEM

In recent decades, addressing the relatively low proportion of female students in STEM fields has become a significant concern due to the inequalities it implies. Disparities in STEM careers are evident globally (e.g., García 2000; Cheyan et al. 2017; García-Holgado et al. 2019; Montgomery and Fernández-Cárdenas 2018; OECD 2015; Reinking and Martin 2018; Romero and Blanco 2018; Cordero and Frutos 2015; Wang and Degol 2016; UNESCO 2019). The TIMSS study (2015) in 110 countries revealed that only 30% of students choose STEM careers, impacting both their educational pursuits and job choices within these fields. Other studies in different countries reveal similar trends (e.g., Cheyan et al. 2017; García-Holgado et al. 2019; Raabe et al. 2019; Sáinz 2020; UNESCO 2019). Additionally, there is a significant gender disparity among educators in STEM areas (Reinking and Martin 2018).
Gender socialization, influenced by prevalent stereotypes, impacts educational and career choices, with mothers often shaping their daughters’ decisions (UNESCO 2019). Bello (2020) highlights the influence of patriarchal societies and associated family factors on females pursuing scientific careers, which are evident globally and rooted in both structural and cultural norms. Gender attributes and biases from parents and educators, as well as self-biases, influence the choice of academic subjects and performance in various domains (Archer et al. 2018; Cvencek et al. 2021; Desy et al. 2011; Gunderson et al. 2012; Moss-Racusin et al. 2012; Osborne et al. 2003). Gender stereotypes impact academic performance, particularly in mathematics, where females are perceived to have poorer spatial skills. Negative attitudes towards mathematics, influenced by gender identity, can affect the learning outcomes of female students (Casad et al. 2017; Cheema and Kitsantas 2014).

1.4. Parenting Dimensions Affecting Male-Female Differences in Learning

This research explores key psychosocial characteristics influencing the disparities between male and female students in learning dimensions, contributing to our understanding of inclusion and equality in academia and, thus, how to improve the current state of affairs. While various factors such as the household financial situation (Guterman and Neuman 2018; Rodríguez-Hernández et al. 2020; Martineli et al. 2018; Piccolo et al. 2016), parents’ levels of education (Guterman and Neuman 2018; Masud et al. 2019; Silva-Laya et al. 2020), and family culture and origin (Kim et al. 2020; Worrell 2014) influence learning, we focused here on how the roles of fathers and mothers impact gender differences in learning.
The traditional research literature on parenting practices highlights three major dimensions, here named: Care (affection and support for the child’s development), Control (discipline and limits), and Protection (Gandarillas 1995, 2022). Different parenting styles of fathers and mothers, such as democratic, authoritative, and permissive styles, stem from combinations of these dimensions and significantly impact the development of learning-related psychological patterns (e.g., Affrunti and Ginsburg 2012; Agbaria and Mahamid 2023; Batool 2019; Bully et al. 2019; Nerín et al. 2014; Fuentes et al. 2015; Gfellner and Córdoba 2020; Gómez-Ortiz et al. 2015; Gordon and Cui 2012; Hernesniemi et al. 2017; Jaureguizar et al. 2018; Kim et al. 2020; Masud et al. 2019; Fernanda-Molina et al. 2017; Duarte 2019; Walsh et al. 2023). Given that males and females are typically assigned different social roles in most cultures, we can anticipate distinct influences of parenting styles and parenting practices on male and female learning patterns. Moreover, the different roles of fathers and mothers in their sons’ and daughters’ learning patterns are of particular interest (Bully et al. 2019; Nerín et al. 2014; Graves et al. 2021; Gunderson et al. 2012; Masud et al. 2019; Moss-Racusin et al. 2012), as they may differentially shape their learning development.

1.5. Addressing the Needs for Research in Male-Female Differences in STEM Fields Based on the Diversity in Learning Patterns

Research exploring gender differences in academic studies reveals a variety of findings across different factors without clear conclusions. Moreover, most of the research in this domain concentrates on primary and secondary education, with fewer studies conducted in higher education settings. Therefore, there is a need for additional research from a wider psychosocial perspective to improve our understanding of and mitigate gender-based inequalities in higher education, particularly when it comes to STEM. It is also crucial to include consideration of mental health issues in learning, given the rising concern in this area in recent years.
Our research is guided by a DinL construct, defined as the set of psychological processes, styles, habits, strategies, difficulties, psychosocial, and contextual factors that build the different ways students learn in a classroom (Gandarillas et al. 2024a). DinL expands beyond traditional fields into a comprehensive framework sensitive to psychosocial diversity, aimed at fostering inclusion and equality in the classroom while promoting creative, active, and collaborative learning through its integration into study groups.
Drawing from the research literature and our previous studies (Gandarillas et al. 2024a, 2024b), we apply DinL through five learning dimensions:
  • Coping with Difficulties: Managing mental health distresses and psychosocial difficulties related to learning, such as anxiety, apathy, demotivation, bad mood, irritability, lack of attention, low achievement expectations, and difficulties dealing with the social learning environments.
  • Effort: Reflecting perseverance, regularity, capacity to delay reward, and internal attribution of performance.
  • Autonomy: Embracing active learning, integrating information from various sources, developing personal theories, and seeking evidence.
  • Learning by Understanding and Career Interest: Demonstrating intrinsic motivation to deeply understand the discipline for professional preparation.
  • Social Context: Preferring studying alone or in groups and choosing the study environment (home vs. university).
In this study, we adopt the World Health Organization’s definitions of sex and gender. Sex refers to the biological characteristics distinguishing males and females at birth, while gender encompasses socially constructed attributes, norms, behaviours, and roles associated with being male or female (World Health Organization 2023). We use female and male to denote biological sex and feminine and masculine to refer to gender.
Our general hypothesis anticipated significant differences in the learning dimensions of DinL between university males and females. Specifically, we hypothesize that:
H1. 
Females will report more significant mental health issues related to learning compared to males.
H2. 
Males will exhibit higher levels of autonomy in learning.
H3. 
No differences in the dimension of Learning by Understanding/Career Interest are expected.
H4. 
Females will demonstrate greater effort and preferences for social studying contexts.
H5. 
Maternal roles will be stronger predictors than paternal roles of mental health issues and learning dimensions in university students.
H6. 
There will be significant differences between males and females in STEM and non-STEM subjects, with pronounced variations in Coping with difficulties and Autonomy.

2. Materials and Methods

2.1. Sample

The sample was composed of 2443 students (1453 undergraduate, 598 Master’s, and 392 PhD students) in 83 different programmes (social, science, humanities and technical), with a mean age of 23.7 years old (Standard Deviation 8.2). 28% of the students were males and 72% were females. Regarding the country of origin, 74.9% of students were born in Spain, and 25.1% were born in different countries in Africa, Asia, Europe, and the USA.

2.2. Instrument

A questionnaire was used that adopted the DinL scale—a self-administered scale assessing the main dimensions that build the DinL in the classroom (see Appendix A). The 4-point Likert scale (1 = Never or Hardly ever, 2 = Sometimes, 3 = Often, 4 = Very often) comprises 28 items in 5 dimensions: (1) Coping with Difficulties (9 items); Effort (6 items); Autonomy (5 items); Understanding and Career Interest (5 items); and Social Context (3 items). The scores for each factor were obtained from the means of all the items belonging to the factor. The items regarding mental health issues in learning used in the analyses of this work are included in the Coping with Difficulties dimension (see Appendix A). The scores of the items in this factor were reversed before obtaining the factor scores. Lower levels in the items in this dimension (regarding mental-health issues and psychosocial difficulties) indicate higher levels of coping with emotional difficulties and psychosocial distress in the study process. The scale was built based on overarching bibliographic research of the basic psychosocial and psycho-educational dimensions regarding learning styles, habits, strategies, and difficulties; (2) A theoretical-empirical development of the DinL construct with the dimensions comprising it; and (3) A survey with a large sample of university students to validate the scale, showing adequate indices of adjustment for the five subscales of the model. The scale has been used in other studies in different countries, and it always shows optimal psychometric properties (Gandarillas et al. 2024a, 2024b).
The reliability of the subscales in terms of internal consistency scores was between ω = 0.62 and ω = 0.80. Model fit indices showed adequate values (CFI > 0.90, RMSEA < 0.07). The questionnaire also included the most representative items of the parenting Care, Control and Protection dimensions, taken from the Egma Minnen av Bardndosnauppforstran (EMBU) scale (Arrindell et al. 1988, 2005)—a retrospective Likert-scale questionnaire, broadly used in different countries (e.g., Cheng and Wu 2021; Mathieu et al. 2020; Hou and Luo 2022) with optimal metric properties of the items as interval scales and three major parenting dimensions (named by the authors as Warmth, Rejection and Protection, corresponding to Care, Control, and Protection, respectively). Two representative items of the Care dimension (the most important parenting dimension), one item of Control, and one item of Protection for the mother and father were included. The items were selected on the basis of previous studies (Gandarillas 2022; Gandarillas et al. 2024a) showing the most statistical and conceptual significance for each dimension. The questionnaire also included questions on academic performance (average grades last year), biological sex, age, and field of study (see Appendix A). The item “biological sex” was used (instead of “gender”) as the authors were interested in exploring to what extent psychosocial factors (such as parenting patterns) influence the development of differences between students up to university age. This would also allow the analysis of the influence of parenting practices on the development of possible gender patterns in learning emerging from the results.

2.3. Design and Procedure

Based on a cross-sectional design, the above-mentioned questionnaire was administered online to 2655 students. Participation in the study was voluntary, confidential, and anonymous, including informed consent. This work followed ethical procedures in accordance with the Declaration of Helsinki (World Medical Association 2013) and had the approval of the Ethical Committee, at the Complutense University of Madrid, where the study was conducted.

