Quantitative evaluation of a theoretical-conceptual model based on affective and socio-behavioral dimensions to explain the academic performance of mathematics students

Objective: There is evidence that suggests that affective dimensions, personality traits, as well as students’ cooperative interpersonal interactions, are an important element in the students learning process. In this work we propose a theoretical model, based on evidence, that shows the direct and indirect relationships between these factors and academic performance in mathematics courses, in undergraduate and school students.

Methods: To understand the type of relationships between these variables, the PANAS psychometric test of positive and negative affect, the BIG FIVE personality test and the economic decision game DUPLES GAME were applied. The study sample was 130 students between 17 and 22 years of age from undergraduate and school (M ± SD = 20.1 ± 3.99).

Results: From a path analysis, statistically significant relationships were found, for example, a direct relationship between neuroticism and positive affect, which in turn is related to academic performance. We also found a direct relationship between neuroticism and negative affect, extraversion and positive affect. This allows us to propose that some of the independent variables of the model directly and indirectly influence the academic performance of students in the subject of mathematics.

Conclusion: Positive affect and negative affect directly affect academic performance in mathematics, neuroticism has a direct impact on negative affect and extraversion direct impact on positive affect. Consequently, there are direct and indirect relationships between personality traits and affective dimensions, which affect the academic performance of mathematics students.

Introduction

In the teaching of mathematics in school and undergraduate contexts, manifestations of aversion and unpleasant memories have been seen in students (Orjuela et al., 2019). There are students who say they feel intimidated (Maninat, 2021), generating massive copies, impersonations, and bad practices (Goldin et al., 2016).

Furthermore, there is evidence that suggests different associations between the learning obtained, reflected in academic performance, which is influenced by affective dimensions of students, their personality traits and the interactions that occur among them in educational contexts. Regarding affective dimensions, positive affect reflects the point to which a person feels active, alert, and energetic (Watson et al., 1988), anticipating rewarding experiences in those who experience them (Sánchez et al., 2008), which favors creativity and motivation (Schmidt, 2008). As well as, negative affect represents a general dimension of distress and unpleasant involvement, including a variety of aversive emotional states such as anger and fear (Watson et al., 1988). In this line, a relationship between affective dimensions and mathematics learning has been demonstrated, for example, in front of an exercise, where students can connect with the emotional experience they have had previously (Gómez, 2016). Thus, when a higher level of abstraction is required, as in algebraic factorizations or calculus in general, triggers of negative emotions have been evidenced (Dorier, 1991; Cerda et al., 2016), where the most frequent states are frustration and confusion, marked by boredom and anxiety (Di Leo et al., 2019).

Regarding mathematics anxiety, it is inversely related to results on mathematics tests (Hembree, 1990), presenting a significant impact on academic performance (Caviola et al., 2021). Furthermore, this inverse relationship between mathematics anxiety and academic performance begins in childhood and remains significant until adulthood (Barroso et al., 2021). There is even a special behavior because poor performance in mathematics can cause mathematics anxiety and, at the same time, mathematics anxiety reduces academic performance (Carey et al., 2016). Along these lines, it is necessary to specify that academic performance in mathematics in Chile corresponds to the arithmetic average calculated with all summative evaluations, while general performance is calculated with all subjects. Specifically in algebra, these difficulties are produced by a higher complexity due to the epistemological nature of these subjects, characterized by the requirement of many new and abstract concepts, together with an absence of graphical examples or geometric associations that facilitate their understanding (Oktaç and Gaisman, 2010; Costa and Rossignoli, 2017). Furthermore, there is a positive effect when there is a good predisposition towards the development of mathematical exercises which in turn reduces anxiety towards learning these elements (Akin and Kurbanoglu, 2011). Thus, the enjoyment of mathematics presents (mild) positive effects on subsequent perceived effort and beliefs about mathematical competence (Pinxten et al., 2014). Likewise, beliefs about mathematical competence have positive effects on mathematical performance and negative effects on the perceived expenditure on mathematical effort (Pinxten et al., 2014).

Consequently, due to the existence of mathematical contents that present greater and lesser difficulty to understand and assimilate (Castro et al., 2020), negative affect is associated with a lower level of achievement and positive affect with a higher one (Pekrun et al., 2017). Additionally, there are factors that contribute to predict academic performance such as those that reflect the development of cognitive abilities together with those that refer to individual characteristics such as personality traits (Ackerman and Beier, 2006). Personality traits correspond to attributes that show associations with important activities and phenomena on an individual and social scale, such as work and school performance (John et al., 1994). Therefore, personality traits allow predicting behaviors of various types such as academic performance (Cupani et al., 2013) even being statistically significant predictors (O’Connor and Paunonen, 2007), so that personality traits have an influence on students’ academic performance (Rothstein et al., 1994). Regarding extraversion, a personality trait characterized by a high display of activity, sociality, and expressiveness (Benet and John, 1998; Diener et al., 2003), there is evidence that shows both a positive and a negative relationship with academic performance.

