Gender differences in STEM careers: autonomy-supportive climate, metacognitive strategies, self-efficacy, and their impact on academic performance and satisfaction

Gender differences in STEM careers: autonomy-supportive climate, metacognitive strategies, self-efficacy, and their impact on academic performance and satisfaction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gender differences in STEM careers: autonomy-supportive climate, metacognitive strategies, self-efficacy, and their impact on academic performance and satisfaction Ricardo Javier Navarro Fernández, Monica Takushi Rodriguez, Lucía Gurbillon Hirano This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7377511/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract STEM education has gained greater relevance in recent years, particularly due to the difficulties in managing the academic demands of these careers. Another significant challenge is that in a predominantly male environment like that of STEM fields, the academic experience of female students is often not the same as that of their male peers. In response to these problems, this research aims to analyze whether educational factors such as the use of metacognitive strategies and an autonomy-supportive climate influence self-efficacy and, in turn, how these factors impact student performance and satisfaction. Furthermore, it aims to assess whether the relationships within this model vary by sex using a multigroup SEM analysis. In the results, the hypothesized general SEM model demonstrated good fit indices. The model also met invariance criteria,, which allowed a multigroup structural equation model to be performed. The predictors of academic self-efficacy were found to be the autonomy-supportive climate and learning strategies for both sexes. Among the predictors of academic satisfaction, self-efficacy was found only for women, and autonomy-supportive climate was found only for men. Finally, self-efficacy and autonomy-supportive climate were found to be predictors of performance only for women. The results open valuable questions about how academic variables differentially shape the academic experience of men and women in STEM careers, with possible cultural explanations. STEM education Academic self-efficacy Autonomy-supportive climate Metacognitive strategies Gender differences in higher education Figures Figure 1 Figure 2 Figure 3 1. Introduction STEM (Science, Technology, Engineering and Mathematics, STEM) education have become increasingly important due to the growing technological and scientific innovation in recent years (Salas-Pilco, 2024; Wang et al., 2023 ). However, there are two important problems related to the demand for professionals in these careers: many of the students have difficulties in coping with the academic demands; and there is a gender gap in STEM careers, marked by a low percentage of female students. Regarding the difficulties in coping with academic demands, evidence shows that about 75% of students in Latin America and the Caribbean who participated in the last PISA test did not achieve basic competencies in mathematics, and 57% did not achieve basic competencies in science (Arias Ortiz et al., 2024 ). This situation is even more worrisome in the case of females, since the percentage with low performance in mathematics is higher than that of males (77% vs. 72%), and the same in science (58% vs. 56%) (Arias Ortiz et al., 2024 ). It is problematic that students, in general, perceive mathematics and science content as complex and difficult (Rodriguez et al., 2021; Markovits & Forgasz, 2017 ), as this can generate disinterest, rejection, or the decision not to study a career related to this field (Ahmed & Mudrey, 2019 ; Lopez et al. 2023; Luo et al., 2021 ;). This tendency is even more present among female students nearing the end of their secondary education, as several studies have found that they show significantly less interest than their male peers in pursuing STEM careers (Wang et al., 2023 ). All of this leads to the problem related to a low proportion of women in the STEM field (Shi, 2018 ). The United Nations Educational, Scientific and Cultural Organization [UNESCO] ( 2017 ) points out that women represent only 35% of students enrolled globally in these careers, a disparity that is also observed in Latin America and the Caribbean (Bello, 2020 ). Moreover, this situation is aggravated if one takes into account that the dropout rate of female students in these careers is particularly high (Wang et al., 2023 ). Quevedo and Vegas (2020) warn that this gender gap has a negative impact on society, as it increases the wage gap, reduces female participation in leadership positions and limits economic growth. In addition to all this, it is important to consider that evidence indicates that the STEM study experience is related to various educational factors, such as the use of learning strategies, the learning climate, self-efficacy, academic performance, and satisfaction. The literature points out that learning strategies are all thoughts and behaviors that students adopt intentionally to facilitate their own learning process (Hattie & Donoghue, 2016 ; Paris et al., 1983 ; Weinstein & Mayer, 1986 ; Yip, 2021 ). According to McKeachie et al. ( 1986 ), learning strategies can be classified into three categories: cognitive, metacognitive, and resource management. The present study will focus specifically only on metacognitive ones. Metacognitive strategies imply a prior planning process, where the student sets study goals, develops a learning plan, and uses strategies according to the assessed difficulty of a task (Akamatsu et al., 2019 ; McKeachie et al., 1986 ). It also involves a personal monitoring process, where the student self-evaluates their strategies, allowing them to monitor progress in any cognitive activity (McKeachie et al., 1986 ; Stanton et al., 2021 ; Weinstein and Mayer, 1986 ). Finally, behavioral regulation is also addressed, where the student makes adjustments or corrections to the study method to correctly adapt to the difficulty of the task (Akamatsu et al., 2019 ; McKeachie et al., 1986 ;). This entire process is important and has a direct relationship with training in STEM careers (Engelmann et al., 2021 ; Gamby & Bauer, 2022 ; Huvard et al., 2020 ), as it facilitates the academic experience in the classroom. In this sense, the classroom experience provided by the teacher must also be taken into account. Thus, another educational factor to consider is the autonomy-supportive climate, which can be understood as the perception of autonomy and agency offered by a teacher in the classroom (Ryan & Deci, 2017 ). It also helps nurture and develop students' internal motivational resources (Ryan & Deci, 2017 ; Núñez & León, 2015 ). Ryan and Deci ( 2017 ) mention that for individuals to feel their autonomy is being supported, they must be provided with clear explanations about the usefulness of the activity to be carried out. Likewise, Núñez and León ( 2015 ) mention that meaningful options should be offered that encourage decision-making and reinforce internal motivational resources (curiosity, enjoyment, interest, or sense of challenge). Another educational factor to consider is academic self-efficacy. Self-efficacy is a key concept in social cognitive theory. It refers to an individual's belief in their ability to perform the behaviors necessary to produce specific outcomes (Bandura, 2019). This perception significantly influences how people think, motivate themselves, and act (Schunk & DiBenedetto, 2020). Metacognitive strategies, especially those related to planning, have been found to improve academic self-efficacy (Akamatsu et al., 2019 ). Likewise, the perception of an autonomy-supportive climate is associated with higher self-efficacy (Gutiérrez & Tomás, 2019 ; Ito et al., 2024 ; Kingsford-Smith et al., 2024 ; Zhao & Qin, 2021 ). All of these variables are relevant because they are related to students' academic performance (Cockrell, 2016 ; Lever et al., 2016 ). Generally, grades are approached as a measure of academic performance, since they allow the evaluation of the degree to which a student has acquired knowledge, skills, and competencies in relation to the established educational objectives (Jaramillo et al., 2022 ; Krou et al., 2021 ; Oh and Kim, 2015 ). A predictor variable of academic performance is the use of metacognitive strategies (Cheng et al., 2025 ; Elbyaly and Elfeky, 2022 ; Iqbal et al., 2022 ). Students who regularly apply metacognitive strategies plan, monitor, and regulate their own learning process more effectively (Flavell et al., 1993 ; Iqbal et al., 2022 ). The adoption of these strategies is associated with more autonomous, deeper, and meaningful learning and is reflected in better academic outcomes (Cheng et al., 2025 ; Elbyaly & Elfeky, 2022 ; Stanton et al., 2021 ). Furthermore, an autonomy-supportive climate also predicts academic performance (Johansen et al., 2023 ; Wang et al., 2024a , 2024b ). An autonomy-supportive climate fosters a greater sense of competence in students and a more active engagement in their own learning process (Johansen et al., 2023 ; Cullen & Oppenheimer, 2024 ). As a result, this environment also fosters deeper learning and has a positive impact on academic performance (Zhao & Qin, 2021 ; Johansen et al., 2023 ; Wang et al., 2024a , 2024b ). Academic self-efficacy has also been shown to be a predictor of improved performance (Meng & Zhang, 2023 ; Zysberg & Schwabsky, 2020 ). Another variable to consider is academic satisfaction. This refers to the level of satisfaction students experience with various aspects of their educational journey. This multifaceted concept encompasses satisfaction with academic services, teaching environments, structural environments, and opportunities for personal growth (Gu et al., 2023 ; Walter et al., 2024 ). One predictor of academic satisfaction is an autonomy-supportive climate (Jiang & Tanaka, 2021 ). An autonomy-supportive climate, fostered by the role of educators, can influence students' positive perceptions of their learning experience (Collie & Martin, 2024 ; Jiang & Tanaka, 2021 ). The use of metacognitive strategies also predicts greater academic satisfaction, and this relationship is influenced by multiple cognitive and motivational factors (Jeong & Park, 2022 ; Koyuncuoglu, 2023 ; Leinecker, 2023). Students with higher self-efficacy use metacognitive strategies more effectively, leading to greater satisfaction (Koyuncuoglu, 2023 ; Vincenzo & Carpi, 2024 ). Self-efficacy is also an important predictor of academic satisfaction (Lent et al., 2018; Morelli et al, 2023 ). Students with higher self-efficacy, understood as the belief in their own abilities to execute necessary actions and achieve specific goals, tend to report greater satisfaction with their academic experiences by feeling more competent to overcome academic challenges (Chahal et al., 2025 ; Tian et al., 2024 ). Self-efficacy also influences various factors, such as academic procrastination, increasing satisfaction (Tian et al., 2024 ). Given this scenario, it is important to consider that there are marked differences in the educational experience according to the gender of students in STEM fields, unfortunately to the detriment of women. Numerous studies have shown that academic self-efficacy in students in these majors is significantly lower among women compared to men (Marshman et al., 2018 ; Wang et al., 2023 ). This is because they may underestimate their abilities in mathematics-related fields due to the widespread perception that these fields are male-dominated (Cheryan et al., 2017 ; Riegle-Crumb and Peng, 2021 ). This low level of self-efficacy is, in turn, linked to lower academic performance among women in STEM majors (Lv et al., 2022 ; Wang and Yu, 2023 ). Likewise, evidence has been found indicating that female students in these majors experience lower academic satisfaction compared to their male peers (Ovink et al., 2024 ; Yang and Shen, 2020 ). Based on all the above, this research aims to analyze whether educational factors corresponding to the use of metacognitive strategies and the learning climate influence academic self-efficacy, and how all these variables, in turn, influence the academic performance and academic satisfaction of students in STEM fields (Fig. 1 ). Likewise, we aim to evaluate whether the relationships in this model vary depending on the sex of the students through a multigroup SEM analysis, as this could be used to propose differentiated interventions based on their needs in the future. Based on this model, the following hypotheses are proposed: H1: Academic self-efficacy is expected to be positively predicted by an autonomy-supportive climate. H2: Academic self-efficacy is expected to be positively predicted by metacognitive learning strategies. H3: Academic performance is expected to be positively predicted by an autonomy-supportive climate. H4: Academic performance is expected to be positively predicted by metacognitive learning strategies. H5: Academic performance is expected to be positively predicted by academic self-efficacy. H6: Satisfaction is expected to be positively predicted by an autonomy-supportive climate. H7: Satisfaction is expected to be positively predicted by metacognitive learning strategies. H8: Satisfaction is expected to be positively predicted by academic self-efficacy. 