2.4. Data Analysis

All cases with more than 5% of missing data or incorrect responses (random responses or clear errors) were rejected. The final database for further analysis included 2443 cases. Then, descriptive analyses were conducted, assessing the mean, standard deviation, asymmetry, and kurtosis of the items. The indices of asymmetry and kurtosis showed values ± 1.96 to assume a normal distribution (Mardia 1970). A 5-factor analysis (Oblimin Rotation) was carried out to confirm the factor structure. Factor analysis with Oblimin rotation was chosen due to the high probability of correlation between the latent dimensions of the DinL, considering the theoretically interrelated nature of the dimensions (Coping with Difficulties, Effort, Autonomy, etc.). This approach allows for a more accurate interpretation of the factors when the variables are not orthogonal. Factor scores were obtained based on the items included within each of the 5 factors (Coping with Difficulties, Effort, Autonomy, Understanding/Career Interest, and Social Context). The internal consistency of the data was assessed through the Omega coefficient of McDonald (ω), considering a lower limit below 0.70 to indicate acceptable reliability (Taber 2018). Pearson correlation coefficients were obtained to assess the relationships between variables and to identify the presence of multicollinearity.
One-way (Welch) analyses of variance (ANOVA) were carried out using the factor scores of each learning dimension and mental health indicators as dependent variables (DVs) by sex as independent variable (IV). To test sex differences in academic achievement, a one-way ANOVA was conducted using the average grades of the previous year (as reported by the student) as DV and biological sex as IV. Regarding paternal and maternal parenting dimensions, one-way ANOVAs were conducted on the representative indicators of each parenting dimension (Care, Control, and Protection) by the sex of the student. As there were two items representing the dimension of Care, a mean score of both items was used in the analyses. Multiple regressions (forward method) were carried out using the paternal and maternal parenting indicators as predictors for each learning dimension score as DVs, separately for male and female students.
Regarding STEM (vs. non-STEM), a group was made with the students of science, technology, engineering, and mathematical subjects (STEM group), and another group was made with the students of social sciences, humanities, and arts (non-STEM group). One-way ANOVAs were conducted for each learning factor by sex divided by STEM vs. non-STEM.

3. Results

3.1. Descriptives and Correlations

Table 1 shows the descriptives and Pearson correlations between the variables of the study. The results underscore the significant correlations between academic performance and learning dimensions/mental health indicators, as well as the substantial number of significant correlations between parenting practices and learning dimensions/mental health indicators in learning. The only learning dimension that did not demonstrate significant correlations with parenting practices was Social Context. No items were removed based on correlation results, as the data presented consistent patterns across the variables.

3.2. Male-Female Differences in Learning Dimensions: Study Findings

A one-way ANOVA (see Table 2) was performed to compare the factor scores in the learning dimensions plus the mental health indicators according to sex (1 = male, 2 = female). The results showed significant differences between the groups in Coping with Difficulties, Effort, and Autonomy. However, no statistically significant differences were found between the groups in Understanding/Career Interest and in Context. Males obtained higher mean scores than females on Coping, Effort (although with very small differences), and Autonomy. Regarding mental health indicators, all of them showed significant differences, with females showing higher means than males.
It is worth noting that female participants reported significantly higher scores in negative mental health-related indicators, including anxiety, irritability, and apathy, within the learning dimension of Coping with Difficulties. (High values in Coping with Difficulties should be understood as the average of low values in the indicators included in it). Scheme 1 shows the male-female differences in percentages. High levels of anxiety were reported by 50% of females, but only 26% of males; bad mood and irritability were reported by 21% of females vs. 12% of males; apathy, demotivation, and lack of interest affected 40% of females vs. 31% of males.
However, the results in terms of differences between males and females in academic performance (as obtained in the previous academic year) did not show statistical significance [F(1, 2.418) = 1.43, p > 0.05].

3.3. Differences in Paternal and Maternal Parenting Practices Related to Learning Dimensions

The one-way ANOVAs comparing parenting dimensions (Care, Control, and Protection of father and mother) between male and female students showed some significant differences (although all of them with p > 0.01): Paternal Care [F(1, 2.416) = 5.33; p = 0.02)], paternal Control [F(1, 2.406) = 3.95; p = 0.047)], and maternal Protection [F(1, 2.401) = 4.23; p = 0.04] (Figure 1). In summary, the results indicate that female students perceive slightly higher levels of parental care, control, and protection than male students.
The multiple regressions of paternal and maternal parenting characteristics predicting learning dimensions showed important differences (Table 3). The highest predicted dimension was Coping with Difficulties (which included the mental health indicators) for both females and males. Comparing the predictions in male and female students, their significance was higher in female than in male students in all the dimensions, with the highest difference in Coping with Difficulties (the dimension comprising the mental health indicators). For both sexes, maternal Care shows a significant positive prediction of Coping with Difficulties (i.e., higher maternal care predicts higher coping with emotional and psychosocial difficulties in learning), whereas Control shows a significant negative prediction of this factor. Maternal parenting appears more significant than paternal parenting for both sexes, although more present as a predictor of female’s learning dimensions.

3.4. Sex Differences in Learning Dimensions in STEM Fields

Figure 2 shows the proportion of students in each type of field in the sample. Health sciences are represented separately from the other STEM sciences in order to show the larger proportion of females compared to males in STEM fields overall. Table 4 shows the results of the differences between males and females in the learning dimensions divided into STEM and non-STEM subjects, using one-way ANOVAs. The differences are illustrated in Figure 3. There are more significant differences between males and females in the non-STEM fields (See Table 4).

4. Discussion

This study aimed to investigate gender differences in learning dimensions, mental health status in the learning environment, and their relationship with parenting practices among STEM and non-STEM students. The findings partially support our hypotheses, providing nuanced insights into the role of gender in shaping learning experiences and outcomes.
The results partly supported our initial hypotheses. In relation to hypotheses H1 and H2, significant differences were found in the learning dimensions of Coping with Difficulties and Autonomy. Males scored higher in both Coping with Difficulties and Autonomy. Always keeping into consideration the limitations of statistical analyses such as ANOVA to establish causality, these results may add support to other studies showing a tendency of males to find it easier to manage emotional and psychosocial hurdles in their studies (Rivera 2010; Brackett et al. 2006; Chaplin and Aldao 2013; Graves et al. 2021; Aguado 2011; MacCann et al. 2020; Doná et al. 2010; Susperreguy et al. 2018). Notably, we found significant differences in levels of anxiety, consistent with previous studies (e.g., Álvarez et al. 2012; Chaplin and Aldao 2013; Dell’Osso et al. 2015; Lund and Ross 2017). Similarly, higher levels of autonomy among male students is in line with existing literature (Blackmore et al. 2021; Lietaert et al. 2015; Nagy et al. 2010). The internalization of gender attributes from childhood may contribute to these differences. Henderlong Corpus and Lepper (2007) suggest that females may learn early on that their qualities are, in fact, not inherent, leading to greater stress regarding academic performance. Conversely, males may develop autonomy and intrinsic motivation based on their stronger perception of intrinsic qualities, requiring less approval from their social environment. In our study, the differences in learning dimensions did not seem to affect academic achievement, suggesting that females may achieve similar academic success despite facing greater mental health issues in the learning environment, potentially indicating that they undergo greater emotional strain to attain similar grades to males.
Regarding (H3), the findings indicate that being female or male does not significantly influence the use of deep understanding and career-focused learning in higher education. Compared to other studies suggesting male-female differences in various factors related to deep understanding and career-focused learning (Fuente et al. 1994; Lugo et al. 2012; Del Buey and Suárez 2001; Carreño et al. 2011; Severiens and Ten Dam 1994), our results did not support this notion when tested as a general dimension. Regarding H4, contrary to expectations and prior research, preferences for social context did not yield significant results despite literature suggesting females are more social in their studies.
Regarding parenting practices (H5), the study underscores the significance of the maternal role in child development. Although no substantial differences were found in parenting practices between fathers and mothers towards their sons and daughters, they appear as significant predictors of most learning dimensions, especially in terms of coping with difficulties in the learning environment. In general terms, maternal parenting practices, especially regarding care and control, appeared more present and significant than paternal parenting practices. In particular, there is a significant relation between higher care/lower control and better coping with difficulties (including mental health indicators) in both females and males. Additionally, the mother’s role appeared to have a greater influence on daughters than sons. Father’s protection emerged as a significant factor in fostering autonomy in both sons and daughters but encouraged career-focused and deep understanding only in sons. It is worth noting the relevance of the maternal role in fostering learning patterns more suited to STEM subjects, especially in female students. These findings underline the enduring traditional gender differences in the roles of fathers and mothers regarding their differential influence on their daughters’ and sons’ learning patterns and mental health in the learning environment, aligned with other studies in the field (Bully et al. 2019; Nerín et al. 2014; Graves et al. 2021; Gunderson et al. 2012; Masud et al. 2019; Moss-Racusin et al. 2012). Studies of this nature highlight the importance of understanding gender expectations as dynamic across cultures, generations, and individual families. While different societies are achieving more egalitarian roles, they may still retain some traditional, patriarchal gender and parental traits. As societal views on gender roles continue to evolve, the impact of maternal and paternal roles in shaping academic outcomes is likely to change as well.
Finally, regarding H6, the present study reveals less difference between male and female students of STEM fields compared to non-STEM fields. Male students exhibit similar levels in learning dimensions and mental health regardless of their field of study, while female students in STEM fields demonstrate learning profiles more similar to male students. This suggests that female students opting for STEM fields tend to exhibit higher levels of effort, autonomy, and deep understanding, with fewer mental-health-related and psychosocial difficulties (Gómez-Ortiz et al. 2015; Batool 2019). Conversely, female students of non-STEM fields exhibit the lowest scores in these factors, while male non-STEM students maintain similar or even higher scores than those studying STEM subjects. This discrepancy may be attributed to a poorer self-perception in terms of capability in learning and coping with mental health issues regarding their studies of female non-STEM students, potentially reducing their likelihood of pursuing STEM fields (Huang 2013; Casad et al. 2017). This interpretation partly explains the findings of other studies in the field regarding the lower presence of female students in STEM fields. The similarities in scores between male students of STEM and non-STEM fields in males could be due to greater self-esteem and less dependence on performance outcomes influenced by learned gender attributes (Archer et al. 2018; Arredondo Trapero et al. 2019).
Notably, the most significant differences between STEM and non-STEM fields are observed in the learning dimensions of Coping with Difficulties and Autonomy. These results, when linked to those regarding parenting styles and self-concept, provide additional support to the notion that differences in learning patterns and mental health between male and female students are intricately linked to the self-attribution of traditional gender roles and identity. Females who internalize traditional gender roles may face greater challenges in their learning patterns, education-related mental health, and attitudes toward STEM fields despite no discernible impact on their academic achievements. This suggests that females who embrace traditional gender roles may need to exert additional emotional effort in their studies to match their male counterparts’ academic performance (Cheema and Kitsantas 2014; Cheng and Wu 2021; Fortin et al. 2015; Cvencek et al. 2021).