There are studies that show that more extroverted students have lower academic performance (Goff and Ackerman, 1992; Bauer and Liang, 2003; Furnham et al., 2003), which could be explained by differences in the study time spent by students with different levels of extraversion. In this regard, one study showed that less extroverted students spend more time studying, while more extroverted students spend more time socializing (Chamorro and Furnham, 2005). In contrast, Rothstein et al. (1994) reported that extraversion is positively correlated with students’ class participation which would generate a positive attitude towards learning (De Raad and Schouwenburg, 1996). Specifically, extroverted students differ significantly from introverted students in higher performance on summative assessments involving exercises in which logical reasoning is required (Furnham et al., 1998). Regarding agreeableness, Furnham and Chamorro (2004) suggest that it is a personality trait that would not be associated with academic performance. However, positive relationships with academic performance have been found (Farsides and Woodfield, 2003; Conard, 2006) as well as negative ones (Rothstein et al., 1994; Paunonen, 1998). Given the above, the favorable or unfavorable relationships of this personality trait with respect to performance are not yet entirely conclusive. As for conscientiousness, the explanatory power of this trait is due to the motivational properties reflected in effort and persistence (Chamorro and Furnham, 2005). That is, it is likely that those students who are more organized and self-disciplined achieve better academic results compared to those who do not possess these characteristics (Cupani et al., 2013).

Neuroticism, a personality trait referred to emotional instability and excessive worry with high degrees of anxiety, is associated with cognitive abilities (Eysenck, 1967), as well as with self-efficacy beliefs, a predictive variable of academic performance, so the effect of this personality trait could be indirect and mediated by other variables (Judge and Ilies, 2002). Finally, openness to experience, a personality trait that has been interpreted in terms of capacity (Johnson, 1994; Chamorro and Furnham, 2005), is characterized by creativity, originality, and exploration of the unknown, which would allow inferring a relationship with the levels of achievement of the activities proposed (Ackerman and Heggestad, 1997) and with academic performance in children and adolescents independent of intelligence (Cupani et al., 2013; Poropat, 2014; Zhang and Ziegler, 2015). Thus, the importance of these elements is because the influence of personality traits on academic performance in various subjects, such as mathematics, have been demonstrated (Spinath et al., 2010; Zhang and Ziegler, 2015). Thereby, extraversion presents a certain correspondence with positive affect and neuroticism with negative affect (Watson and Pennebaker, 1989; Clark et al., 1994), where neuroticism is a personality trait reflecting low stability and high emotional dysregulation (Benet and John, 1998; Del Valle et al., 2020), which is associated with a tendency to experience negative emotions (Matsumoto, 2006; Nettle, 2006; Austin et al., 2008; Reisenzein and Weber, 2009; Andrés et al., 2014; Pollock et al., 2016). Considering this evidence, interactions between affective components and some personality traits of individuals are visualized.

Cooperation among students is of interest in mathematics learning because peer interactions occur in a classroom, where collaborative problem-solving fosters social interaction and is positively related to academic outcomes (Tudge et al., 1996). This occurs, for example, when a student with lower levels of academic achievement is offered active participation, with explanations and stimuli elaborated by a higher-achieving peer (Fuchs et al., 1994). These interactions can facilitate engagement and the development of intellectual skills toward study because of the option to coach higher achievers in cooperative strategies (Fuchs et al., 1994). Thus, active classrooms have fostered greater interaction among students over a traditional lecture-based format (Pulgar et al., 2020). This interaction has allowed observing a significant association of social relationships among peers with academic performance (Candia et al., 2022; Pulgar et al., 2022). Accordingly, a significant association between students’ social relationships and their academic performance at different ages is highlighted in the literature (Baldwin et al., 1997; Caprara et al., 2000; Bruun and Brewe, 2013; Ivaniushina and Alexandrov, 2018; Berthelon et al., 2019; Candia et al., 2019; Pulgar et al., 2020). Complementarily, it is possible to note a link between some personality traits and behavioral attributes that are part of the cooperative behavior evidenced by students in different contexts. Conscientiousness is a personality trait characterized by impulse control and task-oriented and goal-oriented behavior (Benet and John, 1998). It is thought that students with high conscientiousness are more organized and present greater motivation for good academic results (Martínez De Ibarreta et al., 2011). Considering this background, it is possible to infer that students with a high level of this personality trait present low levels of cooperation with other students who have minimal levels of conscientiousness, when facing academic challenges. For example, Komarraju and Karau (2005) studied personality traits in relation to academic performance and found that students with higher conscientiousness scores presented greater competence, and therefore less cooperation, with their peers. Regarding agreeableness, Tobin et al. (2000) indicate that people with a more marked presence of this trait control their negative affect and make more effort to please others. Alternatively, agreeableness is a personality trait that includes characteristics such as altruism, in addition to a prosocial and community orientation towards others (John et al., 2011), so it could be linked to cooperative-type interpersonal interactions among students. Furthermore, personality traits have been studied in children and adolescents from third-party evaluations (Digman and Takemoto-Chock, 1981; John et al., 1994) but with little evidence from measures in people with an age group between ages 15 and 19 (Romero et al., 2002).