2. Method Participants The sample consisted of 550 students between the ages of 18 and 34 (M = 20.87, SD = 2.88), of whom 253 (46%) were women and 297 (54%) were men. Furthermore, it was identified that 97% of the sample was made up of engineering students, while 3% studied a science program, such as mathematics, statistics, chemistry, physics, etc. Furthermore, 78 students (14%) were from public universities, while 472 (86%) were from private universities. The sample size was calculated using a power analysis for multigroup structural equation modeling. Rstudio's "simsem" package was used for this analysis. Parameters were proposed for the indices by group, expecting factor loadings of 0.70 for each item, as well as predictive relationships of at least 0.50 between the latent variables. A Monte Carlo simulation was also used to determine the statistical power of the model. The simulation indicated that a sample of 220 participants per group achieved a statistical power of 0.80 to detect the proposed effects. Furthermore, the simulated models showed adequate fit indices (RMSEA = 0.028, SRMR = 0.059, CFI = 0.955, TLI = 0.953), suggesting a good recovery of the theoretical model. Thus, a total sample of at least 440 participants (220 per group) was considered an optimal size to ensure a valid analysis with sufficient statistical power. Regarding the inclusion and exclusion criteria, the following parameters were considered for sample selection: Inclusion criteria required participants to be university students in STEM (Science, Technology, Engineering, and Mathematics) programs, of legal age, enrolled at public or private universities in Lima, and enrolled at the time of the test. Furthermore, they had to have completed at least one previous academic cycle. Regarding the exclusion criteria, newly enrolled students with no previous university academic experience were excluded, as to participate in the study they had to identify a course they considered important for their professional development among those they had previously taken. It is important to note that, although some participants reported being in their first cycle, the protocol was applied to them after they had already received their grades for the corresponding academic period. Measurement Academic self-efficacy. To assess academic self-efficacy, we used the Self-Efficacy Scale for Academic Situations, originally developed by Palenzuela ( 1983 ) and subsequently adapted to the Peruvian context by Dominguez et al. ( 2018 ). This instrument assesses how students perceive their own abilities to successfully face their academic responsibilities. The scale consists of 9 items organized into a single factor, with Likert-format response options ranging from 1 ("Never") to 4 ("Always"). The version adapted by Dominguez et al. ( 2018 ), which was used in this study, has robust psychometric properties, demonstrating satisfactory internal consistency (Cronbach's alpha = .89) and adequate validity (KMO = .94). As part of this study, a Confirmatory Factor Analysis was conducted to verify the unidimensional structure of the instrument, obtaining a CFI (0.975) and TLI (0.966) with scores greater than 0.95, as well as an RMSEA (0.073 [0.063–0.084]) and SRMR (0.027) below 0.08, demonstrating a good model fit (Hu and Bentler, 1999 ). Likewise, the instrument obtained a Cronbach's Alpha of 0.95 and a McDonald's Omega of 0.96, demonstrating good reliability scores. Metacognitive learning strategies. The Motivated Strategies for Learning Questionnaire (MSLQ) was used. The original instrument was developed by Pintrich et al. ( 1991 ). For this study, the Spanish version adapted and validated on a sample of Peruvian students by Matos & Lens ( 2006 ) was used. This adaptation is composed of a total of 31 items grouped into five dimensions. The first four dimensions measure the use of cognitive learning strategies: rehearsal (4 items), elaboration (6 items), organization (4 items), and critical thinking (5 items). The fifth dimension measures the use of metacognitive strategies (12 items). The scale has a Likert-type response format from 1 to 5, where 1 is "Absolutely False" and 5 is "Absolutely True." Through Confirmatory Factor Analysis (CFA), the authors of this adaptation obtained good fit indices: χ2 (367, N = 1296) = 2038.20, p < .001 (RMSEA = 0.059; SRMR = 0.043), but in the process they had to eliminate two items from the metacognitive strategies dimension. In the reliability tests, they obtained Cronbach's alpha indices greater than .66 for each dimension (Matos & Lens, 2006 ). For the present study, only the dimension corresponding to the use of metacognitive strategies was used, a CFA of the dimension used was performed, obtaining adequate fit indices. Thus, a CFI (0.958), TLI (0.948), RMSEA (0.054 [0.045–0.063]) and SRMR (0.043) were obtained in accordance with the scores suggested by Hu and Bentler ( 1999 ). On the other hand, it was found that the dimension presents a good Cronbach's Alpha index (0.89) and McDonald's Omega (0.91). Autonomy-supportive climate. The Learning Climate Questionnaire was used; the authors of the original version are Williams and Deci ( 1996 ). For this study, the adapted and validated version developed by Bernal ( 2024 ) for Peruvian university students was used. This scale assesses student perceptions of the teacher's ability to foster autonomy in the classroom. It originally consisted of 15 items grouped into a single dimension (Williams & Deci, 1996 ). However, in Bernal's (2024) adaptation, which will be used, one item was eliminated because it did not meet content validity standards according to the judges. The scale has a Likert-type response format from 1 to 7, where 1 is "Strongly disagree" and 7 is "Strongly agree." Using a CFA, the author of this adaptation obtained excellent fit indices: CFI = .977, TLI = .973, RMSEA = .050, SRMR = .026 (Bernal, 2024 ). In the reliability test, the scale also presented a Cronbach's alpha index of .98 (Bernal, 2024 ). For the present study, a CFA was also performed on the scale, obtaining adequate fit indices (Hu & Bentler, 1999 ): CFI = 0.952, TLI = 0.943, RMSEA = 0.067 [0.061–0.074], SRMR = 0.045. Likewise, it was confirmed that the dimension presents a good Cronbach's alpha index (0.94) and McDonald's omega index (0.96). On the one hand, to assess participants' academic performance, they were asked to select a course they considered most important for their professional development. Based on their course selection, they were asked to indicate their final grade. Participants were asked to respond by writing a grade from 0 to 20. On the other hand, to assess participants' level of satisfaction with their career, they were asked to answer the following question: "Are you satisfied with the career you have chosen?" Participants were asked to respond using a Likert scale of 1 to 6, where 1 was "Not at all" and 6 was "Completely." Procedure Initially, the instrument creators were contacted to obtain their authorization for use. In parallel, the content adaptation and validation process was carried out with the support of two or more subject matter experts who evaluated the instruments. After completing the expert validation, a pilot test was implemented to identify any necessary adjustments to the protocol. The identified changes were incorporated to ensure participants understood the protocol. Subsequently, a digital version of the protocol was created using Google Forms, structured in a logical sequence: first, informed consent, followed by the collection of sociodemographic data, and finally, the measurement instruments (following the order specified in the "measurement" section). The form was programmed to show the questionnaires only to those who accepted informed consent; otherwise, it was automatically closed. Distribution was carried out via a QR code that directed the digital form, using both in-person methods and social media dissemination. The data collection staff briefly explained the study's purposes to potential participants and shared the QR code with those who agreed to participate, without supervision during the response process. Once the collection phase was completed, the database was exported for statistical processing. The process culminated with the interpretation of the findings within the framework of the adopted theoretical foundation, formulating conclusions that highlighted both the conceptual contributions and practical applications of the results obtained. Data Analysis Statistical analyses were conducted using RStudio software. A confirmatory factor analysis (CFA) was first performed for each scale, and the internal consistency coefficient (Cronbach's alpha) was also assessed for each dimension. Descriptive statistics were then calculated for all variables. For the structural equation model, the robust maximum likelihood (MLM) estimator was used, which is suitable for correcting for multivariate nonnormality if present (Satorra & Bentler, 2001 ). Model fit was verified using the CFI, TLI, RMSEA, and SRMR indices, using the ideal parameters proposed by Hu and Bentler ( 1999 ) as a guide: CFI and TLI (≥ 0.90), and RMSEA and SRMR (≤ 0.08). The R² of the dependent variables corresponding to academic self-efficacy, academic performance, and satisfaction was also calculated. Then, the configural, metric, scalar, and strict invariance analyses were performed, taking into account the following ideal parameters: ΔCFI < 0.01, ΔSRMR < 0.025, and ΔRMSEA < .005 (Protzko, 2025 ). If the invariance indices are met, the multigroup structural equation model analyses will be performed to evaluate the model's behavior according to the students' sex. 3. Results The descriptive statistics of the variables used for the analysis of the model are presented in Table 1 . Table 1 Descriptive statistics of the model variables. Group Variable M SD Min Max Skewness Kurtosis Total Autonomy Support 60.420 13.302 9 84 -0.231 -0.139 Metacognitive Self-Regulation 45.333 9.455 14 66 -0.262 -0.007 Academic Self-Efficacy 38.598 8.892 11 54 -0.436 0.654 Grade 15.317 2.696 1 20 -0.682 1.847 Satisfaction 4.901 1.152 1 6 -1.025 3.834 Male Autonomy Support 60.656 13.090 14 84 -0.191 -0.036 Metacognitive Self-Regulation 44.836 10.136 11 66 -0.446 0.683 Academic Self-Efficacy 38.833 8.933 14 54 -0.162 -0.321 Grade 15.458 2.636 2 20 -0.589 1.416 Satisfaction 4.949 1.161 1 6 -1.111 3.956 Female Autonomy Support 60.145 13.565 15 84.000 -0.332 0.002 Metacognitive Self-Regulation 45.914 8.573 18 66 -0.328 0.178 Academic Self-Efficacy 38.324 8.854 9 54 -0.317 0.059 Grade 15.152 2.762 1 20 -0.766 2.206 Satisfaction 4.844 1.140 1 6 -0.931 3.735 Table 2 shows the fit indices of the hypothesized model, which are within the parameters recommended by Hu and Bentler ( 1999 ): CFI and TLI (≥ 0.90), and RMSEA and SRMR (≤ 0.08). Table 2 Hypothesized model fit indices Model X² df p S-B χ2 CFI TLI RMSEA SRMR Hypothesized model 1116.902 586 < 0.001 1.423 0.946 0.942 0.041 [0.038–0.044] 0.047 As can be seen in Fig. 2 and Table 3 , not all hypothesized relationships were significant within the overall model. Among the variables that were found to be predictors of academic self-efficacy were the autonomy-supporting climate (β = 0.319, p < .001) and metacognitive learning strategies (β = 0.271, p < .001). The variables that were found to be predictors of academic satisfaction were academic self-efficacy (β = 0.237, p < .001) and the autonomy-supporting climate (β = 0.159, p < .005). The only variable that was found to be a predictor of academic performance was the autonomy-supporting climate (β = 0.159, p < .01). Table 3 List of relationships of the hypothesized model Path β z-value p value Autonomy Support → Self Efficacy 0.319 5.181 < .001 Metacognitive Self-Regulation → Self Efficacy 0.271 4.644 < .001 Autonomy Support → Grade 0.200 3.911 < .001 Metacognitive Self-Regulation → Grade 0.034 0.606 0.545 Self Efficacy → Grade 0.072 1.240 0.215 Autonomy Support → Satisfaction 0.159 2.792 0.005 Metacognitive Self-Regulation → Satisfaction 0.059 0.854 0.393 Self Efficacy → Satisfaction 0.237 4.200 < .001 As can be seen in Table 4 , the ΔCFI indices are less than 0.01 at all levels, which indicates that, according to Protzko ( 2025 ), they are adequate to accept invariance. Likewise, the ΔSRMR scores are less than 0.025, which indicates that invariance exists (Protzko, 2025 ). Finally, the ΔRMSEA scores are less than 0.005, which also indicates that invariance exists (Protzko, 2025 ). These results allow for the performance of a multigroup structural equation model. Table 4 Invariance of the model according to sex Invariance X² df CFI SRMR RMSEA ΔCFI ΔSRMR ΔRMSEA Configural 2445.314 1170 0.94493 0.05369 0.04118 -- -- -- Metric 2489.977 1201 0.94351 0.05793 0.04117 -0.00142 0.00423 -0.00001 Scalar 2525.822 1234 0.94191 0.05835 0.04118 -0.00160 0.00041 0.00001 Strict 2549.363 1270 0.94643 0.055836 0.03899 0.00451 0.00001 -0.00219 As can be seen in Fig. 3 and Table 5 , not all hypothesized relationships were significant for the analysis of the model by sex. As in the general model, the predictor variables of academic self-efficacy turned out to be the autonomy-supporting climate and learning strategies, both in women (β = 0.195, p < 0.027; β = 0.371, p < .001) and men (β = 0.431, p < .001; β = 0.182, p = .010), respectively. In the case of the predictor variables of academic satisfaction, one of them turned out to be academic self-efficacy (β = 0.343, p < .001) only in the case of women and the autonomy-supporting climate (β = 0.248, p = .005) only in the case of men. Finally, the predictive variables of academic performance turned out to be academic self-efficacy (β = 0.184, p = .027) and the autonomy support climate (β = 0.318, p < .001) only in the case of women. Table 5 Gender Path β z-value p value Male Autonomy Support → Self Efficacy 0.431 5.489 < .001 Metacognitive Self-Regulation → Self Efficacy 0.182 2.583 0.010 Autonomy Support → Grade 0.086 1.214 0.225 Metacognitive Self-Regulation → Grade 0.059 0.808 0.419 Self Efficacy → Grade -0.016 -0.215 0.829 Autonomy Support → Satisfaction 0.248 2.790 0.005 Metacognitive Self-Regulation → Satisfaction 0.089 0.865 0.387 Self Efficacy → Satisfaction 0.130 1.761 0.078 Autonomy Support → Self Efficacy 0.195 2.212 0.027 Female Metacognitive Self-Regulation → Self Efficacy 0.371 3.902 < .001 Autonomy Support → Grade 0.318 4.228 < .001 Metacognitive Self-Regulation → Grade 0.033 0.423 0.672 Self Efficacy → Grade 0.184 2.206 0.027 Autonomy