5. Conclusions

The present study examined gender differences in learning patterns and mental health indicators between students of STEM and non-STEM subjects, also analyzing the relationship of parenting practices with the students’ learning patterns and mental health-related indicators in learning. The findings reveal several key conclusions that provide further insights into the influence of gender in higher education. The results are in line with most studies in the research literature, which find significant differences between male and female students in coping with difficulties and autonomy. Males appear to show greater ability to cope with mental health-related difficulties and higher levels of autonomy than females.
In terms of the impact of parenting practices, the study highlights the importance of the mother’s role in the development of students’ learning patterns and mental health. Parenting practices, especially high levels of maternal care and lower control, appear crucial factors that positively influence students’ ability to cope with difficulties. The results also suggest that the maternal influence is more pronounced for daughters than for sons. On the other hand, the father’s protective role is strongly associated with his sons’ autonomy and career-centred learning. These findings highlight the persistence of traditional gender roles in parenting practices and their impact on learning patterns. In any case, the impact of the mediating role of parenting practices on learning outcomes and mental health indicators requires further examination and more robust evidence to strengthen these conclusions.
Gender differences in learning patterns and mental health indicators are less pronounced in STEM fields than in non-STEM fields. Female students of STEM fields show learning profiles and psychosocial difficulties that are more similar to those of male students, which may indicate that females who choose these disciplines perceive themselves as more capable in terms of effort, autonomy, deeper understanding, and better mental health in learning. In contrast, females in non-STEM fields show lower scores for these factors, while males maintain similar or even higher levels than those studying STEM fields. This may indicate that perceived learning ability and coping with mental health issues may influence the choice of fields and academic performance.
The results of this study support the following general views:
  • Female university students expressed greater psychosocial difficulties, poorer perceived self-efficacy, and less autonomy in their studies than male students, but with no differences in academic performance.
  • Mothers’ and fathers’ roles continue to influence their sons’ and daughters’ learning and mental health patterns in higher education according to the traditional gender roles. The mother’s role continues to have a stronger and more significant impact than the father’s role.
  • Female students in STEM fields seem to be more similar to male students in their learning patterns and mental health than students in non-STEM fields.
Although the questionnaire asked about biological sex, the results appeared to show a traditional binary, hetero-normative perception of gender among university students today, that is to say, that these young people continue to internalize traditional attributes, stereotypes, and roles. Gender seems to affect female students more negatively than male students in higher education. Female students seem to have more difficulties with mental health and learning patterns than male students of similar academic performance and achievement.
Implications:
  • In the present study, traditional gender roles and attitudes seem to feature among university students and in the impact of their father’s and mother’s roles. Although we have made significant progress in equality between females and males in terms of presence and achievement in higher education, there is considerable room for psychological, psychosocial, and psychoeducational progress before we can reach a truly egalitarian education system.
  • If we are to improve the presence of females in STEM fields, we need to make greater efforts to reduce gender attributions from the early stages of education.
  • We need to apply more in-depth psychosocial and psychoeducational work on eliminating gender stereotypes—which will otherwise persist across generations—in order to achieve truly inclusive education for males and females.
  • Educational policies need to foster environments that encourage autonomy and emotional resilience, especially in fields where females are underrepresented. In addition, it is crucial to promote more inclusive and stereotype-free education from the earliest stages in order to reduce gender gaps in access and success in STEM and non-STEM fields.
In future studies, mediation analyses using the bootstrap method will be conducted to further explore how parental practices (care, control, protection) may influence differences in learning and academic achievement in female and male students. These analyses will provide additional empirical evidence on the mediating role of parental factors. Also, future studies using mixed designs, with a combination of qualitative and longitudinal analyses, may further explore gender interactions and differences between STEM and non-STEM fields. This approach will allow dynamic changes to be captured and contextual influences to be explored in greater depth.

Author Contributions

Conceptualization, M.N.E.-Z. and M.Á.G.; Methodology, M.N.E.-Z. and M.Á.G.; Validation, M.N.E.-Z., M.Á.G. and M.M.-G.; Formal analysis, M.N.E.-Z. and M.Á.G.; Investigation, M.N.E.-Z., M.Á.G. and M.M.-G.; Resources, M.N.E.-Z., M.Á.G. and M.M.-G.; Data curation, M.N.E.-Z., M.Á.G. and M.M.-G.; Writing—original draft, M.N.E.-Z., M.Á.G. and M.M.-G.; Writing—review & editing, M.N.E.-Z., M.Á.G.and M.M.-G.; Visualization, M.N.E.-Z. and M.Á.G.; Supervision, M.N.E.-Z., M.Á.G. and M.M.-G.; Project administration M.N.E.-Z. and M.Á.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the official Ethical Committee of the Complutense University of Madrid (UCM). CE_20211118-15_SOC 2021-11-18 on the 18 November 2021.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors upon request as appropriate.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Appendix A. Questionnaire Used in the Study

Table A1. Items of the Diversity in Learning (DinL) scale are grouped into the five DinL dimensions (1 = Never or hardly ever; 4 = Very often).
Table A1. Items of the Diversity in Learning (DinL) scale are grouped into the five DinL dimensions (1 = Never or hardly ever; 4 = Very often).
DimensionItem
Coping with difficulties15The circumstances determine the final results of my studies (learning, grades), whether they are good or bad.
20Bad mood/Irritability.
21Anxiety/nervousness.
22Apathy/Discouragement/Reluctance.
23Difficulties in attention and concentration.
25Poor expectations of academic achievement or success.
26Low interest of classmates in learning.
27The university lacks resources for the students.
28Difficulties at home in concentrating on studying (home environment, room to study, etc.).
Effort1I study with perseverance and regularity.
3I study and concentrate hardest under the pressure of an upcoming exam.
9I am able to manage my time and study environment.
10I am able to delay the satisfaction of desires or impulses.
12Frequent daily reading (including all sorts of texts).
24Poor consistency in my study habits.
Autonomy4I like to develop my own theories and I pay attention to whether there are real examples to support or refute my theories.
5I search for useful and practical applications of new knowledge.
8I read complementary texts and watch videos that are not required for the exams, for my own knowledge.
13I organise and integrate information gathered from different sources in my learning.
14I search for evidence of my theories.
Understanding/Career Interest6When I study, I focus on understanding the concepts more than anything else.
7I memorize the concepts and theories without needing to understand everything perfectly.
11When studying, I focus primarily on relating ideas and concepts.
18The main focus of my studies is professional development for my career.
19I know all the profiles and professional prospects of my course with a view to my future career.
Social Context2I believe that studying in a group helps me to solve questions that I cannot solve by myself.
16I study in in spaces provided at the university.
17I study at home.
Note. The numbers in bold indicate inverse items.
Items from the EMBU scale:
(1 = Never or hardly ever; 4 = Very often)
  • Parental Care:
    −
    I think that my parents tried to make my adolescence stimulating, interesting and instructive (for instance by giving me good books, arranging for me to go to holiday camps, taking me to clubs).
    −
    When faced with a difficult task. I felt supported by my parents.
  • Parental Control:
    My parents would punish me strictly, even for trifles (small offenses).
  • Parental Protection:
I think that my parents’ anxiety that something might happen to me was unwarranted.
Biological sex:
  • Male.
  • Female.
  • I prefer not to answer
Age
Field of study
Academic performance:
What was your most common grade last year?
  • F (grade below 5)
  • E-D-C (grades between 5–6)
  • B (grades between 7–8)
  • A (grades between 9–10)