For educational mathematics, the study of the affectivity present in mathematics has been a subject of research since the seventies. Becker (1983) provides an overview of emotional reactions to mathematics which has led to the emergence of theoretical models such as the one proposed by Buxton (1981) in his book ‘Do you panic about mathematics?’ With this background, McLeod (1988) integrates the emotional responses of students working with routine mathematical problems, which were considered important motivating forces due to positive or negative reactions that could arise. These works in the field of educational mathematics made it possible to “build an approach to affect in a theoretical framework based on cognitive science” (McLeod, 1994, p. 642). However, these studies paid little attention to affect and the types of personality traits and interactions in the classroom, which establishes the need to develop a more integrated approach to the investigation of affectivity and other dimensions of personality and behavior in the classroom. Mathematics, theoretical elements that have served us for the construction of the theoretical-conceptual model that we propose here.

Current study

The previous antecedents show the incidence of affectivity, personality, and socio-behavioral traits in the learning of mathematics; but there is no analysis that explores the interrelationships between these variables and academic performance in mathematics. Thus, the aim of our study is to establish the link between academic performance in mathematics and affective dimensions, personality traits and cooperative behaviors, as well as the associations between these variables, in students in higher education and school education. In this way, knowledge is provided for future curricular innovations that incorporate these dimensions.

Materials and methods

Participants

Data were obtained from a sample of undergraduate students taking the subject “Mathematics 3” at the Universidad Andrés Bello, at the Santiago and Viña del Mar campuses, and third year students of Colegio San Adrián de Quilicura, both institutions located in Chile. This sample was formed due to the failure and dropout rates that existed in both institutions, along with comments rejecting the subject. Furthermore, certain stability has been seen in personality traits in young people of these ages (Abella and Bárcena, 2014), which is why these students of these ages were chosen.

Thus, they were chosen at random three courses of students of the Commercial Engineering career at Universidad Andrés Bello, in which the subject “Mathematics 3” was taught, due to the fact that students frequently recognize a high level of difficulty in passing this subject (personal communication), which is also corroborated with the existing failure rate (e.g., 39% in 2016; 37% in 2017; 42% in 2018; 27% in 2019). Furthermore, based on what the school authorities reported (personal communication), based on complicated and difficult content, such as basic elements of trigonometry, as well as arithmetic work with functions, a third-year course was chosen at random. San Adrián School. This course teaches elementary mathematical content required for the aforementioned university subject. For this reason, there are curricular similarities between school and university mathematics content, in addition to there being no considerable difference in age, reasons that justify the selection of these study groups. The sample consists of 130 students (75.3% male) with $M\pm SD=20.1\pm 3.99$ , as shown in Table 1.

Table 1

Table 1. Number of research participants.

Ethical authorization

The study was conducted in accordance with the Declaration of Helsinki. The protocol for data collection in students was approved by the Ethics Committee of the University of Santiago de Chile code 258, prior to the application of the instruments, for which informed consent and assent was also obtained from all participants and guardians of participants in this research. Data collection was carried out between August and October 2022, in university students, and during November of the same year in school students.

Psychometric measurement of positive and negative affect

The Positive Affect and Negative Affect Schedule (PANAS) instrument was applied, widely used for recording emotions, which consists of a set of 20 words describing feelings and emotions, corresponding to the subdimensions Positive Affect (PA) and Negative Affect (NA), but this information is unknown to the respondents.