Support → Satisfaction 0.090 1.250 0.211 Metacognitive Self-Regulation → Satisfaction -0.001 -0.006 0.996 Self Efficacy → Satisfaction 0.343 4.120 < .001 4. Discussion As expected, an autonomy-supportive climate predicted a higher sense of academic efficacy in students of both sexes (H1), a result that has also been supported by previous research with mixed samples (Gutiérrez & Tomás, 2019 ; Kingsford-Smith et al., 2024 ; Ito et al., 2024 ). An autonomy-supportive climate is characterized by an environment in which the teacher trusts the capacities, skills, and potential of each student (Núñez & León, 2015 ; Ryan & Deci, 2017 ). This trust translates into pedagogical practices that respect and promote student autonomy, providing them with opportunities to make decisions, assume responsibilities, and express their own voice in the learning process (Núñez & León, 2015 ; Ryan & Deci, 2017 ). When students perceive that their teacher believes in them and considers them capable of acting autonomously, a process of internalization of external recognition is activated; That is, external support and appreciation strengthens beliefs about self-efficacy or perceived competence in those who receive it (Bandura, 1997 ; Li & Singh, 2023 ; Peifer et al., 2020 ; Rader et al., 2024 ; Rodriguez et al., 2019 ). From this perspective, external recognition plays a key role in self-validation and in developing a more positive perception of one's own abilities (Bandura, 1997 ; Rader et al., 2024 ). From self-determination theory, it is proposed that a climate of autonomy support encourages students to trust their own abilities and potential more, which satisfies their basic psychological need for competence and strengthens their sense of academic self-efficacy (Bandura, 1997 ; Germani & Palombi, 2022 ; Ryan & Deci, 2017 ; Slemp et al., 2024 ). Likewise, metacognitive strategies were also found to predict better self-efficacy in STEM careers (H2), in both men and women. This suggests that when students develop skills to plan, monitor, and regulate their own learning process, they also strengthen their perception of competence and ability to face academic challenges. This finding supports the importance of promoting metacognitive skills in higher education, particularly in STEM fields where the content is often perceived as complex (Rodríguez et al., 2021 ; Markovits & Forgasz, 2017 ). Authors such as Akamatsu et al. ( 2019 ) and Khosravi et al. ( 2023 ) have also documented the predictive role of metacognitive strategies on the improvement of academic self-efficacy. This finding corroborates that metacognitive skills can improve performance and confidence in one's own abilities (Hayat et al., 2020 ). On the other hand, an autonomy-supportive climate was found to predict better academic performance in women in STEM fields (H3), but not in their male peers. This finding can be explained based on previous research that has shown that an environment characterized by support for student autonomy favors better academic performance (e.g., Bonem et al., 2020 ; Marshik et al., 2017 ; Mammadov and Schroeder, 2023 ). This is because such a climate encourages students to study not out of external obligation, but because they manage to find meaning and personal direction in learning (Ryan and Deci, 2017 ). Thus, students position themselves in the educational process in a more active, profound, and genuine way, which usually translates into better academic performance (Ryan and Deci, 2017 ; Mammadov and Schroeder, 2023 ). However, most of these studies have not considered possible variations in this relationship based on the student's sex. This observed difference in outcomes could be explained, in part, by the fact that in traditionally male-dominated fields such as STEM careers, women tend to feel greater pressure to demonstrate their abilities (Cokley et al., 2015 ; Butler, 2014 ; Cheryan et al., 2017 ), which could lead them to further internalize the desire to obtain good grades as a way of validating their presence in that space. Evidence suggests that women place greater emphasis on academic performance (Kuśnierz et al., 2020 ); however, this goes beyond the STEM field, as it responds to social factors. From an early age, girls are often reinforced for showing responsibility, obedience, and academic commitment, while boys are granted greater tolerance for distraction (Kangethe et al., 2014 ; Martínez and Gil, 2019 ). Likewise, some studies have shown that men tend to respond better to external regulations or more controlling incentives to improve their performance (Vecchione et al., 2014 ), which could be interpreted as a manifestation that, for them, academic performance is not always among their most internalized goals. In this sense, a context that favors autonomy could especially drive women to obtain better grades, since for many of them this constitutes an important goal (Kuśnierz et al., 2020 ). In contrast, in the case of men, achieving high academic performance is not usually an equally marked priority (Kuśnierz et al., 2020 ), which would limit the impact of autonomy support on this variable. Another hypothesis (H5) proposed for the present study suggests that the use of metacognitive strategies would predict better academic performance (De Boer et al., 2018 ; Swanson et al., 2024 ; Zhang and Lian, 2024 ). However, in the present study, no significant relationship was found between both variables. This discrepancy could be explained by the characteristics of the assessment system used in the analyzed context. Several authors have pointed out that, in some cases, mathematics teaching adopts a superficial and mechanistic approach, focusing on repetition and memorization rather than comprehension (Khalid et al., 2020 ; Garcés-Córdova & Font-Moll, 2022 ; Garcés Córdova et al., 2021 ; Marín et al., 2024 ). In line with this, Vatterott ( 2015 ) warns that grading styles that prioritize content memorization over the development of critical thinking and deep understanding still persist. Under these conditions, it is understandable that the use of metacognitive strategies is not necessarily reflected in higher academic performance. Another possible explanation is that students who report using metacognitive strategies may not be applying them appropriately or in-depth, which would limit their effectiveness. As Craig et al. ( 2020 ) and Berger and Karabenick ( 2016 ) point out, although self-report instruments are useful for determining the frequency of use of metacognitive strategies, they do not always accurately capture the quality or effectiveness of their application in real-life contexts. Another hypothesis proposed was that higher academic self-efficacy would predict better academic performance in STEM students (H5), which was fulfilled but only for women, not for men. In this sense, it is important to note that several studies have shown that academic self-efficacy has a moderate, rather than a strong, predictive role in academic performance (Honicke & Broadbent, 2016 ; Yokoyama, 2024 ). This suggests that, although self-efficacy contributes to academic success, it is not the only determining factor and its influence may vary depending on other elements and contextual factors (Honicke & Broadbent, 2016 ; Yokoyama, 2024 ). In the specific case of women, the relationship between self-efficacy and academic performance has particular characteristics. In the context of STEM careers, women often face negative stereotypes and may feel greater pressure to demonstrate their abilities, which could create a stronger connection between their self-efficacy and their academic performance (Cheryan et al., 2017 ; Riegle-Crumb & Peng, 2021 ). For women, confidence in their own abilities appears to be a fundamental driver of their performance, possibly as a way to validate their belonging in these academic spaces (Lv et al., 2022 ; Wang & Yu, 2023 ). In contrast, for male students, academic performance appears to be influenced by other factors not considered in this model. This could reflect the fact that, being in an environment where their presence is predominant and culturally expected, they do not experience the same need to validate their abilities through grades (Kuśnierz et al., 2020 ). On the other hand, it was proposed that satisfaction is positively predicted by an autonomy-supportive climate (H6), exploring possible differences based on students' gender. It was found that this climate predicted greater academic satisfaction in male students in STEM fields, but not in female students. This finding is especially interesting considering that several previous studies have shown that an autonomy-supportive climate tends to predict greater academic satisfaction (Barrientos-Illanes et al., 2021 ; Froment et al., 2023 ), as it promotes an environment in which students can make decisions about their own learning process, aligning it, as much as possible, with their personal interests and needs (Ryan & Deci, 2017 ; Núñez & León, 2015 ). This possibility of self-direction contributes to a more meaningful and satisfying educational experience. The difference found in this study could be explained, in part, by a habituation phenomenon, which would cause men and women to develop different emotional responses to this type of climate (Galak and Redden, 2018 ; Klausen et al., 2022 ). From a sociocultural perspective, women are often trained to develop a greater orientation toward autonomy, due to social norms and cultural expectations that reinforce self-regulation, responsibility, and self-management in them (Lenes et al., 2020 ; Modrek et al., 2021 ). It is important to clarify that, although autonomy is generally perceived as something positive, the fact that women have been socialized towards it does not necessarily imply greater freedom to make decisions. Rather, it responds to the traditional assignment of care and service roles, which require them to be self-sufficient in supporting and caring for others (del Río-Lozano et al., 2013 ; Tavero et al., 2018 ). As a result, an autonomy-supportive academic environment, while it may favor their performance, does not necessarily increase their academic satisfaction, since it does not represent a significant change from what they already know (Lenes et al., 2020 ; Modrek et al., 2021 ). On the other hand, for many boys, who may have been socialized under frameworks that prioritize external control, this type of climate may be more novel and rewarding (Vecchione et al., 2014 ). Therefore, the perception of an environment that validates their personal initiative, something they are not used to, may generate a more marked positive emotional response, translating into higher levels of academic satisfaction. The seventh hypothesis suggests a positive relationship between metacognitive learning strategies and satisfaction (H7). Contrary to expectations, metacognitive strategies did not predict academic satisfaction in any of the groups studied. This result contradicts some previous research (Jeong & Park, 2022 ; Koyuncuoglu, 2023 ) and could be related to the characteristics of the educational system. The use of these strategies may not be adequately valued and rewarded in prevailing assessment methods, which may focus more on memorization than on deep comprehension and critical thinking (Khalid et al., 2020 ; Garcés-Córdova & Font-Moll, 2022 ). In this context, the effort invested in developing and applying these strategies does not necessarily translate into a more satisfying academic experience (Vatterott, 2015 ). Koca et al. (2014) mention that while students who develop awareness and control over their cognitive processes may experience some improvement in their academic satisfaction, the magnitude of this effect is so small that it does not represent a relevant determining or predictive factor for overall satisfaction with the university experience; that is, the direct impact of metacognitive skills on satisfaction is limited (Koca et al., 2024 ). Likewise, Norman (2020) argues that metacognition will not necessarily always predict greater satisfaction, since in some cases where metacognition involves a negative self-assessment, it can harm emotional well-being, including, within this well-being, the perception of satisfaction. Academic satisfaction is an affective-emotional construct that depends on broader factors such as the educational environment, social relationships, the fulfillment of personal expectations, and the general psychological well-being of the student (Fernández-Zabala et al., 2016 ). Although metacognitive skills can improve performance and confidence in one's abilities (Hayat et al., 2020 ), they do not necessarily guarantee that the student will experience greater well-being or satisfaction with their overall educational experience, since the latter responds to subjective and contextual dimensions that transcend the purely cognitive processes of planning, monitoring, and evaluating learning (Norman, 2020). Finally, academic self-efficacy was found to be a predictor of academic satisfaction only in female students (H8). This finding is consistent with previous research that has found self-efficacy to be an important predictor of academic satisfaction (Lent et al., 2018 ; Morelli et al., 2023 )). For female students in STEM fields, feeling capable and competent in their field of study is critical to their well-being and satisfaction at university (Chahal et al., 2025 ; Tian et al., 2024 ). In an environment where they are underrepresented and where they may face implicit or explicit questions about their abilities (Ovink et al., 2024 ; Yang & Shen, 2020 ), self-confidence appears to play a crucial role in how they experience and enjoy their academic training. For male students, academic satisfaction appears to be linked to factors other than self-efficacy. This could reflect different priorities or sources of satisfaction, possibly related to aspects such as social recognition or perceived professional opportunities (Vecchione et al., 2014 ). This gender difference was also found by Koca et al. (2023), who found that academic self-efficacy increased academic satisfaction scores only in the case of female students. In contrast, this effect of academic self-efficacy on satisfaction does not operate in the same way in males. 