References

  1. Affrunti, Nicholas W., and Golda S. Ginsburg. 2012. Maternal overcontrol and child anxiety: The mediating role of perceived competence. Child Psychiatry and Human Development 43: 102–12. [Google Scholar] [CrossRef]
  2. Agbaria, Qutaiba, and Fayez Mahamid. 2023. The association between parenting styles, maternal self-efficacy, and social and emotional adjustment among Arab preschool children. Psicologia: Reflexão e Crítica 36: 10. [Google Scholar] [CrossRef]
  3. Aguado, Mercedes López. 2011. Estrategias de aprendizaje en estudiantes universitarios: Diferencias por género, curso y tipo de titulación. Education in the Knowledge Society 12: 203–33. [Google Scholar] [CrossRef]
  4. Aldosari, Mohammad A., Aljazi H. Aljabaa, Fares S. Al-Sehaibany, and Sahar F. Albarakati. 2018. Learning style preferences of dental students at a single institution in Riyadh, Saudi Arabia, evaluated using the VARK questionnaire. Advances in Medical Education and Practice 9: 179–86. [Google Scholar] [CrossRef]
  5. ALMasaudi, Ahmad Saleem. 2021. The differences between genders in academic perseverance, motivations, and their relation to academic achievement in the University of Tabuk. Psychology and Behavioral Sciences International Journal 9: 131–46. [Google Scholar] [CrossRef]
  6. Alonso, Catalina, Domingo Gallego, and Peter Honey. 1995. Los Estilos de Aprendizaje: Procedimientos de Diagnóstico y Mejora. Bilbao: Mensajero. [Google Scholar]
  7. Archer, Louise, Jennifer DeWitt, Jonathan Osborne, Justin Dillon, Beatrice Willis, and Billy Wong. 2018. “Not girly, not sexy, not glamorous”: Primary school girls’ and parents’ constructions of science aspirations. In Pedagogical Responses to the Changing Position of Girls and Young Women. Edited by C. Jackson and J. S. Roche. Abingdon-on-Thames: Routledge, pp. 171–94. [Google Scholar]
  8. Arredondo Trapero, Florina Guadalupe, José Carlos Vázquez Parra, and Luz María Velázquez Sánchez. 2019. STEM y brecha de género en Latinoamérica. Revista de El Colegio de San Luis. Nueva época 9: 137–58. [Google Scholar] [CrossRef]
  9. Arrindell, Willem A., Alma Akkerman, Nuri Bagés, Lya Feldman, Vicente E. Caballo, Tian P. S. Oei, Barbara Torres, Gloria Canalda, Josefina Castro, Iain M. Montgomery, and et al. 2005. The short-EMBU in Australia, Spain, and Venezuela. European Journal of Psychological Assessment 21: 56–66. [Google Scholar] [CrossRef]
  10. Arrindell, Willem A., Hjördis Perris, Mercedes Denia, Jan Van Der Ende, Carlo Perris, Anna Kokkevi, José Ignacio Anasagasti, and Martin Eisemann. 1988. The constancy of structure of perceived parental rearing style in Greek and Spanish subjects as compared with the Dutch. International Journal of Psychology 23: 3–23. [Google Scholar] [CrossRef]
  11. Álvarez, Joaquín, José Manuel Aguilar, and José Javier Lorenzo. 2012. La ansiedad ante los exámenes en estudiantes universitarios: Relaciones con variables personales y académicas. Electronic Journal of Research in Educational Psychology 10: 333–54. [Google Scholar] [CrossRef]
  12. Baader, Tomas, Carmen Rojas, José Luis Molina, Marcelo Gotelli, Catalina Alamo, Carlos Fierro, Silvia Venezian, and Paula Dittus. 2014. Diagnóstico de la prevalencia de trastornos de la salud mental en estudiantes universitarios y los factores de riesgo emocionales asociados. Revista Chilena de Neuro-Psiquiatría 52: 167–76. [Google Scholar] [CrossRef]
  13. Batool, Syeda S. 2019. Academic achievement: Interplay of positive parenting, self-esteem, and academic procrastination. Australian Journal of Psychology 72: 1–14. [Google Scholar] [CrossRef]
  14. Bello, Alessandro. 2020. Las Mujeres en Ciencias, Tecnología, Ingeniería Y Matemáticas en América Latina y el Caribe. New York City: ONU Mujeres. [Google Scholar]
  15. Beltrán, Jesús. 1993. Procesos, Estrategias y Técnicas de Aprendizaje. Santa Cruz: Síntesis. [Google Scholar]
  16. Berlin, M., B. Modin, P. A. Gustafsson, A. Hjern, and M. Bergström. 2012. The Impact of School on Children’s and Adolescents’ Mental Health. Stockholm: The Swedish National Board of Health and Welfare and Center for Health Equity Studies (CHESS). [Google Scholar]
  17. Blackmore, Conner, Julian Vitali, Louise Ainscough, Tracey Langfield, and Kay Colthorpe. 2021. A review of self-regulated learning and self-efficacy: The key to tertiary transition in science, technology, engineering, and mathematics (STEM). International Journal of Higher Education 10: 169–77. [Google Scholar] [CrossRef]
  18. Boson, Karin, Peter Wennberg, Claudia Fahlke, and Kristina Berglund. 2019. Personality traits as predictors of early alcohol inebriation among young adolescents: Mediating effects by mental health and gender-specific patterns. Addictive Behaviors 95: 152–59. [Google Scholar] [CrossRef] [PubMed]
  19. Brackett, Marc A., Susan E. Rivers, Sara Shiffman, Nicole Lerner, and Peter Salovey. 2006. Relating emotional abilities to social functioning: A comparison of self-report and performance measures of emotional intelligence. Journal of Personality and Social Psychology 91: 780–95. [Google Scholar] [CrossRef] [PubMed]
  20. Bully, Paola, Joana Jaureguizar, Elena Bernaras, and Iratxe Redondo. 2019. Relationship between parental socialization, emotional symptoms, and academic performance during adolescence: The influence of parents’ and teenagers’ gender. International Journal of Environmental Research and Public Health 16: 2231. [Google Scholar] [CrossRef]
  21. Caballero, Carmen Cecilia, Raymundo Abello, and Jorge Palacios. 2007. Relación del burnout y el rendimiento académico con la satisfacción frente a los estudios en estudiantes universitarios. Avances en Psicología Latinoamericana 25: 98–111. [Google Scholar]
  22. Caballo, Vicente E., Isabel C. Salazar, Benito Arias, María Jesús Irurtia, and Marta Calderero. 2010. Validación del “Cuestionario de ansiedad social para adultos” (CASO-A30) en universitarios españoles: Similitudes y diferencias entre carreras universitarias y comunidades autónomas. Behavioral Psychology 18: 5–34. [Google Scholar]
  23. Camarero Suárez, Francisco José, Francisco de Asís Martín del Buey, and Francisco Javier Herrero Díez. 2000. Estilos y estrategias de aprendizaje en estudiantes universitarios. Psicothema 12: 615–22. [Google Scholar]
  24. Carreño, María Jacqueline Sepúlveda, Mariela López Quiero, Pablo Torres Vergara, and Javiana Luengo Contreras. 2011. Diferencias de género en el rendimiento académico y en el perfil de estilos y de estrategias de aprendizaje en estudiantes de química y farmacia de la Universidad de Concepción. Revista Estilos de Aprendizaje 7: 135–50. [Google Scholar] [CrossRef]
  25. Casad, Bettina J., Patricia Hale, and Faye L. Wachs. 2017. Stereotype threat among girls: Differences by gender identity and math education context. Psychology of Women Quarterly 41: 513–29. [Google Scholar] [CrossRef]
  26. Castillo Riquelme, Víctor, Nicolás Cabezas Maureira, Constanza Vera Navarro, and Constanza Toledo Puente. 2021. Ansiedad al aprendizaje en línea: Relación con actitud, género, entorno y salud mental en universitarios. Revista Digital de Investigación en Docencia Universitaria 15: e1284. [Google Scholar] [CrossRef]
  27. Chaplin, Tara M., and Amelia Aldao. 2013. Gender differences in emotion expression in children: A meta-analytic review. Psychological Bulletin 139: 735–65. [Google Scholar] [CrossRef] [PubMed]
  28. Cheema, Jehanzeb R., and Anastasia Kitsantas. 2014. Influences of disciplinary classroom climate on high school student self-efficacy and mathematics achievement: A look at gender and racial–ethnic differences. International Journal of Science and Mathematics Education 12: 1261–79. [Google Scholar] [CrossRef]
  29. Cheng, Wen, and Chia-Chun Wu. 2021. Family socioeconomic status and children’s gender differences in Taiwanese teenagers’ perception of parental rearing behaviors. Journal of Child and Family Studies 30: 1619–32. [Google Scholar] [CrossRef]
  30. Cheryan, Sapna, Sianna A. Ziegler, Amanda K. Montoya, and Lily Jiang. 2017. Why are some STEM fields more gender balanced than others? Psychological Bulletin 143: 1–35. [Google Scholar] [CrossRef]
  31. Clark, David M. 1986. A cognitive approach to panic. Behaviour Research and Therapy 24: 461–70. [Google Scholar] [CrossRef]
  32. Colorado, Yuly Suárez, and Carlos Wilches Bisval. 2015. Habilidades emocionales en una muestra de estudiantes universitarios: Las diferencias de género. Educación y Humanismo 17: 119–32. [Google Scholar] [CrossRef]
  33. Cordero, Andrea Estefanía Rossi, and Mario Barajas Frutos. 2015. Elección de estudios CTIM y desequilibrios de género. Enseñanza de las Ciencias 33: 59–76. [Google Scholar] [CrossRef]
  34. Cvencek, Dario, Ružica Brečić, Dora Gacesa, and Andrew N. Meltzoff. 2021. Development of math attitudes and math self-concepts: Gender differences, implicit-explicit dissociations, and relations to math achievement. Child Development 92: e940–e956. [Google Scholar] [CrossRef]
  35. Del Buey, Francisco Martín, and Francisco Camarero Suárez. 2001. Diferencias de género en los procesos de aprendizaje universitarios. Psicothema 13: 598–604. [Google Scholar]
  36. Dell’Osso, Liliana, Marianna Abelli, Stefano Pini, Barbara Carpita, Marina Carlini, Francesco Mengali, Rosalba Tognetti, Francesco Rivetti, and Gabriele Massimetti. 2015. The influence of gender on social anxiety spectrum symptoms in a sample of university students. Rivista di Psichiatria 50: 295–301. [Google Scholar]