The version used of said instrument was adapted by Dufey and Fernandez (2012) for Chilean students, with the following levels of internal consistency measured by means of Cronbach’s alpha indicator: $\alpha =0.79$ in men and $\alpha =0.73$ in women, for the PA subdimension, and $\alpha =0.79$ in men and $\alpha =0.83$ in women, for the NA subdimension. These subdimensions present low correlations and no statistical significance, so that both scales are orthogonal in the assessment of affectivity (Dufey and Fernandez, 2012). Students were asked to read each of the words and choose an option according to how they generally feel, assigning a number from 1 to 5 according to the following detail: 1 = “very slightly or not at all,” 2 = “a little,” 3 = “moderately,” 4 = “quite a lot,” 5 = “extremely.”

Psychometric measurement of personality traits

The Big Five personality measure (John et al., 2011), in its Spanish version (Benet and John, 1998), was also applied. The instrument consists of 44 items that assess dimensions defined on a 5-point Likert scale (1 = “strongly disagree”; 5 = “strongly agree”). Five personality factors are measured: Extraversion; Agreeableness; Conscientiousness; Neuroticism and Openness to Experience (Benet and John, 1998).

Economic game

To measure the cooperation or cooperative tendency of the students, the “Duples Game” was implemented using an online interface through computers connected to the network (Candia et al., 2022). Student groups were formed with a maximum of 6 participants, where each of the members played with the rest of the group members, forming pairs for each round of the game. In each of these rounds, each student had 10 chips to distribute, where he had to decide how many to give to the person he was playing with and how many to keep (e.g., he could give all the chips and keep none, or give 2 chips and keep 8, etc.). At the end of the game, the amount of chips collected by each participant was converted to real Chilean money (each chip was equivalent to $50 Chilean pesos), therefore, the final profit of each participant depended on the amount of chips donated, not donated, and received.

Measurement of academic performance

Subsequently, each student was given a summative evaluation of mathematics on a numerical scale with a decimal from 1 to 7, according to the educational level in which he/she was. Thus, the test applied in the university course was prepared by the teaching team that taught the course, while the test applied in the school course was prepared by the team of teachers belonging to the high school level.

Based on the literature review, a theoretical model was constructed that predicts direct and indirect relationships between independent variables and the academic performance obtained in a mathematics evaluation, as shown in Figure 1. This theoretical model serves as a basis for future replication studies, essential for the testing and verification of this proposed model.

Figure 1

Figure 1. Direct and indirect relationships of variables.

Statistical analysis

To test our predictions, a path analysis (Bollen and Curran, 2006) was performed to statistically evaluate the relationships between variables presented in the theoretical model (Figure 1) through structural equation modeling (Ruiz et al., 2010). SPSS Statistics 25 software (IBM) was used together with the AMOS extension. We considered the qualification obtained in the mathematics test as the dependent variable (academic performance) and the dimensions positive and negative affect, cooperation, and personality traits as independent variables. In addition, in the model we controlled for the establishment and the biological sex reported by the students. A significance level of $\alpha =0.05$ was established.

Results

Different statistically significant relationships were found in the pre-established direction according to the theoretical model, as shown in Table 2 ( $\ast \ast \ast p<0.001$ ) and Figure 2. Also, we use an ML as a maximum likelihood estimation model. These relationships confirm the existence of variables that directly and indirectly affect academic performance in mathematics. The variables that show statistically significant direct relationships with mathematics performance are positive affect, extraversion, diligence, negative affect. In addition, the variables that mediate the impact on mathematics performance are extraversion and neuroticism. We did not find differences according to educational level in personality traits, affective dimensions, and cooperation. The model variables are the following: Extraversion ( $M=3.35,SD=0.47,\alpha =0.26$ ), Agreeableness (M = 3.23, SD = 0.41, α = 0.33), Conscientiousness (M = 3.59, SD = 0.37, α = 0.22), Neuroticism (M = 3.22, SD = 0.41, α = 0.33), Openness (M = 3.48, SD = 0.47, α = 0.52), PA (M = 32.02, SD = 8.22, α = 0.84), NA (M = 26.34, SD = 9.61, α = 0.86), Cooperation ( $M=4.95,SD=2.44$ ).

Table 2

Table 2. Level of significance of the relationships involved.

Figure 2

Figure 2. Statistical significance level of direct and indirect variables.

The academic level presents a positive and statistically significant relationship in academic performance in mathematics, which can be understood due to the accumulation of knowledge in students as they advance in their educational process. However, this variable has not been considered in the theoretical model because we only consider the affective variables, personality traits and cooperation, as seen in Figure 2. There is no data dependence or grouping within the sample, so we discard correlations between classes, that is, there is no serial autocorrelation between the residuals (Aguilar et al., 2012), Durbin-Watson statistic $DW=1.87$ . This is important in studies with SEM procedures where independence of the data is required.