5. Conclusions The findings of this study suggest that both the autonomy-supportive climate and metacognitive strategies have a significant impact on academic self-efficacy in STEM students, regardless of gender. However, the role of climate and self-efficacy in academic satisfaction and performance varies by gender. In women, both the autonomy-supportive climate and self-efficacy predicted academic performance. In contrast, neither of these variables predicted performance in men, which could be because men are accustomed to improving their performance based on more external or controlling incentives (Vecchione et al., 2014 ), or because they do not experience the need to validate their self-efficacy through grades (Kuśnierz et al., 2020 ). These results may reveal that the symbolic value assigned to academic achievement differs between genders: for women, it may represent an important achievement, while for men, it may not carry the same subjective or symbolic weight (Kuśnierz et al., 2020 ). These differences raise valuable questions about how men and women construct their academic identity. Furthermore, while self-efficacy predicted greater academic satisfaction in women, this relationship was not significant in men. This could be because, being underrepresented in STEM fields, women find self-efficacy an important source of validation to sustain satisfaction (Chahal et al., 2025 ; Ovink et al., 2024 ). In contrast, for men, it was an autonomy-supportive climate that predicted academic satisfaction. One possible explanation is that this type of climate represents a novel experience for them, having been socialized in contexts where external control and direction predominate (Vecchione et al., 2014 ). For women, however, this type of support may be so normalized that it fails to elicit a significant emotional response (Lenes et al., 2020 ; Modrek et al., 2021 ). These differences invite reflection on how gender trajectories shape the academic experience. 6. Recommendations and Future Directions For future research, it is suggested that self-perceived learning be included as a complementary variable to performance, as the latter could be mediated by evaluation criteria focused on memorization, which would not accurately reflect meaningful learning (Vatterott, 2015 ; Fearnley et al., 2022 ; Anthonysamy & Singh, 2023 ; Vallet-Bellmunt et al., 2017 ). Finally, although the discussion suggests that men may be more oriented toward responding to external stimuli, this finding should not be interpreted as an invitation to foster controlling practices. On the contrary, Self-Determination Theory emphasizes the importance of cultivating autonomous and intrinsic motivation for all students, as this fosters deeper and more sustained learning over time (Ryan & Deci, 2017 ; Zhang & Lian, 2024 ). It is also suggested that future research compare these findings with what occurs in traditionally feminized careers in order to explore whether the differences found remain the same or vary depending on the type of career and the gender stereotypes associated with it. Declarations Funding Declaration This research was funded by Pontificia Universidad Católica del Perú under grant CAP PI-1139. Consent for publication Not applicable. Ethics Declaration This study was conducted in accordance with the ethical standards of the Ethics Committee of the Pontifical Catholic University of Peru. Ethical approval was obtained under approval number 085-2024-CEI-CCSSHHyAA/PUCP, prior to data collection. 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18:46:32","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":253476,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7377511/v1/bd98010d500a6b2b0f080322.html"},{"id":92112441,"identity":"c20a8ec2-bab8-461f-8e23-d66af04adb64","added_by":"auto","created_at":"2025-09-24 18:46:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":24782,"visible":true,"origin":"","legend":"\u003cp\u003eHypothesized equation model for the predictive relationship of the study variables.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7377511/v1/ce70f958f4814b23fe587e8b.jpg"},{"id":92112442,"identity":"ecc59300-c098-4877-a47d-403a44613199","added_by":"auto","created_at":"2025-09-24 18:46:32","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17723,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the hypothesized model.\u003c/p\u003e\n\u003cp\u003e* p \u0026lt; 0.05; ** p \u0026lt; 0.01; ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7377511/v1/0225be6cfafd60a6007b7fc4.jpg"},{"id":92112448,"identity":"478eff7b-449a-4818-a566-4ff03823eb58","added_by":"auto","created_at":"2025-09-24 18:46:32","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":22270,"visible":true,"origin":"","legend":"\u003cp\u003eNotes: * p less than 0.05; ** p less than 0.01; ***p less than 0.001; women's β and R² are shown outside the parentheses, men's β and R² are shown inside the parentheses.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7377511/v1/bdad39132a1ce058f0a2a173.jpg"},{"id":92114699,"identity":"27637f17-5410-476f-b0d9-ba04cd5a8b2a","added_by":"auto","created_at":"2025-09-24 19:10:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":993848,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7377511/v1/36d204c2-f28c-464b-ab97-614c06fda234.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gender differences in STEM careers: autonomy-supportive climate, metacognitive strategies, self-efficacy, and their impact on academic performance and satisfaction","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSTEM (Science, Technology, Engineering and Mathematics, STEM) education have become increasingly important due to the growing technological and scientific innovation in recent years (Salas-Pilco, 2024; Wang et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, there are two important problems related to the demand for professionals in these careers: many of the students have difficulties in coping with the academic demands; and there is a gender gap in STEM careers, marked by a low percentage of female students.\u003c/p\u003e\u003cp\u003eRegarding the difficulties in coping with academic demands, evidence shows that about 75% of students in Latin America and the Caribbean who participated in the last PISA test did not achieve basic competencies in mathematics, and 57% did not achieve basic competencies in science (Arias Ortiz et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This situation is even more worrisome in the case of females, since the percentage with low performance in mathematics is higher than that of males (77% vs. 72%), and the same in science (58% vs. 56%) (Arias Ortiz et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIt is problematic that students, in general, perceive mathematics and science content as complex and difficult (Rodriguez et al., 2021; Markovits \u0026amp; Forgasz, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), as this can generate disinterest, rejection, or the decision not to study a career related to this field (Ahmed \u0026amp; Mudrey, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lopez et al. 2023; Luo et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e;). This tendency is even more present among female students nearing the end of their secondary education, as several studies have found that they show significantly less interest than their male peers in pursuing STEM careers (Wang et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAll of this leads to the problem related to a low proportion of women in the STEM field (Shi, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The United Nations Educational, Scientific and Cultural Organization [UNESCO] (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) points out that women represent only 35% of students enrolled globally in these careers, a disparity that is also observed in Latin America and the Caribbean (Bello, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, this situation is aggravated if one takes into account that the dropout rate of female students in these careers is particularly high (Wang et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Quevedo and Vegas (2020) warn that this gender gap has a negative impact on society, as it increases the wage gap, reduces female participation in leadership positions and limits economic growth. In addition to all this, it is important to consider that evidence indicates that the STEM study experience is related to various educational factors, such as the use of learning strategies, the learning climate, self-efficacy, academic performance, and satisfaction.\u003c/p\u003e\u003cp\u003eThe literature points out that learning strategies are all thoughts and behaviors that students adopt intentionally to facilitate their own learning process (Hattie \u0026amp; Donoghue, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Paris et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Weinstein \u0026amp; Mayer, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Yip, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). According to McKeachie et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), learning strategies can be classified into three categories: cognitive, metacognitive, and resource management. The present study will focus specifically only on metacognitive ones.\u003c/p\u003e\u003cp\u003eMetacognitive strategies imply a prior planning process, where the student sets study goals, develops a learning plan, and uses strategies according to the assessed difficulty of a task (Akamatsu et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; McKeachie et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). It also involves a personal monitoring process, where the student self-evaluates their strategies, allowing them to monitor progress in any cognitive activity (McKeachie et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Stanton et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Weinstein and Mayer, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Finally, behavioral regulation is also addressed, where the student makes adjustments or corrections to the study method to correctly adapt to the difficulty of the task (Akamatsu et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; McKeachie et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1986\u003c/span\u003e;). This entire process is important and has a direct relationship with training in STEM careers (Engelmann et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gamby \u0026amp; Bauer, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Huvard et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), as it facilitates the academic experience in the classroom.\u003c/p\u003e\u003cp\u003eIn this sense, the classroom experience provided by the teacher must also be taken into account. Thus, another educational factor to consider is the autonomy-supportive climate, which can be understood as the perception of autonomy and agency offered by a teacher in the classroom (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It also helps nurture and develop students' internal motivational resources (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; N\u0026uacute;\u0026ntilde;ez \u0026amp; Le\u0026oacute;n, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Ryan and Deci (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) mention that for individuals to feel their autonomy is being supported, they must be provided with clear explanations about the usefulness of the activity to be carried out. Likewise, N\u0026uacute;\u0026ntilde;ez and Le\u0026oacute;n (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) mention that meaningful options should be offered that encourage decision-making and reinforce internal motivational resources (curiosity, enjoyment, interest, or sense of challenge).