  37. Desy, Elizabeth A., Scott A. Peterson, and Vicky Brockman. 2011. Gender differences in science-related attitudes and interests among middle school and high school students. Science Educator 20: 23–30. [Google Scholar]
  38. Domes, Gregor, Alexander Lischke, Christoph Berger, Annette Grossmann, Karlheinz Hauenstein, Markus Heinrichs, and Sabine C. Herpertz. 2010. Effects of intranasal oxytocin on emotional face processing in women. Psychoneuroendocrinology 35: 83–93. [Google Scholar] [CrossRef] [PubMed]
  39. Doná, Stella Maris, María Susana Lopetegui, Lilia Elba Rossi Casé, and Rosa Haydée Neer. 2010. Estrategias de aprendizaje y rendimiento académico según el género en estudiantes universitarios. Revista de Psicología 11: 199–211. [Google Scholar]
  40. Duarte, Oswaldo Soria. 2019. Contexto familiar y los factores intervinientes en el rendimiento académico del sujeto educativo: Aproximación diagnóstica. Polo del Conocimiento 4: 23–38. [Google Scholar] [CrossRef]
  41. Eisenberg, Daniel, Justin Hunt, and Nicole Speer. 2013. Mental health in American colleges and universities: Variation across student subgroups and across campuses. The Journal of Nervous and Mental Disease 201: 60–67. [Google Scholar] [CrossRef] [PubMed]
  42. Entwistle, Noel, Maureen Hanley, and Dai Hounsell. 1979. Identifying distinctive approaches to studying. Higher Education 8: 365–80. [Google Scholar] [CrossRef]
  43. Essau, Cecilia A., Judith Conradt, and Franz Petermann. 2000. Frequency, comorbidity, and psychosocial impairment of anxiety disorders in adolescents. Journal of Anxiety Disorders 14: 263–79. [Google Scholar] [CrossRef]
  44. Fernanda-Molina, María, María Julia Raimundi, and Lucía Bugallo. 2017. La percepción de los estilos de crianza y su relación con las autopercepciones de los niños de Buenos Aires: Diferencias en función del género. Universitas Psychologica 16: 1–12. [Google Scholar]
  45. Fernández-Castillo, A. 2009. Ansiedad durante pruebas de evaluación académica: Influencia de la cantidad de sueño y la agresividad. Salud Mental 32: 479–86. [Google Scholar]
  46. Fortin, Nicole M., Philip Oreopoulos, and Shelley Phipps. 2015. Leaving boys behind: Gender disparities in high academic achievement. Journal of Human Resources 50: 549–79. [Google Scholar] [CrossRef]
  47. Francis, Perry C., and Aaron S. Horn. 2017. Mental health issues and counseling services in US higher education: An overview of recent research and recommended practices. Higher Education Policy 30: 263–77. [Google Scholar] [CrossRef]
  48. Fuente, J., F. Justicia, I. Arcilla, and A. Soto. 1994. Factores Condicionantes de las Estrategias de Aprendizaje y del Rendimiento Académico en Alumnos Universitarios, a Través del ACRA. Investigación del Dpto. de Psicología Evolutivo y de la Educación. La Cañada de San Urbano: Facultad de Humanidades y Ciencias de la Educación, Universidad de Almería. [Google Scholar]
  49. Fuentes, María C., Antonio Alarcón, Enrique Gracia, and Fernando García. 2015. School adjustment among Spanish adolescents: Influence of parental socialization. Cultura y Educación: Revista de Teoría, Investigación y Práctica 27: 1–32. [Google Scholar] [CrossRef]
  50. Gaibor-González, Ismael, and Rodrigo Moreta-Herrera. 2020. Optimismo disposicional, ansiedad, depresión y estrés en una muestra del Ecuador: Análisis intergénero y de predicción. Actualidades en Psicología 34: 17–31. [Google Scholar] [CrossRef]
  51. Gandarillas, Miguel Ángel. 1995. Culture and Physiology: The Role of Rearing Practices on Physiological Activity: A Multidisciplinary Study. Ph.D. dissertation, Universidad Autónoma de Madrid, Madrid, Spain. Available online: http://hdl.handle.net/10486/13160 (accessed on 20 November 2024).
  52. Gandarillas, Miguel Ángel. 2022. An Integrated Approach to Learning Diversity and Its Psychosocial Predictors in the University Context. Preliminary Study. In Teaching Innovation and Educational Practices for Quality Education. Edited by C. Romero. Madrid: Dykinson, pp. 837–63. [Google Scholar]
  53. Gandarillas, Miguel Ángel, María Natividad Elvira-Zorzo, and M. Rodríguez-Vera. 2024a. The Impact of Parenting Practices and Family Economy on Psychological Wellbeing and Learning Patterns in Higher Education Students. Psicologia Reflexão e Crítica 37: 8. [Google Scholar] [CrossRef]
  54. Gandarillas, Miguel Ángel, María Natividad Elvira-Zorzo, Gabriela Alicia Pica-Miranda, and Bernardita Correa-Concha. 2024b. The Impact of Family Factors and Digital Technologies on Mental Health in University Students. Frontiers in Psychology 15: 1433725. [Google Scholar] [CrossRef] [PubMed]
  55. Gao, Qingsong, and Yongxia Wei. 2023. Understanding the cultivation mechanism for mental health education of college students in campus culture construction from the perspective of deep learning. Current Psychology 43: 1715–32. [Google Scholar] [CrossRef]
  56. Garber, Lawrence L., Eva M. Hyatt, and Ünal Ö. Boya. 2017. Gender differences in learning preferences among participants of serious business games. International Journal of Management Education 15: 11–29. [Google Scholar] [CrossRef]
  57. García, Francisco Cano. 2000. Diferencias de género en estrategias y estilos de aprendizaje. Psicothema 12: 360–67. [Google Scholar]
  58. García-Holgado, Alicia, Amparo Camacho Díaz, and Francisco J. García-Peñalvo. 2019. La brecha de género en el sector STEM en América Latina: Una propuesta europea. Paper presented at CINAIC: V Congreso Internacional sobre Aprendizaje, Innovación y Competitividad, Madrid, Spain, October 9–11; pp. 704–9. [Google Scholar] [CrossRef]
  59. Garnefski, Nadia, Jan Teerds, Vivian Kraaij, Jeroen Legerstee, and Tessa van Den Kommer. 2004. Cognitive emotion regulation strategies and depressive symptoms: Differences between males and females. Personality and Individual Differences 36: 267–76. [Google Scholar] [CrossRef]
  60. Gfellner, Barbara M., and Ana I. Córdoba. 2020. The interface of identity distress and psychological problems in students’ adjustment to university. Scandinavian Journal of Psychology 61: 527–34. [Google Scholar] [CrossRef]
  61. Gili, Margalida, Javier García Campayo, and Miquel Roca. 2014. Crisis económica y salud mental. Informe SESPAS 2014. Gaceta Sanitaria 28: 104–8. [Google Scholar] [CrossRef] [PubMed]
  62. González Cabanach, Ramón, Francisca Fariña, Carlos Freire Rodríguez, Patricia González Berruga, and María del Mar Ferradás. 2013. Diferencias en el afrontamiento del estrés en estudiantes universitarios hombres y mujeres. European Journal of Education and Psychology 6: 19–32. [Google Scholar] [CrossRef]
  63. Gordon, Mellissa S., and Ming Cui. 2012. The effect of school-specific parenting processes on academic achievement in adolescence and young adulthood. Family Relations 61: 728–41. [Google Scholar] [CrossRef]
  64. Gómez-Ortiz, Olga, Rosario Del Rey, Eva M. Romera, and Rosario Ortega-Ruiz. 2015. Los estilos educativos paternos y maternos en la adolescencia y su relación con la resiliencia, el apego y la implicación en acoso escolar. Anales de Psicología 31: 979–89. [Google Scholar] [CrossRef]
  65. Graves, B. Sue, Michael E. Hall, Carolyn Dias-Karch, Michael H. Haischer, and Christine Apter. 2021. Gender differences in perceived stress and coping among college students. PLoS ONE 16: e0255634. [Google Scholar] [CrossRef]
  66. Gunderson, Elizabeth A., Gerardo Ramirez, Susan C. Levine, and Sian L. Beilock. 2012. The role of parents and teachers in the development of gender-related math attitudes. Sex Roles 66: 153–66. [Google Scholar] [CrossRef]
  67. Guterman, Oz, and Ari Neuman. 2018. Personality, socio-economic status, and education: Factors that contribute to the degree of structure in homeschooling. Social Psychology of Education 21: 75–90. [Google Scholar] [CrossRef]
  68. Henderlong Corpus, Jennifer, and Mark R. Lepper. 2007. The effects of person versus performance praise on children’s motivation: Gender and age as moderating factors. Educational Psychology 27: 487–508. [Google Scholar] [CrossRef]
  69. Hernesniemi, Elina, Hannu Räty, Kati Kasanen, Xuejiao Cheng, Jianzhong Hong, and Matti Kuittinen. 2017. Burnout among Finnish and Chinese university students. Scandinavian Journal of Psychology 58: 400–8. [Google Scholar] [CrossRef]
  70. Herrera, Lucía, Mohamed Al-Lal, and Laila Mohamed. 2020. Academic performance, self-concept, personality, and emotional intelligence in primary education: Analysis by gender and cultural group. Frontiers in Psychology 10: 3075. [Google Scholar] [CrossRef]
  71. Herrera, Lucía, Rafael E. Buitrago, and Sergio Cepero. 2017. Emotional intelligence in Colombian primary school children: Location and gender analysis. Universitas Psychologica 16: 1–10. [Google Scholar] [CrossRef]
  72. Hou, Yongmei, and Jiaying Luo. 2022. Psychometric evaluation of EMBU for junior high school students in Guangdong Province. Psychiatry 5: 13–19. [Google Scholar]
  73. Hu, Xiaowen, and Gillian B. Yeo. 2020. Emotional exhaustion and reduced self-efficacy: The mediating role of deep and surface learning strategies. Motivation and Emotion 44: 785–95. [Google Scholar] [CrossRef]
  74. Huang, Chiungjung. 2013. Gender differences in academic self-efficacy: A meta-analysis. European Journal of Psychology of Education 28: 1–35. [Google Scholar] [CrossRef]
  75. Inzunza Melo, Bárbara Cecilia, Carolina Márquez Urrizola, and Cristhian Pérez Villalobos. 2020. Relación entre aprendizaje autorregulado, antecedentes académicos y características sociodemográficas en estudiantes de medicina. Educación Médica Superior 34: 1–11. [Google Scholar]