We use the Kolmogórov-Smirnov test (K-S) and we find normal distribution in our variables Academic Performance ( $p=0.218$ ), PA ( $p=0.591$ ), NA ( $p=0.237$ ), Extraversion ( $p=0.359$ ), Agreeableness ( $p=0.323$ ), Conscientiousness ( $p=0.200$ ), Openness ( $p=0.145$ ), Neuroticism ( $p=0.257$ ). Also, we use the Levene test and we find that there is homogeneity of variance ( $p=0.445$ ). The correlations between the variables of the model are seen in the Figure 3.

Figure 3

Figure 3. Correlation matrix.

Discussion

From our results, the direct relationship of PA with academic performance, as well as the inverse relationship of NA with academic performance, coincides with the results obtained in another research (Watson et al., 1988; Sandín et al., 1999). During learning and exercising in mathematics, interactions between the affective and cognitive are developed (McLeod, 1994), which leads the student to connect with the affective experience he/she has presented in his/her academic journey (Gómez, 2016). NA is related to physical stress and anxiety symptoms (Toro et al., 2018) elements of importance in the process of teaching and learning mathematics. In the understanding of contents that require a higher level of abstraction such as algebra, polynomials or algebraic management of functions, negative emotions arise in students who in many cases experience mathematical anxiety (Klein et al., 2019), which supports the idea that mathematics is difficult to learn regardless of the student’s educational level (Castro et al., 2020). Regarding PA, its association with the occurrence of positive events in interpersonal relationships has been documented (Hamilton et al., 2017). This is relevant because it could generate a transit towards these emotions by controlling the effects of emotional dysregulation based on fear and anxiety (Klein et al., 2019), even more so when in PA there is a component of importance in the study of mathematics such as alertness (Watson et al., 1988).

Furthermore, we found significant relationships between positive affect with extraversion and negative affect with neuroticism that coincide with another research (Watson and Pennebaker, 1989; Clark et al., 1994). This relationship is explained by a high conceptual congruence evidenced in some definitions of temperament (Clark and Watson, 2008), which positions extraversion and neuroticism as two factors linked to affectivity from a genetic basis (Bouchard and Loehlin, 2001) and biological substrates (Depue and Collins, 1999). Thus, neuroticism and extraversion are strongly associated with individual differences in affective experience (Watson et al., 1988; Meyer and Shack, 1989), which in turn allows evidence of systematic links between the neurobiological bases of mood, emotions, and temperament (Clark and Watson, 2008). Additionally, there are no collinearity problems from the VIF values ( $PA=1.153;NA=1.137)$ . Additionally, there is a conceptual similarity between neuroticism with NA, and extraversion with PA, which is observed in the moderate correlations that we found between these variables.

On the other hand, it has been shown that cognitive ability, measured through cognitive aptitude, often has a weaker relationship with academic success in samples of university students (Furnham et al., 2003), as evidenced by the decrease in the variability of scores in summative evaluations (Furnham et al., 2003). One explanation could be that the operationalization of the academic performance variable has been changing (Ackerman et al., 2001), for example, at the university level, greater emphasis has been given to other factors to evaluate performance, such as participation in the classroom (Furnham et al., 2003). Likewise, another likely reason is that there is some scope restriction at the university level as people with less cognitive ability are less likely to apply or be accepted into university courses. Guzmán and Serrano (2011) indicated that the applicants most likely to enter had studied in private primary schools with excellent conditions, had obtained high averages at the time of graduation and had access to cultural and educational resources. Therefore, personality traits could be considered a psychological variable of relevance in predicting academic performance (Ackerman et al., 2001; Furnham et al., 2003), for example, in the relationships between personality traits and the grades obtained in mathematics (Zhang and Ziegler, 2018). Our results show relationships between personality traits and academic performance, which coincides with other research where links between these variables were studied, justified by the importance of perseverance and responsibility in study (Rothstein et al., 1994). Just as a person’s aptitudes reflect what he or she can do, personality traits reflect how he or she can do it (Furnham et al., 2003). Thus, academic performance may be better predicted by obtaining a behavioral profile from personality trait measure than from intelligence tests (Goff and Ackerman, 1992). Thus, the personality measure used in this research has been considered as one of the most comprehensive and promising explanations for the measurement of personality (McAdams and Donnellan, 2009).