\u003c/p\u003e\u003cp\u003eAnother educational factor to consider is academic self-efficacy. Self-efficacy is a key concept in social cognitive theory. It refers to an individual's belief in their ability to perform the behaviors necessary to produce specific outcomes (Bandura, 2019). This perception significantly influences how people think, motivate themselves, and act (Schunk \u0026amp; DiBenedetto, 2020). Metacognitive strategies, especially those related to planning, have been found to improve academic self-efficacy (Akamatsu et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Likewise, the perception of an autonomy-supportive climate is associated with higher self-efficacy (Guti\u0026eacute;rrez \u0026amp; Tom\u0026aacute;s, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ito et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kingsford-Smith et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhao \u0026amp; Qin, \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAll of these variables are relevant because they are related to students' academic performance (Cockrell, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lever et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Generally, grades are approached as a measure of academic performance, since they allow the evaluation of the degree to which a student has acquired knowledge, skills, and competencies in relation to the established educational objectives (Jaramillo et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Krou et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Oh and Kim, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). A predictor variable of academic performance is the use of metacognitive strategies (Cheng et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Elbyaly and Elfeky, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Iqbal et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Students who regularly apply metacognitive strategies plan, monitor, and regulate their own learning process more effectively (Flavell et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Iqbal et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The adoption of these strategies is associated with more autonomous, deeper, and meaningful learning and is reflected in better academic outcomes (Cheng et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Elbyaly \u0026amp; Elfeky, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Stanton et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, an autonomy-supportive climate also predicts academic performance (Johansen et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). An autonomy-supportive climate fosters a greater sense of competence in students and a more active engagement in their own learning process (Johansen et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Cullen \u0026amp; Oppenheimer, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As a result, this environment also fosters deeper learning and has a positive impact on academic performance (Zhao \u0026amp; Qin, \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Johansen et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). Academic self-efficacy has also been shown to be a predictor of improved performance (Meng \u0026amp; Zhang, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zysberg \u0026amp; Schwabsky, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnother variable to consider is academic satisfaction. This refers to the level of satisfaction students experience with various aspects of their educational journey. This multifaceted concept encompasses satisfaction with academic services, teaching environments, structural environments, and opportunities for personal growth (Gu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Walter et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOne predictor of academic satisfaction is an autonomy-supportive climate (Jiang \u0026amp; Tanaka, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). An autonomy-supportive climate, fostered by the role of educators, can influence students' positive perceptions of their learning experience (Collie \u0026amp; Martin, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jiang \u0026amp; Tanaka, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The use of metacognitive strategies also predicts greater academic satisfaction, and this relationship is influenced by multiple cognitive and motivational factors (Jeong \u0026amp; Park, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Koyuncuoglu, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Leinecker, 2023). Students with higher self-efficacy use metacognitive strategies more effectively, leading to greater satisfaction (Koyuncuoglu, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Vincenzo \u0026amp; Carpi, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSelf-efficacy is also an important predictor of academic satisfaction (Lent et al., 2018; Morelli et al, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Students with higher self-efficacy, understood as the belief in their own abilities to execute necessary actions and achieve specific goals, tend to report greater satisfaction with their academic experiences by feeling more competent to overcome academic challenges (Chahal et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tian et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Self-efficacy also influences various factors, such as academic procrastination, increasing satisfaction (Tian et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGiven this scenario, it is important to consider that there are marked differences in the educational experience according to the gender of students in STEM fields, unfortunately to the detriment of women. Numerous studies have shown that academic self-efficacy in students in these majors is significantly lower among women compared to men (Marshman et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This is because they may underestimate their abilities in mathematics-related fields due to the widespread perception that these fields are male-dominated (Cheryan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Riegle-Crumb and Peng, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This low level of self-efficacy is, in turn, linked to lower academic performance among women in STEM majors (Lv et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang and Yu, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Likewise, evidence has been found indicating that female students in these majors experience lower academic satisfaction compared to their male peers (Ovink et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yang and Shen, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Based on all the above, this research aims to analyze whether educational factors corresponding to the use of metacognitive strategies and the learning climate influence academic self-efficacy, and how all these variables, in turn, influence the academic performance and academic satisfaction of students in STEM fields (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Likewise, we aim to evaluate whether the relationships in this model vary depending on the sex of the students through a multigroup SEM analysis, as this could be used to propose differentiated interventions based on their needs in the future.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on this model, the following hypotheses are proposed:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eH1: Academic self-efficacy is expected to be positively predicted by an autonomy-supportive climate.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eH2: Academic self-efficacy is expected to be positively predicted by metacognitive learning strategies.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eH3: Academic performance is expected to be positively predicted by an autonomy-supportive climate.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eH4: Academic performance is expected to be positively predicted by metacognitive learning strategies.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eH5: Academic performance is expected to be positively predicted by academic self-efficacy.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eH6: Satisfaction is expected to be positively predicted by an autonomy-supportive climate.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eH7: Satisfaction is expected to be positively predicted by metacognitive learning strategies.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eH8: Satisfaction is expected to be positively predicted by academic self-efficacy.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"2. Method","content":"\u003cp\u003e\u003cb\u003eParticipants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe sample consisted of 550 students between the ages of 18 and 34 (M\u0026thinsp;=\u0026thinsp;20.87, SD\u0026thinsp;=\u0026thinsp;2.88), of whom 253 (46%) were women and 297 (54%) were men. Furthermore, it was identified that 97% of the sample was made up of engineering students, while 3% studied a science program, such as mathematics, statistics, chemistry, physics, etc. Furthermore, 78 students (14%) were from public universities, while 472 (86%) were from private universities.\u003c/p\u003e\u003cp\u003eThe sample size was calculated using a power analysis for multigroup structural equation modeling. Rstudio's \"simsem\" package was used for this analysis. Parameters were proposed for the indices by group, expecting factor loadings of 0.70 for each item, as well as predictive relationships of at least 0.50 between the latent variables. A Monte Carlo simulation was also used to determine the statistical power of the model. The simulation indicated that a sample of 220 participants per group achieved a statistical power of 0.80 to detect the proposed effects. Furthermore, the simulated models showed adequate fit indices (RMSEA\u0026thinsp;=\u0026thinsp;0.028, SRMR\u0026thinsp;=\u0026thinsp;0.059, CFI\u0026thinsp;=\u0026thinsp;0.955, TLI\u0026thinsp;=\u0026thinsp;0.953), suggesting a good recovery of the theoretical model. Thus, a total sample of at least 440 participants (220 per group) was considered an optimal size to ensure a valid analysis with sufficient statistical power.\u003c/p\u003e\u003cp\u003eRegarding the inclusion and exclusion criteria, the following parameters were considered for sample selection: Inclusion criteria required participants to be university students in STEM (Science, Technology, Engineering, and Mathematics) programs, of legal age, enrolled at public or private universities in Lima, and enrolled at the time of the test. Furthermore, they had to have completed at least one previous academic cycle. Regarding the exclusion criteria, newly enrolled students with no previous university academic experience were excluded, as to participate in the study they had to identify a course they considered important for their professional development among those they had previously taken. It is important to note that, although some participants reported being in their first cycle, the protocol was applied to them after they had already received their grades for the corresponding academic period.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMeasurement\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAcademic self-efficacy.\u003c/b\u003e To assess academic self-efficacy, we used the Self-Efficacy Scale for Academic Situations, originally developed by Palenzuela (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) and subsequently adapted to the Peruvian context by Dominguez et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This instrument assesses how students perceive their own abilities to successfully face their academic responsibilities. The scale consists of 9 items organized into a single factor, with Likert-format response options ranging from 1 (\"Never\") to 4 (\"Always\"). The version adapted by Dominguez et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which was used in this study, has robust psychometric properties, demonstrating satisfactory internal consistency (Cronbach's alpha\u0026thinsp;=\u0026thinsp;.89) and adequate validity (KMO\u0026thinsp;=\u0026thinsp;.94). As part of this study, a Confirmatory Factor Analysis was conducted to verify the unidimensional structure of the instrument, obtaining a CFI (0.975) and TLI (0.966) with scores greater than 0.95, as well as an RMSEA (0.073 [0.063\u0026ndash;0.084]) and SRMR (0.027) below 0.08, demonstrating a good model fit (Hu and Bentler, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Likewise, the instrument obtained a Cronbach's Alpha of 0.95 and a McDonald's Omega of 0.96, demonstrating good reliability scores.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMetacognitive learning strategies.\u003c/b\u003e The Motivated Strategies for Learning Questionnaire (MSLQ) was used. The original instrument was developed by Pintrich et al. (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). For this study, the Spanish version adapted and validated on a sample of Peruvian students by Matos \u0026amp; Lens (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) was used. This adaptation is composed of a total of 31 items grouped into five dimensions. The first four dimensions measure the use of cognitive learning strategies: rehearsal (4 items), elaboration (6 items), organization (4 items), and critical thinking (5 items). The fifth dimension measures the use of metacognitive strategies (12 items). The scale has a Likert-type response format from 1 to 5, where 1 is \"Absolutely False\" and 5 is \"Absolutely True.\" Through Confirmatory Factor Analysis (CFA), the authors of this adaptation obtained good fit indices: χ2 (367, N\u0026thinsp;=\u0026thinsp;1296)\u0026thinsp;=\u0026thinsp;2038.20, p\u0026thinsp;\u0026lt;\u0026thinsp;.001 (RMSEA\u0026thinsp;=\u0026thinsp;0.059; SRMR\u0026thinsp;=\u0026thinsp;0.043), but in the process they had to eliminate two items from the metacognitive strategies dimension. In the reliability tests, they obtained Cronbach's alpha indices greater than .66 for each dimension (Matos \u0026amp; Lens, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). For the present study, only the dimension corresponding to the use of metacognitive strategies was used, a CFA of the dimension used was performed, obtaining adequate fit indices. Thus, a CFI (0.958), TLI (0.948), RMSEA (0.054 [0.045\u0026ndash;0.063]) and SRMR (0.043) were obtained in accordance with the scores suggested by Hu and Bentler (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). On the other hand, it was found that the dimension presents a good Cronbach's Alpha index (0.89) and McDonald's Omega (0.91).\u003c/p\u003e\u003cp\u003e\u003cb\u003eAutonomy-supportive climate.\u003c/b\u003e The Learning Climate Questionnaire was used; the authors of the original version are Williams and Deci (\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). For this study, the adapted and validated version developed by Bernal (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) for Peruvian university students was used. This scale assesses student perceptions of the teacher's ability to foster autonomy in the classroom. It originally consisted of 15 items grouped into a single dimension (Williams \u0026amp; Deci, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). However, in Bernal's (2024) adaptation, which will be used, one item was eliminated because it did not meet content validity standards according to the judges. The scale has a Likert-type response format from 1 to 7, where 1 is \"Strongly disagree\" and 7 is \"Strongly agree.