  76. Jaureguizar, Joana, Elena Bernaras, Paola Bully, and Maite Garaigordobil. 2018. Perceived parenting and adolescents’ adjustment. Psicologia: Reflexão e Crítica 31: 8. [Google Scholar] [CrossRef] [PubMed]
  77. Kelly, Megan M., Audrey R. Tyrka, George M. Anderson, Lawrence H. Price, and Linda L. Carpenter. 2008. Sex differences in emotional and physiological responses to the Trier Social Stress Test. Journal of Behavioral Therapy and Experimental Psychiatry 39: 87–98. [Google Scholar] [CrossRef]
  78. Kessler, Ronald C. 2003. Epidemiology of women and depression. Journal of Affective Disorders 74: 5–13. [Google Scholar] [CrossRef] [PubMed]
  79. Kessler, Ronald C., Kathleen R. Merikangas, and Philip S. Wang. 2007. Prevalence, comorbidity, and service utilization for mood disorders in the United States at the beginning of the twenty-first century. Annual Review of Clinical Psychology 3: 137–58. [Google Scholar] [CrossRef] [PubMed]
  80. Ketchen Lipson, Sarah, S. Michael Gaddis, Justin Heinze, Kathryn Beck, and Daniel Eisenberg. 2015. Variations in student mental health and treatment utilization across U.S. colleges and universities. Journal of American College Health 63: 388–96. [Google Scholar] [CrossRef] [PubMed]
  81. Khodarahimi, Siamak, and Rayhan Fathi. 2016. Mental health, coping styles, and risk-taking behaviors in young adults. Journal of Forensic Psychology Practice 16: 287–303. [Google Scholar] [CrossRef]
  82. Kim, Yeeun, Sog Yee Mok, and Tina Seidel. 2020. Parental influences on immigrant students’ achievement-related motivation and achievement: A meta-analysis. Educational Research Review 30: 1–19. [Google Scholar] [CrossRef]
  83. Kolb, David A. 1984. Experiential Learning. Englewood Cliffs: Prentice Hall. [Google Scholar]
  84. Latas, Milan, Marina Pantić, and Danilo Obradović. 2010. Analysis of test anxiety in medical students. Medicinski Pregled 63: 863–66. [Google Scholar] [CrossRef] [PubMed]
  85. Lattie, Emily G., Elizabeth C. Adkins, Nathan Winquist, Colleen Stiles-Shields, Q. Eileen Wafford, and Andrea K. Graham. 2018. Digital mental health interventions for depression, anxiety, and enhancement of psychological well-being among college students: Systematic review. Journal of Medical Internet Research 21: e12869. [Google Scholar] [CrossRef]
  86. León, Jaime, Juan L. Núñez, and Jeffrey Liew. 2015. Self-determination and STEM education: Effects of autonomy, motivation, and self-regulated learning on high school math achievement. Learning and Individual Differences 43: 156–63. [Google Scholar] [CrossRef]
  87. Leppink, Eric W., Brian L. Odlaug, Katherine Lust, Gary Christenson, and Jon E. Grant. 2016. The young and the stressed: Stress, impulse control, and health in college students. The Journal of Nervous and Mental Disease 204: 931–38. [Google Scholar] [CrossRef] [PubMed]
  88. Leupold, Christopher R., Erika C. Lopina, and Julianne Erickson. 2020. Examining the effects of core self-evaluations and perceived organizational support on academic burnout among undergraduate students. Psychological Reports 123: 1260–81. [Google Scholar] [CrossRef] [PubMed]
  89. Lietaert, Sofie, Debora Roorda, Ferre Laevers, Karine Verschueren, and Bieke De Fraine. 2015. The gender gap in student engagement: The role of teachers’ autonomy support, structure, and involvement. British Journal of Educational Psychology 85: 498–518. [Google Scholar] [CrossRef]
  90. Lipson, Sarah Ketchen, Emily G. Lattie, and Daniel Eisenberg. 2019. Increased rates of mental health service utilization by U.S. college students: 10-year population-level trends (2007–2017). Psychiatric Services 70: 60–63. [Google Scholar] [CrossRef] [PubMed]
  91. Lopez, Luis-Joaquin Garcia, Candido J. Ingles, and Jose M. Garcia-Fernandez. 2008. Exploring the relevance of gender and age differences in the assessment of social fears in adolescence. Social Behavior and Personality 36: 385–90. [Google Scholar] [CrossRef]
  92. Lugo, Carlos Saul Juarez, Gabriela Rodriguez Hernandez, and Elba Luna Montijo. 2012. El cuestionario de estilos de aprendizaje CHAEA y la escala de estrategias de aprendizaje ACRA como herramienta potencial para la tutoría académica. Revista de Estilos de Aprendizaje 10: 148–71. [Google Scholar]
  93. Lund, Emily M., and Scott W. Ross. 2017. Bullying perpetration, victimization, and demographic differences in college students: A review of the literature. Trauma, Violence, & Abuse 18: 348–60. [Google Scholar]
  94. MacCann, Carolyn, Yixin Jiang, Luke E. R. Brown, Kit S. Double, Micaela Bucich, and Amirali Minbashian. 2020. Emotional intelligence predicts academic performance: A meta-analysis. Psychological Bulletin 146: 150–86. [Google Scholar] [CrossRef] [PubMed]
  95. Mammadov, Sakhavat. 2022. Big Five personality traits and academic performance: A meta-analysis. Journal of Personality 90: 222–55. [Google Scholar] [CrossRef]
  96. Mardia, Kanti V. 1970. Measures of Multivariate Skewness and Kurtosis with Applications. Biometrika 57: 519–30. [Google Scholar] [CrossRef]
  97. Martineli, Ana Karina Braguim, Fernanda Aguiar Pizeta, and Sonia Regina Loureiro. 2018. Behavioral problems of school children: Impact of social vulnerability, chronic adversity, and maternal depression. Psicologia: Reflexão e Crítica 31: 11. [Google Scholar] [CrossRef] [PubMed]
  98. Martínez, Isabel M., Isabella Meneghel, and Jonathan Peñalver. 2019. ¿El género afecta en las estrategias de afrontamiento para mejorar el bienestar y el desempeño académico? Revista de Psicodidáctica 24: 111–19. [Google Scholar] [CrossRef]
  99. Masud, Sarwat, Syed Hamza Mufarrih, Nada Qaisar Qureshi, Fahad Khan, Saad Khan, and Muhammad Naseem Khan. 2019. Academic performance in adolescent students: The role of parenting style and socio-demographic factors. A cross-sectional study from Peshawar, Pakistan. Frontiers in Psychology 10: 2497. [Google Scholar] [CrossRef] [PubMed]
  100. Mathieu, Sharna L., Elizabeth G. Conlon, Allison M. Waters, and Lara J. Farrell. 2020. Perceived parental rearing in pediatric obsessive–compulsive disorder: Examining the factor structure of the EMBU child and parent versions and associations with OCD symptoms. Child Psychiatry & Human Development 51: 956–68. [Google Scholar]
  101. Mayorga-Lascano, Marlon, and Rodrigo Moreta-Herrera. 2019. Síntomas clínicos, subclínicos y necesidades de atención psicológica en estudiantes universitarios con bajo rendimiento. Revista Educación 43: 452–67. [Google Scholar] [CrossRef]
  102. Meyer, J. H. F. 1995. Gender differences in the learning behaviour of entering first-year university students. Higher Education 29: 201–15. [Google Scholar] [CrossRef]
  103. Meyerson, Shari L., Joel M. Sternbach, Joseph B. Zwischenberger, and Edward M. Bender. 2017. The effect of gender on resident autonomy in the operating room. Journal of Surgical Education 74: e111–e118. [Google Scholar] [CrossRef] [PubMed]
  104. Méndez, Francisco Xavier, Candido J. Inglés, and María D. Hidalgo. 2002. Estrés en las relaciones interpersonales: Un estudio descriptivo en la adolescencia. Ansiedad y Estrés 8: 25–36. [Google Scholar]
  105. Micin, Sonia, and Verónica Bagladi. 2011. Salud mental en estudiantes universitarios: Incidencia de psicopatología y antecedentes de conducta suicida en población que acude a un servicio de salud estudiantil. Terapia Psicológica 29: 53–64. [Google Scholar] [CrossRef]
  106. Miller, C. Dean, Jeff Finley, and Donna L. McKinley. 1990. Learning approaches and motives: Male and female differences and implications for learning assistance programs. Journal of College Student Development 31: 147–54. [Google Scholar]
  107. Montgomery, Catherine, and Juan Manuel Fernández-Cárdenas. 2018. Teaching STEM education through dialogue and transformative learning: Global significance and local interactions in Mexico and the UK. Journal of Education for Teaching 44: 2–13. [Google Scholar] [CrossRef]
  108. Moss-Racusin, Corinne A., John F. Dovidio, Victoria L. Brescoll, Mark J. Graham, and Jo Handelsman. 2012. Science faculty’s subtle gender biases favor male students. Proceedings of the National Academy of Sciences 109: 16474–479. [Google Scholar] [CrossRef] [PubMed]
  109. Nagy, Gabriel, Helen M. G. Watt, Jacquelynne S. Eccles, Ulrich Trautwein, Oliver Lüdtke, and Jürgen Baumert. 2010. The development of students’ mathematics self-concept in relation to gender: Different countries, different trajectories. Journal of Research on Adolescence 20: 482–506. [Google Scholar] [CrossRef]
  110. Naz, Sadaf, Syed Afzal Shah, and Anjum Qayum. 2020. Gender differences in motivation and academic achievement: A study of the university students of KP, Pakistan. Global Regional Review 5: 67–75. [Google Scholar] [CrossRef]
  111. Nerín, Natalia Franco, Miguel Ángel Pérez Nieto, and María José de Dios Pérez. 2014. Relación entre los estilos de crianza parental y el desarrollo de ansiedad y conductas disruptivas en niños de 3 a 6 años. Revista de Psicología Clínica con Niños y Adolescentes 1: 149–56. [Google Scholar]
  112. Nolen-Hoeksema, Susan. 2012. Emotion regulation and psychopathology: The role of gender. Annual Review of Clinical Psychology 8: 161–87. [Google Scholar] [CrossRef]
  113. OECD. 2015. The ABC of Gender Equality in Education. Paris: OECD Publishing. [Google Scholar]
  114. Osborne, Jonathan, Shirley Simon, and Sue Collins. 2003. Attitudes towards science: A review of the literature and its implications. International Journal of Science Education 25: 1049–79. [Google Scholar] [CrossRef]