The personality traits extraversion and conscientiousness also presented a direct and statistically significant influence on performance, but in a negative and positive way, respectively. In addition, the personality trait extraversion had an indirect impact on academic performance, through its statistically significant and positive influence on the positive affect dimension. This is interesting because methodological strategies could be developed in the classroom that consider these personality traits. Openness to experience, characterized by creative behaviors in unknown contexts (Ackerman and Heggestad, 1997), did not present a statistically significant relationship with academic performance in mathematics. We believe that the characteristics of engineering students, mainly styles of approaching study in a concrete, structured and practical way (Bitran et al., 2004) would serve to understand this result. In an exploratory manner, we found that extraversion in women has a negative relationship with academic performance in mathematics, that is, less extroverted women present higher results.

This is interesting because from socio-epistemological theory, feminist theory, and the theory of social representations, it is evident that girls and women see the possibility of participating in the construction of mathematical knowledge as limited (Simón-Ramos et al., 2022). Thus, it is not surprising that in careers related to mathematics the enrollment of women is 30% (Simón, 2018), evidencing the exclusion that women experience, denying their epistemic authority, making their activities, interests and denigrating their styles of knowledge invisible (Blázquez, 2012).

Along these lines, differences have been reported in mathematical performance between female and male students of different levels (Eccles, 1989), explained by beliefs and conceptions (Andrews and Hatch, 2000), motivation (Middleton and Spanias, 1999) and attitude toward mathematics (McGraw et al., 2006). For the socio-epistemological theory of educational mathematics, the problem focuses on the democratization of learning (Cantoral et al., 2014), since in the traditional classroom, women, and other social groups (ethnic minorities, low-income, disability conditions, etc.) are excluded from the construction of mathematical knowledge for reasons unrelated to their abilities in mathematics.

Regarding cooperation, there is a statistically significant association of certain personality traits such as extraversion, agreeableness, and neuroticism, but they fail to indirectly influence academic performance, since cooperation does not have a statistically significant effect on this variable. When undergraduate and school students who made up the study sample had to decide how much money to give (in the economic game in which they participated), without being certain of receiving money from their peers, or of the amount they would receive, they were faced with the decision to cooperate (contribute money) or not, assuming the possible cost of being left without money in their favor. This was relevant because both educational establishments declare their educational model to be student-centered, where collaborative work is fundamental in their teaching systems.

These results could be understood from the existing risk aversion to a decision that implies an economic cost (Aguado, 2023). Thus, there is an instance in which those who make the decision to cooperate tend not to give importance to the absence of economic retribution from some players because they trust that they will also receive it from some player (Aguado, 2023). The non-existence of an association between cooperation and grade in mathematics could be explained by the characteristics of the graduation profile of a career such as commercial engineering, which the university students in the sample were studying. There are antecedents that propose that graduates of this career should evidence the ability to carry out entrepreneurial projects focused on maximizing their profits, where a high level of individualism is consistent with the required entrepreneurial attitude, but a high level of collectivism, even if it increases the social benefit, does not influence the maximization of profits (Felipe, 2016). Thus, students in this type of careers show attitudes based on the importance of numerical and purely quantitative elements over instances of reflection that incorporate social and personal dimensions (Argandoña et al., 2018). Additionally, the relationship between cooperation and achievement can be explained by aggressive behaviors among peers as mediating variables, such as bullying. Indeed, personality traits have been found to be associated with bullying (e.g., van Geel et al., 2017; Tatiani, 2023), which in turn may be related to different levels of cooperation, to explain their effect on academic performance. For example, Mitsopoulou and Giovazolias (2015) conducted a systematic review and meta-analysis showing that lower levels of agreeableness and conscientiousness and higher levels of neuroticism and extraversion are related to bullying as a victim and perpetrator in children and adolescents, which can be considered for future studies. It is expected that the results presented here constitute a contribution to future studies on strategies to improve student support in mathematics education contexts, incorporating affective, socio-behavioral dimensions and personality traits. In this way, the influence of these dimensions on mathematics students must be considered in educational practices to move towards scenarios conducive to learning.

Projections

The results obtained in our research allow us to project future studies that consider other curricular programs based on the theoretical-conceptual model proposed here, a larger sample size, explore other techniques such as bootstrapping, adjust some variables that may present collinearity, and also to incorporate other measures associated with prosociality, such as aggression. We also hope to perform a comparison of models with and without indirect effects or employ standard model building/trimming strategies.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Ethics statement

The studies involving humans were approved by Ethics Committee of The University of Santiago de Chile code 258. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.