\" Using a CFA, the author of this adaptation obtained excellent fit indices: CFI\u0026thinsp;=\u0026thinsp;.977, TLI\u0026thinsp;=\u0026thinsp;.973, RMSEA\u0026thinsp;=\u0026thinsp;.050, SRMR\u0026thinsp;=\u0026thinsp;.026 (Bernal, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the reliability test, the scale also presented a Cronbach's alpha index of .98 (Bernal, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For the present study, a CFA was also performed on the scale, obtaining adequate fit indices (Hu \u0026amp; Bentler, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1999\u003c/span\u003e): CFI\u0026thinsp;=\u0026thinsp;0.952, TLI\u0026thinsp;=\u0026thinsp;0.943, RMSEA\u0026thinsp;=\u0026thinsp;0.067 [0.061\u0026ndash;0.074], SRMR\u0026thinsp;=\u0026thinsp;0.045. Likewise, it was confirmed that the dimension presents a good Cronbach's alpha index (0.94) and McDonald's omega index (0.96).\u003c/p\u003e\u003cp\u003eOn the one hand, to assess participants' academic performance, they were asked to select a course they considered most important for their professional development. Based on their course selection, they were asked to indicate their final grade. Participants were asked to respond by writing a grade from 0 to 20. On the other hand, to assess participants' level of satisfaction with their career, they were asked to answer the following question: \"Are you satisfied with the career you have chosen?\" Participants were asked to respond using a Likert scale of 1 to 6, where 1 was \"Not at all\" and 6 was \"Completely.\"\u003c/p\u003e\u003cp\u003e\u003cb\u003eProcedure\u003c/b\u003e\u003c/p\u003e\u003cp\u003eInitially, the instrument creators were contacted to obtain their authorization for use. In parallel, the content adaptation and validation process was carried out with the support of two or more subject matter experts who evaluated the instruments.\u003c/p\u003e\u003cp\u003eAfter completing the expert validation, a pilot test was implemented to identify any necessary adjustments to the protocol. The identified changes were incorporated to ensure participants understood the protocol. Subsequently, a digital version of the protocol was created using Google Forms, structured in a logical sequence: first, informed consent, followed by the collection of sociodemographic data, and finally, the measurement instruments (following the order specified in the \"measurement\" section). The form was programmed to show the questionnaires only to those who accepted informed consent; otherwise, it was automatically closed. Distribution was carried out via a QR code that directed the digital form, using both in-person methods and social media dissemination. The data collection staff briefly explained the study's purposes to potential participants and shared the QR code with those who agreed to participate, without supervision during the response process.\u003c/p\u003e\u003cp\u003eOnce the collection phase was completed, the database was exported for statistical processing. The process culminated with the interpretation of the findings within the framework of the adopted theoretical foundation, formulating conclusions that highlighted both the conceptual contributions and practical applications of the results obtained.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eStatistical analyses were conducted using RStudio software. A confirmatory factor analysis (CFA) was first performed for each scale, and the internal consistency coefficient (Cronbach's alpha) was also assessed for each dimension. Descriptive statistics were then calculated for all variables. For the structural equation model, the robust maximum likelihood (MLM) estimator was used, which is suitable for correcting for multivariate nonnormality if present (Satorra \u0026amp; Bentler, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Model fit was verified using the CFI, TLI, RMSEA, and SRMR indices, using the ideal parameters proposed by Hu and Bentler (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) as a guide: CFI and TLI (\u0026ge;\u0026thinsp;0.90), and RMSEA and SRMR (\u0026le;\u0026thinsp;0.08). The R\u0026sup2; of the dependent variables corresponding to academic self-efficacy, academic performance, and satisfaction was also calculated.\u003c/p\u003e\u003cp\u003eThen, the configural, metric, scalar, and strict invariance analyses were performed, taking into account the following ideal parameters: ΔCFI\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ΔSRMR\u0026thinsp;\u0026lt;\u0026thinsp;0.025, and ΔRMSEA\u0026thinsp;\u0026lt;\u0026thinsp;.005 (Protzko, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). If the invariance indices are met, the multigroup structural equation model analyses will be performed to evaluate the model's behavior according to the students' sex.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe descriptive statistics of the variables used for the analysis of the model are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of the model variables.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSkewness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eKurtosis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAutonomy Support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60.420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.231\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.139\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMetacognitive Self-Regulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.262\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAcademic Self-Efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.654\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.847\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSatisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.834\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAutonomy Support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60.656\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMetacognitive Self-Regulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44.836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.446\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.683\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAcademic Self-Efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.321\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.416\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSatisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.949\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.956\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAutonomy Support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMetacognitive Self-Regulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAcademic Self-Efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.206\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSatisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.735\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the fit indices of the hypothesized model, which are within the parameters recommended by Hu and Bentler (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1999\u003c/span\u003e): CFI and TLI (\u0026ge;\u0026thinsp;0.90), and RMSEA and SRMR (\u0026le;\u0026thinsp;0.08).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHypothesized model fit indices\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eModel\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eX\u0026sup2;\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eS-B χ2\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTLI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSRMR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothesized model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1116.902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.041 [0.038\u0026ndash;0.044]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, not all hypothesized relationships were significant within the overall model. Among the variables that were found to be predictors of academic self-efficacy were the autonomy-supporting climate (β\u0026thinsp;=\u0026thinsp;0.319, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and metacognitive learning strategies (β\u0026thinsp;=\u0026thinsp;0.271, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). The variables that were found to be predictors of academic satisfaction were academic self-efficacy (β\u0026thinsp;=\u0026thinsp;0.237, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and the autonomy-supporting climate (β\u0026thinsp;=\u0026thinsp;0.159, p\u0026thinsp;\u0026lt;\u0026thinsp;.005). The only variable that was found to be a predictor of academic performance was the autonomy-supporting climate (β\u0026thinsp;=\u0026thinsp;0.159, p\u0026thinsp;\u0026lt;\u0026thinsp;.01).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eList of relationships of the hypothesized model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePath\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ez-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutonomy Support \u0026rarr; Self Efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetacognitive Self-Regulation \u0026rarr; Self Efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutonomy Support \u0026rarr; Grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetacognitive Self-Regulation \u0026rarr; Grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.545\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf Efficacy \u0026rarr; Grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.215\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutonomy Support \u0026rarr; Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetacognitive Self-Regulation \u0026rarr; Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.393\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf Efficacy \u0026rarr; Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the ΔCFI indices are less than 0.01 at all levels, which indicates that, according to Protzko (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), they are adequate to accept invariance. Likewise, the ΔSRMR scores are less than 0.025, which indicates that invariance exists (Protzko, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Finally, the ΔRMSEA scores are less than 0.005, which also indicates that invariance exists (Protzko, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These results allow for the performance of a multigroup structural equation model.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInvariance of the model according to sex\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInvariance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eX\u0026sup2;\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSRMR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eΔCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eΔSRMR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eΔRMSEA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConfigural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2445.314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.94493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.05369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.04118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetric\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2489.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.94351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.05793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.04117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.00142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.00001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScalar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2525.822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.94191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.05835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.04118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.00160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.00001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStrict\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2549.363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.94643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.055836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.03899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.00219\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, not all hypothesized relationships were significant for the analysis of the model by sex. As in the general model, the predictor variables of academic self-efficacy turned out to be the autonomy-supporting climate and learning strategies, both in women (β\u0026thinsp;=\u0026thinsp;0.195, p\u0026thinsp;\u0026lt;\u0026thinsp;0.027; β\u0026thinsp;=\u0026thinsp;0.371, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and men (β\u0026thinsp;=\u0026thinsp;0.431, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; β\u0026thinsp;=\u0026thinsp;0.182, p\u0026thinsp;=\u0026thinsp;.010), respectively. In the case of the predictor variables of academic satisfaction, one of them turned out to be academic self-efficacy (β\u0026thinsp;=\u0026thinsp;0.343, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) only in the case of women and the autonomy-supporting climate (β\u0026thinsp;=\u0026thinsp;0.248, p\u0026thinsp;=\u0026thinsp;.005) only in the case of men. Finally, the predictive variables of academic performance turned out to be academic self-efficacy (β\u0026thinsp;=\u0026thinsp;0.184, p\u0026thinsp;=\u0026thinsp;.027) and the autonomy support climate (β\u0026thinsp;=\u0026thinsp;0.318, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) only in the case of women.