  115. Pedrelli, Paola, Maren Nyer, Albert Yeung, Courtney Zulauf, and Timothy Wilens. 2015. College students: Mental health problems and treatment considerations. Academic Psychiatry 39: 503–11. [Google Scholar] [CrossRef]
  116. Perchtold, Corinna M., Ilona Papousek, Andreas Fink, Hannelore Weber, Christian Rominger, and Elisabeth M. Weiss. 2019. Gender differences in generating cognitive reappraisals for threatening situations: Reappraisal capacity shields against depressive symptoms in men, but not women. Frontiers in Psychology 10: 553. [Google Scholar] [CrossRef] [PubMed]
  117. Piccolo, Luciane da Rosa, Adriane Xavier Arteche, Rochele Paz Fonseca, Rodrigo Grassi-Oliveira, and Jerusa Fumagalli Salles. 2016. Influence of family socioeconomic status on IQ, language, memory and executive functions of Brazilian children. Psicologia: Reflexão e Crítica 29: 23. [Google Scholar] [CrossRef]
  118. Poorolajal, Jalal, Ali Ghaleiha, Nahid Darvishi, Shahla Daryaei, and Soheila Panahi. 2017. The prevalence of psychiatric distress and associated risk factors among college students using GHQ-28 questionnaire. Iranian Journal of Public Health 46: 957–63. [Google Scholar]
  119. Puskar, Kathryn R., Susan M. Sereika, and Linda L. Haller. 2003. Anxiety, somatic complaints, and depressive symptoms in rural adolescents. Journal of Child and Adolescent Psychiatric Nursing 16: 102–11. [Google Scholar] [CrossRef] [PubMed]
  120. Raabe, Isabel J., Zsófia Boda, and Christoph Stadtfeld. 2019. The social pipeline: How friend influence and peer exposure widen the STEM gender gap. Sociology of Education 92: 105–23. [Google Scholar] [CrossRef]
  121. Reinking, Anni, and Barbara Martin. 2018. The gender gap in STEM fields: Theories, movements, and ideas to engage girls in STEM. Journal of New Approaches in Educational Research 7: 148–53. [Google Scholar] [CrossRef]
  122. Richardson, John T. E., and Estelle King. 1991. Gender differences in the experience of higher education: Qualitative and quantitative approaches. Educational Psychology 11: 247–57. [Google Scholar] [CrossRef]
  123. Rivera, María del Carmen Aguilar. 2010. Estilos y estrategias de aprendizaje en jóvenes ingresantes a la universidad. Revista de Psicología 28: 208–22. [Google Scholar] [CrossRef]
  124. Rodrigo, Mercedes Romero, Ángel San Martín Alonso, and José Peirats Chacón. 2018. Diferencias de sexo en estrategias de aprendizaje de estudiantes online. Innoeduca: International Journal of Technology and Educational Innovation 4: 114–26. [Google Scholar] [CrossRef]
  125. Rodríguez-Hernández, Carlos Felipe, Eduardo Cascallar, and Eva Kyndt. 2020. Socio-economic status and academic performance in higher education: A systematic review. Educational Research Review 29: 100305. [Google Scholar] [CrossRef]
  126. Romero, Isabel María Vázquez, and Ángeles Blanco Blanco. 2018. Factores sociocognitivos asociados a la elección de estudios científico-matemáticos. Un análisis diferencial por sexo y curso en la Educación Secundaria. Revista de Investigación Educativa 37: 269–86. [Google Scholar] [CrossRef]
  127. Said, David, Kypros Kypri, and Jenny Bowman. 2013. Risk factors for mental disorder among university students in Australia: Findings from a web-based cross-sectional survey. Social Psychiatry and Psychiatric Epidemiology 48: 935–944. [Google Scholar] [CrossRef]
  128. Saleh, Dalia, Nathalie Camart, and Lucia Romo. 2017. Predictors of stress in college students. Frontiers in Psychology 8: 19. [Google Scholar] [CrossRef]
  129. San Gregorio, María Ángeles Pérez, Agustín Martín Rodríguez, Mercedes Borda Mas, and Carmen del Río Sánchez. 2003. Estrés y rendimiento académico en estudiantes universitarios. Cuadernos de Medicina Psicosomática y Psiquiatría de Enlace 67/68: 26–33. [Google Scholar]
  130. Saura, Cándido J. Inglés, María Soledad Torregrosa Díez, José Manuel García Fernández, Mari Carmen Martínez Monteagudo, Estefanía Estévez López, and Beatriz Delgado Domenech. 2014. Conducta agresiva e inteligencia emocional en la adolescencia [Aggressive behavior and emotional intelligence in adolescence]. European Journal of Education and Psychology 7: 29–41. [Google Scholar] [CrossRef]
  131. Sáinz, Milagros. 2020. Brechas y Sesgos de Género en la Elección de Estudios STEM: ¿Por qué Ocurren y Cómo Actuar para Eliminarlas? Seville: Centro de Estudios Andaluces. [Google Scholar]
  132. Severiens, Sabine E., and Geert T. M. Ten Dam. 1994. Gender differences in learning styles: A narrative review and quantitative meta-analysis. Higher Education 27: 487–501. [Google Scholar] [CrossRef]
  133. Shan, Yi, Meng Ji, Wenxiu Xie, Rongying Li, Xiaobo Qian, Xiaomin Zhang, and Tianyong Hao. 2022. Interventions in Chinese undergraduate students’ mental health: Systematic review. Interactive Journal of Medical Research 11: e38249. [Google Scholar] [CrossRef]
  134. Silva-Laya, Marisol, Natalia D’Angelo, Elda García, Laura Zúñiga, and Teresa Fernández. 2020. Urban poverty and education: A systematic literature review. Educational Research Review 29: 100280. [Google Scholar] [CrossRef]
  135. Spinath, Birgit, Christine Eckert, and Ricarda Steinmayr. 2014. Gender differences in school success: What are the roles of students’ intelligence, personality, and motivation? Educational Research 56: 230–43. [Google Scholar] [CrossRef]
  136. Steel, Zachary, Claire Marnane, Changiz Iranpour, Tien Chey, John W. Jackson, Vikram Patel, and Derrick Silove. 2014. The global prevalence of common mental disorders: A systematic review and meta-analysis 1980–2013. International Journal of Epidemiology 43: 476–93. [Google Scholar] [CrossRef]
  137. Steinhausen, Hans-Christoph, Nora Müller, and Christa Winkler Metzke. 2008. Frequency, stability and differentiation of self-reported school fear and truancy in a community sample. Child and Adolescent Psychiatry and Mental Health 2: 17. [Google Scholar] [CrossRef]
  138. Steinmayr, Ricarda, and Ursula Kessels. 2017. Good at school = successful on the job? Explaining gender differences in scholastic and vocational success. Personality and Individual Differences 105: 107–15. [Google Scholar] [CrossRef]
  139. Stevens, Jennifer S., and Stephan Hamann. 2012. Sex differences in brain activation to emotional stimuli: A meta-analysis of neuroimaging studies. Neuropsychologia 50: 1578–593. [Google Scholar] [CrossRef]
  140. Susperreguy, Maria Ines, Pamela E. Davis-Kean, Kathryn Duckworth, and Meichu Chen. 2018. Self-concept predicts academic achievement across levels of the achievement distribution: Domain specificity for math and reading. Child Development 89: 2196–214. [Google Scholar] [CrossRef]
  141. Taber, Keith T. 2018. The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Research in Science Education 48: 1273–296. [Google Scholar] [CrossRef]
  142. TIMSS. 2015. Trends in International Mathematics and Science Study. Available online: https://bit.ly/3nuTsJP (accessed on 22 January 2025).
  143. Tran, Antoine, Laurie Tran, Nicolas Geghre, David Darmon, Marion Rampal, Diane Brandone, Jean-Michel Gozzo. Hervé Haas, Karine Rebouillat-Savy, Hervé Caci, and Paul Avillach. 2017. Health assessment of French university students and risk factors associated with mental health disorders. PLoS ONE 12: e0188187. [Google Scholar] [CrossRef]
  144. UNESCO. 2019. Descifrar el Código: La Educación de las niñas y las Mujeres en Ciencias, Tecnología, Ingeniería y Matemáticas (STEM). Paris: UNESCO. [Google Scholar]
  145. Vera-Villarroel, Pablo, Karem Celis-Atenas, Alfonso Urzúa, Jaime Silva, Daniela Contreras, and Sebastián Lillo. 2016. Los afectos como mediadores de la relación optimismo y bienestar. Revista Argentina de Clínica Psicológica 25: 195–202. [Google Scholar]
  146. Voyer, Daniel, and Susan D. Voyer. 2014. Gender differences in scholastic achievement: A meta-analysis. Psychological Bulletin 140: 1174–204. [Google Scholar] [CrossRef]
  147. Walsh, Bridget A., Sarah Mitchell, Ruby Batz, Angela Lee, Matthew Aguirre, Julie Lucero, Adrienne Edwards, Keira Hambrick, and David W. Zeh. 2023. Familial roles and support of doctoral students. Family Relations 72: 2444–64. [Google Scholar] [CrossRef]
  148. Wang, Ming-Te, and Jessica L. Degol. 2016. Gender gap in science, technology, engineering, and mathematics (STEM): Current knowledge, implications for practice, policy, and future directions. Educational Psychology Review 29: 119–40. [Google Scholar] [CrossRef]
  149. Whittle, Sarah, Murat Yücel, Marie B. H. Yap, and Nicholas B. Allen. 2011. Sex differences in the neural correlates of emotion: Evidence from neuroimaging. Biological Psychology 87: 319–33. [Google Scholar] [CrossRef]
  150. Witkin, Herman A., and Donald R. Goodenough. 1981. Cognitive Styles: Essence and Origins. New York: International University Press. [Google Scholar]
  151. World Health Organization. 2001. Fortaleciendo la Prevención de Salud. Geneva: World Health Organization. [Google Scholar]
  152. World Health Organization. 2014. Invertir en Salud Mental. Geneva: World Health Organization. [Google Scholar]
  153. World Health Organization. 2023. Gender and Health. Available online: https://www.who.int/health-topics/gender#tab=tab_1 (accessed on 15 January 2023).
  154. World Medical Association. 2013. World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA 310: 2191–194. [Google Scholar] [CrossRef]
  155. Worrell, Frank C. 2014. Theories school psychologists should know: Culture and academic achievement. Psychology in the Schools 51: 332–47. [Google Scholar] [CrossRef]
  156. Worsley, Joanne Deborah, Andy Pennington, and Rhiannon Corcoran. 2022. Supporting mental health and wellbeing of university and college students: A systematic review of review-level evidence of interventions. PLoS ONE 17: e0266725. [Google Scholar] [CrossRef]