Author contributions

FM-A: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing. LF-P: Conceptualization, Methodology, Supervision, Writing – review & editing. JM-R: Formal analysis, Funding acquisition, Methodology, Project administration, Resources, Writing – review & editing. OF: Conceptualization, Data curation, Methodology, Writing – review & editing. PP: Formal Analysis, Writing – review & editing. JV: Investigation, Visualization, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This article is supported by the Project Fondecyt Regular (1200607) from the Government of Chile.

Conflict of interest

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

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Abella, V., and Bárcena, C. (2014). PEN, modelo de los cinco factores y problemas de conducta en la adolescencia. Acción Psicol. 11, 55–67. doi: 10.5944/ap.1.1.13867

Crossref Full Text | Google Scholar

Ackerman, P., and Beier, M. (2006). Determinants of domain knowledge and independent study learning in an adult sample. J. Educ. Psychol. 98, 366–381. doi: 10.1037/0022-0663.98.2.366

Crossref Full Text | Google Scholar

Ackerman, P., Bowen, K., Beier, M., and Kanfer, R. (2001). Determinants of individual differences and gender differences in knowledge. J. Educ. Psychol. 93, 797–825. doi: 10.1037/0022-0663.93.4.797

Crossref Full Text | Google Scholar

Ackerman, P., and Heggestad, E. (1997). Intelligence, personality, and interests: evidence for overlapping traits. Psychol. Bull. 121, 219–245. doi: 10.1037/0033-2909.121.2.219

Crossref Full Text | Google Scholar

Aguado, J. (2023). Aversión al riesgo al tomar decisiones económicas, efecto certeza y estimación de probabilidades. RETOS 13, 55–66. doi: 10.17163/ret.n25.2023.04

Crossref Full Text | Google Scholar

Aguilar, S., Ávalos, A., Giraldo, D., Quintero, S., Zartha, J., and Cortés, F. (2012). La curva en s como herramienta para la medición de los ciclos de vida de productos. J. Technol. Manag. Innov. 7, 238–248. doi: 10.4067/S0718-27242012000100016

Crossref Full Text | Google Scholar

Akin, A., and Kurbanoglu, I. (2011). The relationships between math anxiety, math attitudes, and self-efficacy: a structural equation model. Stud. Psychol. 53, 263–274.

Google Scholar

Andrés, M., Castañeiras, C., and Richaud, M. (2014). Relaciones entre la personalidad y el bienestar emocional en niños. El rol de la regulación emocional. Cuadernos Neuropsicol. Panam. J. Neuropsychol. 8, 217–241. doi: 10.7714/cnps/8.2.205

Crossref Full Text | Google Scholar

Andrews, P., and Hatch, G. (2000). A comparison of Hungarian and English teachers' conceptions of mathematics and its teaching. Educ. Stud. Math. 43, 31–64. doi: 10.1023/A:1017575231667

Crossref Full Text | Google Scholar

Argandoña, F., Persico, M., Visic, A., and Bouffanais, J. (2018). Estudio de Casos: Una metodología de enseñanza en la educación superior para la adquisición de competencias integradoras y emprendedoras. Tec Empresarial 12, 7–16. doi: 10.18845/te.v12i3.3934

Crossref Full Text | Google Scholar

Austin, E., Dore, T., and O’Donovan, K. (2008). Associations of personality and emotional intelligence with display rule perceptions and emotional labour. Personal. Individ. Differ. 44, 679–688. doi: 10.1016/j.paid.2007.10.001

Crossref Full Text | Google Scholar

Baldwin, T., Bedell, M., and Johnson, J. (1997). The social fabric of a team-based MBA program: network effects on student satisfaction and performance. Acad. Manag. J. 40, 1369–1397. doi: 10.2307/257037

Crossref Full Text | Google Scholar

Barroso, C., Ganley, C., McGraw, A., Geer, E., Hart, S., and Daucourt, M. (2021). A meta-analysis of the relation between math anxiety and math achievement. Psychol. Bull. 147, 134–168. doi: 10.1037/bul0000307

PubMed Abstract | Crossref Full Text | Google Scholar

Bauer, K., and Liang, Q. (2003). The effect of personality and precollege characteristics on first year activities and academic performance. J. Coll. Stud. Dev. 44, 277–290. doi: 10.1353/csd.2003.0023

Crossref Full Text | Google Scholar

Becker, J. (1983). Fear of the unknowns [review of do you panic about maths?]. J. Res. Math. Educ. 14, 126–129.