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePath\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAutonomy Support \u0026rarr; Self Efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMetacognitive Self-Regulation \u0026rarr; Self Efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAutonomy Support \u0026rarr; Grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.225\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMetacognitive Self-Regulation \u0026rarr; Grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.419\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf Efficacy \u0026rarr; Grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.829\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAutonomy Support \u0026rarr; Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMetacognitive Self-Regulation \u0026rarr; Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.387\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf Efficacy \u0026rarr; Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAutonomy Support \u0026rarr; Self Efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMetacognitive Self-Regulation \u0026rarr; Self Efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAutonomy Support \u0026rarr; Grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMetacognitive Self-Regulation \u0026rarr; Grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.672\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf Efficacy \u0026rarr; Grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAutonomy Support \u0026rarr; Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.211\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMetacognitive Self-Regulation \u0026rarr; Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf Efficacy \u0026rarr; Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAs expected, an autonomy-supportive climate predicted a higher sense of academic efficacy in students of both sexes (H1), a result that has also been supported by previous research with mixed samples (Guti\u0026eacute;rrez \u0026amp; Tom\u0026aacute;s, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kingsford-Smith et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ito et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). An autonomy-supportive climate is characterized by an environment in which the teacher trusts the capacities, skills, and potential of each student (N\u0026uacute;\u0026ntilde;ez \u0026amp; Le\u0026oacute;n, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ryan \u0026amp; Deci, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This trust translates into pedagogical practices that respect and promote student autonomy, providing them with opportunities to make decisions, assume responsibilities, and express their own voice in the learning process (N\u0026uacute;\u0026ntilde;ez \u0026amp; Le\u0026oacute;n, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ryan \u0026amp; Deci, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). When students perceive that their teacher believes in them and considers them capable of acting autonomously, a process of internalization of external recognition is activated; That is, external support and appreciation strengthens beliefs about self-efficacy or perceived competence in those who receive it (Bandura, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Li \u0026amp; Singh, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Peifer et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rader et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rodriguez et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). From this perspective, external recognition plays a key role in self-validation and in developing a more positive perception of one's own abilities (Bandura, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Rader et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). From self-determination theory, it is proposed that a climate of autonomy support encourages students to trust their own abilities and potential more, which satisfies their basic psychological need for competence and strengthens their sense of academic self-efficacy (Bandura, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Germani \u0026amp; Palombi, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ryan \u0026amp; Deci, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Slemp et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Likewise, metacognitive strategies were also found to predict better self-efficacy in STEM careers (H2), in both men and women. This suggests that when students develop skills to plan, monitor, and regulate their own learning process, they also strengthen their perception of competence and ability to face academic challenges. This finding supports the importance of promoting metacognitive skills in higher education, particularly in STEM fields where the content is often perceived as complex (Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Markovits \u0026amp; Forgasz, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Authors such as Akamatsu et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Khosravi et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) have also documented the predictive role of metacognitive strategies on the improvement of academic self-efficacy. This finding corroborates that metacognitive skills can improve performance and confidence in one's own abilities (Hayat et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn the other hand, an autonomy-supportive climate was found to predict better academic performance in women in STEM fields (H3), but not in their male peers. This finding can be explained based on previous research that has shown that an environment characterized by support for student autonomy favors better academic performance (e.g., Bonem et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Marshik et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mammadov and Schroeder, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This is because such a climate encourages students to study not out of external obligation, but because they manage to find meaning and personal direction in learning (Ryan and Deci, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Thus, students position themselves in the educational process in a more active, profound, and genuine way, which usually translates into better academic performance (Ryan and Deci, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mammadov and Schroeder, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, most of these studies have not considered possible variations in this relationship based on the student's sex. This observed difference in outcomes could be explained, in part, by the fact that in traditionally male-dominated fields such as STEM careers, women tend to feel greater pressure to demonstrate their abilities (Cokley et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Butler, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cheryan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which could lead them to further internalize the desire to obtain good grades as a way of validating their presence in that space.\u003c/p\u003e\u003cp\u003eEvidence suggests that women place greater emphasis on academic performance (Kuśnierz et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); however, this goes beyond the STEM field, as it responds to social factors. From an early age, girls are often reinforced for showing responsibility, obedience, and academic commitment, while boys are granted greater tolerance for distraction (Kangethe et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mart\u0026iacute;nez and Gil, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Likewise, some studies have shown that men tend to respond better to external regulations or more controlling incentives to improve their performance (Vecchione et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which could be interpreted as a manifestation that, for them, academic performance is not always among their most internalized goals. In this sense, a context that favors autonomy could especially drive women to obtain better grades, since for many of them this constitutes an important goal (Kuśnierz et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In contrast, in the case of men, achieving high academic performance is not usually an equally marked priority (Kuśnierz et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which would limit the impact of autonomy support on this variable.\u003c/p\u003e\u003cp\u003eAnother hypothesis (H5) proposed for the present study suggests that the use of metacognitive strategies would predict better academic performance (De Boer et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Swanson et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang and Lian, \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, in the present study, no significant relationship was found between both variables. This discrepancy could be explained by the characteristics of the assessment system used in the analyzed context. Several authors have pointed out that, in some cases, mathematics teaching adopts a superficial and mechanistic approach, focusing on repetition and memorization rather than comprehension (Khalid et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Garc\u0026eacute;s-C\u0026oacute;rdova \u0026amp; Font-Moll, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Garc\u0026eacute;s C\u0026oacute;rdova et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mar\u0026iacute;n et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In line with this, Vatterott (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) warns that grading styles that prioritize content memorization over the development of critical thinking and deep understanding still persist. Under these conditions, it is understandable that the use of metacognitive strategies is not necessarily reflected in higher academic performance.\u003c/p\u003e\u003cp\u003eAnother possible explanation is that students who report using metacognitive strategies may not be applying them appropriately or in-depth, which would limit their effectiveness. As Craig et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Berger and Karabenick (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) point out, although self-report instruments are useful for determining the frequency of use of metacognitive strategies, they do not always accurately capture the quality or effectiveness of their application in real-life contexts.\u003c/p\u003e\u003cp\u003eAnother hypothesis proposed was that higher academic self-efficacy would predict better academic performance in STEM students (H5), which was fulfilled but only for women, not for men. In this sense, it is important to note that several studies have shown that academic self-efficacy has a moderate, rather than a strong, predictive role in academic performance (Honicke \u0026amp; Broadbent, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yokoyama, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This suggests that, although self-efficacy contributes to academic success, it is not the only determining factor and its influence may vary depending on other elements and contextual factors (Honicke \u0026amp; Broadbent, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yokoyama, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the specific case of women, the relationship between self-efficacy and academic performance has particular characteristics. In the context of STEM careers, women often face negative stereotypes and may feel greater pressure to demonstrate their abilities, which could create a stronger connection between their self-efficacy and their academic performance (Cheryan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Riegle-Crumb \u0026amp; Peng, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor women, confidence in their own abilities appears to be a fundamental driver of their performance, possibly as a way to validate their belonging in these academic spaces (Lv et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang \u0026amp; Yu, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In contrast, for male students, academic performance appears to be influenced by other factors not considered in this model. This could reflect the fact that, being in an environment where their presence is predominant and culturally expected, they do not experience the same need to validate their abilities through grades (Kuśnierz et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn the other hand, it was proposed that satisfaction is positively predicted by an autonomy-supportive climate (H6), exploring possible differences based on students' gender. It was found that this climate predicted greater academic satisfaction in male students in STEM fields, but not in female students. This finding is especially interesting considering that several previous studies have shown that an autonomy-supportive climate tends to predict greater academic satisfaction (Barrientos-Illanes et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Froment et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), as it promotes an environment in which students can make decisions about their own learning process, aligning it, as much as possible, with their personal interests and needs (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; N\u0026uacute;\u0026ntilde;ez \u0026amp; Le\u0026oacute;n, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This possibility of self-direction contributes to a more meaningful and satisfying educational experience. The difference found in this study could be explained, in part, by a habituation phenomenon, which would cause men and women to develop different emotional responses to this type of climate (Galak and Redden, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Klausen et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). From a sociocultural perspective, women are often trained to develop a greater orientation toward autonomy, due to social norms and cultural expectations that reinforce self-regulation, responsibility, and self-management in them (Lenes et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Modrek et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is important to clarify that, although autonomy is generally perceived as something positive, the fact that women have been socialized towards it does not necessarily imply greater freedom to make decisions. Rather, it responds to the traditional assignment of care and service roles, which require them to be self-sufficient in supporting and caring for others (del R\u0026iacute;o-Lozano et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Tavero et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As a result, an autonomy-supportive academic environment, while it may favor their performance, does not necessarily increase their academic satisfaction, since it does not represent a significant change from what they already know (Lenes et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Modrek et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn the other hand, for many boys, who may have been socialized under frameworks that prioritize external control, this type of climate may be more novel and rewarding (Vecchione et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, the perception of an environment that validates their personal initiative, something they are not used to, may generate a more marked positive emotional response, translating into higher levels of academic satisfaction.