  157. Yu, Zhonggen. 2019. Gender differences in cognitive loads, attitudes, and academic achievements in mobile English learning. International Journal of Distance Education Technologies 17: 21–35. [Google Scholar] [CrossRef]
  158. Zimmermann, Peter, and Alexandra Iwanski. 2014. Emotion regulation from early adolescence to emerging adulthood and middle adulthood: Age differences, gender differences, and emotion-specific developmental variations. International Journal of Behavioral Development 38: 182–94. [Google Scholar] [CrossRef]
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Scheme 1. Frequencies of mental health indicators in learning reported by male and female students (STEM and non-STEM students).
Scheme 1. Frequencies of mental health indicators in learning reported by male and female students (STEM and non-STEM students).
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Figure 1. Paternal and maternal parenting practices are perceived retrospectively by male and female students (means in the 1–4 range) (STEM and non-STEM students).
Figure 1. Paternal and maternal parenting practices are perceived retrospectively by male and female students (means in the 1–4 range) (STEM and non-STEM students).
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Figure 2. Percentage of sample in each type of study field.
Figure 2. Percentage of sample in each type of study field.
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Figure 3. Graphic representations of significant differences in learning dimensions between male and female students in STEM vs. non-STEM academic subjects. Note: Significance was tested using a cutoff value of p < 0.05 for all comparisons.
Figure 3. Graphic representations of significant differences in learning dimensions between male and female students in STEM vs. non-STEM academic subjects. Note: Significance was tested using a cutoff value of p < 0.05 for all comparisons.
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Table 1. Descriptives and correlations between variables in the study.
Table 1. Descriptives and correlations between variables in the study.
1. Coping with Difficulties2. Effort3. Autonomy4. Understanding/Career Interest5. Social Context6. Bad Mood/Irritability7. Anxiety8. Apathy/Demotivation9. Lack of Attention10. Poor Achievement Expectations11. Academic performance12. Care Father13. Care Mother14. Control Father15. Control Mother16. Protection Father17. Protection Mother18. Educational Level—Father19. Educational Level—Mother20. Family Economic LevelsΩMSDAsymmetryKurtosis
1--0.36 **0.05 **0.24 **−0.06 **−0.67 **−0.62 **−0.71 **−0.65 **−0.68 **0.18 **0.11 **0.21 **−0.21 **−0.23 **−0.15 **−0.11 **0.010.040.11 **0.792.640.061−0.23−0.50
2 −−0.26 **0.30 **−0.09 **−0.15 **−0.12 **−0.33 **−0.43 **−0.26 **0.25 **0.10 **0.09 **−0.08 **−0.12 **−0.02−0.04−0.06 **−0.06 **0.020.722.440.61−0.01−0.58
3 −−0.43 **0.06 **−0.02−0.06−0.11 **−0.05 **−0.07 **0.18 **−0.03−0.06 **0.06 **0.09 **0.09 **0.08 **−0.06 **−0.02−0.08 **0.722.510.660.09−0.54
4 −−0.05 *−0.11 **−0.09 **−0.25 **−0.16 **−0.25 **0.18 **0.05 **0.07 **−0.02−0.04−0.010.04−0.01−0.01−0.000.593.090.52−0.640.47
5 −−−0.020.010.000.05 *0.02−0.18 **−0.040.040.000.03−0.010.040.05 *0.09 **0.010.662.040.710.57−0.25
6 −−0.48 **0.47 **0.33 **0.35 **−0.05 *−0.04−0.11 **0.12 **0.13 **0.13 **0.08 **−0.01−0.02−0.03 2.411.040.12−1.16
7 −−0.43 **0.30 **0.36 **−0.07 **−0.01−0.10 **0.11 **0.12 **0.13 **0.10 **0.02−0.03−0.06 ** 3.041.03−0.70−0.76
8 −−0.46 **0.41 **−0.11 **−0.07 **−0.14 **0.14 **0.15 **0.18 **0.05 * 0.07 **0.02−0.05 * 2.911.05−0.50−1.01
9 −−0.37 **−0.14 **−0.06 **−0.14 **0.13 **0.14 **0.10 **0.07 **0.04 *0.02−0.02 2.651.09−0.14−1.28
10 −−−0.23 **−0.11 **−0.14 **0.16 **0.17 **0.09 **0.08 **−0.06 **−0.06 **−0.09 ** 2.261.100.33−1.21
11 −−0.07 **0.06 **0.06 **−0.05 **0.00−0.05 *00.030.020.06 ** 2.011.040.64−0.84
12 −−0.56 **−0.21 **−0.18 **−0.03−0.09 **0.30 **0.19 **0.24 ** 2.780.99−0.36−1.06
13 −−−0.19 **−0.21 **−0.13 **0.030.22 **0.32 **0.29 ** 2.571.03−0.12−1.24
14 −−0.56 **0.35 **0.24 **0.000.00−0.07 ** 1.610.921.350.68
15 −−0.26 **0.33 **−0.020.01−0.09 ** 1.620.931.330.60
16 −−0.57 **−0.02−0.04−0.05 * 2.171.070.42−1.10
17 −0.04*−0.01−0.01 2.001.010.64−0.75
18 −−0.55 **0.37 ** 2.380.70−0.68−0.74
19 −−0.40 ** 2.360.71−0.64−0.80
20 3.010.81−0.290.39
Note: M: Mean; SD: Standard Deviation; * p < 0.05; ** p < 0.01.
Table 2. Male–female differences in learning factors.
Table 2. Male–female differences in learning factors.
RangeMaleFemaleSignificance
NMSDNMSD
Effort1–46362.480.641.7332.430.61F(3.35) *
Autonomy1–46502.640.661.7572.460.65F(34.04) ***
Understanding/Career interest1–46533.120.521.7653.080.52F(3.23)
Context1–46562.020.741.7732.050.70F(0.51)
Coping with Difficulties1–46482.800.611.7582.590.60F(56.31) ***
  Bad mood/Irritability1–46582.141.001.7802.511.04F(60.78) ***
  Anxiety1–46582.611.081.7803.200.96F(170.40) ***
  Apathy/Demotivation1–46572.731.081.7812.981.03F(27.94) ***
  Lack of attention1–46592.481.081.7812.711.08F(22.21) ***
  Low achievement expectations1–46601.870.971.7812.061.06F(16.64) ***
Note: N: Sample number; M: Mean; SD: Standard Deviation; * p < 0.05; *** p < 0.001.
Table 3. Significant (p < 0.05) multiple regressions of paternal and maternal parenting characteristics predicting learning dimensions in male and female students (STEM and non-STEM students). (Only the significant predictors included in the regression equations are shown in the table).
Table 3. Significant (p < 0.05) multiple regressions of paternal and maternal parenting characteristics predicting learning dimensions in male and female students (STEM and non-STEM students). (Only the significant predictors included in the regression equations are shown in the table).
Males Coping with DifficultiesNon-Standarized CoefficientsStandarized CoefficientstSig.FR2Sig.
BBeta
 (Constant)2.86 15.110.07<0.001
 Maternal Control −0.09−0.13−2.740.00
 Maternal Care 0.080.133.210.00
 Paternal Control −0.08−0.11−2.380.02
Effort
 (Constant)2.59 48.17<0.0016.620.010.01
 Maternal Control −0.08−0.10−2.570.010
Autonomy
 (Constant)2.47 41.61<0.00110.290.010.001
 Paternal Protection 0.080.133.200.001
Learning by Understanding/Career-focused
 (Constant)3.03 64.46<0.0015.320.010.021
 Paternal Protection 0.050.092.300.021
Females Coping with DifficultiesBBetatSig.FR2Sig.
 (Constant)2.59 46.49<0.00162.340.10<0.001
 Maternal Care 0.110.187.82<0.001
 Maternal Control −0.10−0.16−6.84<0.001
 Paternal Protection −0.05−0.09−3.99<0.001
Effort
 (Constant)2.34 42.46<0.00120.670.02<0.001
 Paternal Care 0.060.104.17<0.001
 Maternal Control −0.06−0.10−3.96<0.001
Autonomy
 (Constant)2.28 56.540.00011.400.01<0.001
 Maternal Control0.050.0783.120.002
 Paternal Protection0.040.072.640.008
Learning by Understanding/Career-focused
 (Constant)2.96 87.590.00013.620.01<0.001
 Maternal Care0.050.093.69<0.001
Table 4. Male-female differences in learning dimensions in STEM and non-STEM subjects (one-way ANOVAs).
Table 4. Male-female differences in learning dimensions in STEM and non-STEM subjects (one-way ANOVAs).
STEMNMeanSDANOVA
dfFp
Coping with DifficultiesMale3582.800.60Between groups121.49<0.001
Female8482.630.57Within groups1.204
EffortMale3462.410.63Between groups10.210.650
Female8252.430.59Within groups1.169
AutonomyMale3542.480.59Between groups18.580.003
Female8432.360.63Within groups1.195
Learning by Understanding/Career-focusedMale3613.080.50Between groups10.280.594
Female8513.090.50Within groups1.210
Social ContextMale3622.090.72Between groups13.640.057
Female8532.180.71Within groups1.213
NON-STEMNMeanSDANOVA
dfFp
Coping with DifficultiesMale2292.510.58Between groups130.29<0.001
Female8042.340.55Within groups1.031
EffortMale2292.350.59Between groups110.95<0.001
Female7982.370.58Within groups0.1025
AutonomyMale2312.330.57Between groups129.45<0.001
Female8062.290.61Within groups1.035
Learning by Understanding/Career-focusedMale2292.980.49Between groups110.860.001
Female8053.010.50Within groups1.032
Social ContextMale2322.070.68Between groups1.0330.050.82
Female8092.110.66Within groups1
Note: Items with p < 0.05 are considered statistically significant based on the one-way ANOVA results.
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Elvira-Zorzo, M.N.; Gandarillas, M.Á.; Martí-González, M. Psychosocial Differences Between Female and Male Students in Learning Patterns and Mental Health-Related Indicators in STEM vs. Non-STEM Fields. Soc. Sci. 2025, 14, 71. https://doi.org/10.3390/socsci14020071

AMA Style

Elvira-Zorzo MN, Gandarillas MÁ, Martí-González M. Psychosocial Differences Between Female and Male Students in Learning Patterns and Mental Health-Related Indicators in STEM vs. Non-STEM Fields. Social Sciences. 2025; 14(2):71. https://doi.org/10.3390/socsci14020071

Chicago/Turabian Style

Elvira-Zorzo, María Natividad, Miguel Ángel Gandarillas, and Mariacarla Martí-González. 2025. "Psychosocial Differences Between Female and Male Students in Learning Patterns and Mental Health-Related Indicators in STEM vs. Non-STEM Fields" Social Sciences 14, no. 2: 71. https://doi.org/10.3390/socsci14020071

APA Style

Elvira-Zorzo, M. N., Gandarillas, M. Á., & Martí-González, M. (2025). Psychosocial Differences Between Female and Male Students in Learning Patterns and Mental Health-Related Indicators in STEM vs. Non-STEM Fields. Social Sciences, 14(2), 71. https://doi.org/10.3390/socsci14020071

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