\u003c/p\u003e\u003cp\u003eThe seventh hypothesis suggests a positive relationship between metacognitive learning strategies and satisfaction (H7). Contrary to expectations, metacognitive strategies did not predict academic satisfaction in any of the groups studied. This result contradicts some previous research (Jeong \u0026amp; Park, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Koyuncuoglu, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and could be related to the characteristics of the educational system. The use of these strategies may not be adequately valued and rewarded in prevailing assessment methods, which may focus more on memorization than on deep comprehension and critical thinking (Khalid et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Garc\u0026eacute;s-C\u0026oacute;rdova \u0026amp; Font-Moll, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this context, the effort invested in developing and applying these strategies does not necessarily translate into a more satisfying academic experience (Vatterott, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eKoca et al. (2014) mention that while students who develop awareness and control over their cognitive processes may experience some improvement in their academic satisfaction, the magnitude of this effect is so small that it does not represent a relevant determining or predictive factor for overall satisfaction with the university experience; that is, the direct impact of metacognitive skills on satisfaction is limited (Koca et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Likewise, Norman (2020) argues that metacognition will not necessarily always predict greater satisfaction, since in some cases where metacognition involves a negative self-assessment, it can harm emotional well-being, including, within this well-being, the perception of satisfaction. Academic satisfaction is an affective-emotional construct that depends on broader factors such as the educational environment, social relationships, the fulfillment of personal expectations, and the general psychological well-being of the student (Fern\u0026aacute;ndez-Zabala et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Although metacognitive skills can improve performance and confidence in one's abilities (Hayat et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), they do not necessarily guarantee that the student will experience greater well-being or satisfaction with their overall educational experience, since the latter responds to subjective and contextual dimensions that transcend the purely cognitive processes of planning, monitoring, and evaluating learning (Norman, 2020).\u003c/p\u003e\u003cp\u003eFinally, academic self-efficacy was found to be a predictor of academic satisfaction only in female students (H8). This finding is consistent with previous research that has found self-efficacy to be an important predictor of academic satisfaction (Lent et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Morelli et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)). For female students in STEM fields, feeling capable and competent in their field of study is critical to their well-being and satisfaction at university (Chahal et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tian et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In an environment where they are underrepresented and where they may face implicit or explicit questions about their abilities (Ovink et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yang \u0026amp; Shen, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), self-confidence appears to play a crucial role in how they experience and enjoy their academic training.\u003c/p\u003e\u003cp\u003eFor male students, academic satisfaction appears to be linked to factors other than self-efficacy. This could reflect different priorities or sources of satisfaction, possibly related to aspects such as social recognition or perceived professional opportunities (Vecchione et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This gender difference was also found by Koca et al. (2023), who found that academic self-efficacy increased academic satisfaction scores only in the case of female students. In contrast, this effect of academic self-efficacy on satisfaction does not operate in the same way in males.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe findings of this study suggest that both the autonomy-supportive climate and metacognitive strategies have a significant impact on academic self-efficacy in STEM students, regardless of gender. However, the role of climate and self-efficacy in academic satisfaction and performance varies by gender.\u003c/p\u003e\u003cp\u003eIn women, both the autonomy-supportive climate and self-efficacy predicted academic performance. In contrast, neither of these variables predicted performance in men, which could be because men are accustomed to improving their performance based on more external or controlling incentives (Vecchione et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), or because they do not experience the need to validate their self-efficacy through grades (Kuśnierz et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These results may reveal that the symbolic value assigned to academic achievement differs between genders: for women, it may represent an important achievement, while for men, it may not carry the same subjective or symbolic weight (Kuśnierz et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These differences raise valuable questions about how men and women construct their academic identity.\u003c/p\u003e\u003cp\u003eFurthermore, while self-efficacy predicted greater academic satisfaction in women, this relationship was not significant in men. This could be because, being underrepresented in STEM fields, women find self-efficacy an important source of validation to sustain satisfaction (Chahal et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ovink et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In contrast, for men, it was an autonomy-supportive climate that predicted academic satisfaction. One possible explanation is that this type of climate represents a novel experience for them, having been socialized in contexts where external control and direction predominate (Vecchione et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). For women, however, this type of support may be so normalized that it fails to elicit a significant emotional response (Lenes et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Modrek et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These differences invite reflection on how gender trajectories shape the academic experience.\u003c/p\u003e"},{"header":"6. Recommendations and Future Directions","content":"\u003cp\u003eFor future research, it is suggested that self-perceived learning be included as a complementary variable to performance, as the latter could be mediated by evaluation criteria focused on memorization, which would not accurately reflect meaningful learning (Vatterott, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fearnley et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Anthonysamy \u0026amp; Singh, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Vallet-Bellmunt et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFinally, although the discussion suggests that men may be more oriented toward responding to external stimuli, this finding should not be interpreted as an invitation to foster controlling practices. On the contrary, Self-Determination Theory emphasizes the importance of cultivating autonomous and intrinsic motivation for all students, as this fosters deeper and more sustained learning over time (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhang \u0026amp; Lian, \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIt is also suggested that future research compare these findings with what occurs in traditionally feminized careers in order to explore whether the differences found remain the same or vary depending on the type of career and the gender stereotypes associated with it.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Pontificia Universidad Católica del Perú under grant CAP PI-1139.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical standards of the Ethics Committee of the Pontifical Catholic University of Peru. Ethical approval was obtained under approval number 085-2024-CEI-CCSSHHyAA/PUCP, prior to data collection.\u003c/p\u003e\n\u003cp\u003eAll participants provided informed consent to participate in this study. Participation was voluntary, and confidentiality and anonymity were guaranteed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets and instruments generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed W, Mudrey RR. The role of motivational factors in predicting STEM career aspirations. 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Educational Psychol. 2020;41:467\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/01443410.2020.1813690\u003c/span\u003e\u003cspan address=\"10.1080/01443410.2020.1813690\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"STEM education, Academic self-efficacy, Autonomy-supportive climate, Metacognitive strategies, Gender differences in higher education","lastPublishedDoi":"10.21203/rs.3.rs-7377511/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7377511/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSTEM education has gained greater relevance in recent years, particularly due to the difficulties in managing the academic demands of these careers. Another significant challenge is that in a predominantly male environment like that of STEM fields, the academic experience of female students is often not the same as that of their male peers. In response to these problems, this research aims to analyze whether educational factors such as the use of metacognitive strategies and an autonomy-supportive climate influence self-efficacy and, in turn, how these factors impact student performance and satisfaction. Furthermore, it aims to assess whether the relationships within this model vary by sex using a multigroup SEM analysis. In the results, the hypothesized general SEM model demonstrated good fit indices. The model also met invariance criteria,, which allowed a multigroup structural equation model to be performed. The predictors of academic self-efficacy were found to be the autonomy-supportive climate and learning strategies for both sexes. Among the predictors of academic satisfaction, self-efficacy was found only for women, and autonomy-supportive climate was found only for men. Finally, self-efficacy and autonomy-supportive climate were found to be predictors of performance only for women. The results open valuable questions about how academic variables differentially shape the academic experience of men and women in STEM careers, with possible cultural explanations.\u003c/p\u003e","manuscriptTitle":"Gender differences in STEM careers: autonomy-supportive climate, metacognitive strategies, self-efficacy, and their impact on academic performance and satisfaction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-24 18:46:27","doi":"10.21203/rs.3.rs-7377511/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-30T06:20:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-29T19:11:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-26T16:21:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110536069183307457058468371647840120700","date":"2025-09-23T18:59:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"158084597929916388607053397448644327891","date":"2025-09-18T10:09:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-16T06:53:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-20T13:22:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-18T02:29:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-18T02:28:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2025-08-15T01:26:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f278ed95-e9bd-4ec5-b783-74f742f6d4ca","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T06:10:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-24 18:46:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7377511","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7377511","identity":"rs-7377511","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}