Google Scholar

Benet, V., and John, O. (1998). Los Cinco Grandes across cultures and ethnic groups: multitrait-multimethod analyses of the big five in Spanish and English. J. Pers. Soc. Psychol. 75, 729–750. doi: 10.1037/0022-3514.75.3.729

PubMed Abstract | Crossref Full Text | Google Scholar

Berthelon, M., Bettinger, E., Kruger, D., and Montecinos, A. (2019). The structure of peers: the impact of peer networks on academic achievement. Res. High. Educ. 60, 931–959. doi: 10.1007/s11162-018-09543-7

Crossref Full Text | Google Scholar

Bitran, M., Zúñiga, D., Lafuente, M., Viviani, P., and Mena, B. (2004). Características psicológicas y estilos cognitivos de estudiantes de medicina y de otras carreras de la Pontificia Universidad Católica de Chile. Rev. Med. Chile 132, 809–815. doi: 10.4067/S0034-98872004000700004

Crossref Full Text | Google Scholar

Blázquez, N. (2012). “Epistemología feminista: temas centrales” in Investigación feminista. Epistemología, metodología y representaciones sociales. eds. E. N. Blázquez and F. F. Y. M. Ríos (México, DF: Universidad Nacional Autónoma de México, Centro de Investigaciones Interdisciplinarias en Ciencias y Humanidades), 21–38.

Google Scholar

Bollen, K., and Curran, P. (2006). Latent curve models: A structural equation perspective. 1 st Edn. New Jersey: John Wiley & Sons.

Google Scholar

Bouchard, T., and Loehlin, J. (2001). Genes, evolution, and personality. Behav. Genet. 31, 243–273. doi: 10.1023/A:1012294324713

Crossref Full Text | Google Scholar

Bruun, J., and Brewe, E. (2013). Talking and learning physics: predicting future grades from network measures and force concept inventory pretest scores. Physical Rev. Special Topics 9:020109. doi: 10.1103/PhysRevSTPER.9.020109

Crossref Full Text | Google Scholar

Buxton, L. (1981). Do you panic about maths? London: Heineman.

Google Scholar

Candia, C., Encarnação, S., and Pinheiro, F. (2019). The higher education space: connecting degree programs from individuals’ choices. EPJ Data Sci. 8:39. doi: 10.1140/epjds/s13688-019-0218-4

Crossref Full Text | Google Scholar

Candia, C., Oyarzún, M., Landaeta, V., Yaikin, T., Monge, C., Hidalgo, C., et al. (2022). Reciprocity heightens academic performance in elementary school students. Heliyon 8:e11916. doi: 10.1016/j.heliyon.2022.e11916

PubMed Abstract | Crossref Full Text | Google Scholar

Cantoral, R., Montiel, G., and Reyes, D. (2014). Socioepistemología, matemáticas y realidad. Revist. Latinoam. Etnomatemática 7, 91–116.

Google Scholar

Caprara, G., Barbaranelli, C., Pastorelli, C., Bandura, A., and Zimbardo, P. (2000). Prosocial foundations of children's academic achievement. Psychol. Sci. 11, 302–306. doi: 10.1111/1467-9280.00260

PubMed Abstract | Crossref Full Text | Google Scholar

Carey, E., Hill, F., Devine, A., and Szucs, D. (2016). The chicken or the egg? The direction of the relationship between mathematics anxiety and mathematics performance. Front. Psychol. 6:1987. doi: 10.3389/fpsyg.2015.01987

PubMed Abstract | Crossref Full Text | Google Scholar

Castro, E., Beltran, J., and Miranda, I. (2020). Emociones de estudiantes en clases online sincrónicas que tratan espacios vectoriales. Paradigma, XLI:227–251. doi: 10.37618/paradigma.1011-2251.0.p227-251.id890

Crossref Full Text | Google Scholar

Caviola, S., Toffalini, E., Giofre, D., Ruiz, J., Szucs, D., and Mammarrella, I. (2021). Math performance and academic anxiety forms, from sociodemographic to cognitive aspects: a meta-analysis on 906, 311 participants. Educ. Psychol. Rev. 34, 363–399. doi: 10.1007/s10648-021-09618-5

Crossref Full Text | Google Scholar

Cerda, G., Ortega, R., Casas, J., del Rey, R., and Pérez, C. (2016). Predisposición desfavorable hacia el aprendizaje de las Matemáticas: una propuesta para su medición. Estudios Pedagóg. 42, 53–63. doi: 10.4067/S0718-07052016000100004

Crossref Full Text | Google Scholar

Chamorro, T., and Furnham, A. (2005). Personality and intellectual competence. Mahwah, NJ: Lawrence Erlbaum Associates.

Google Scholar

Clark, L., and Watson, D. (2008). “Temperament: an organizing paradigm for trait psychology” in Handbook of personality: Theory and research. eds. O. P. John, R. W. Robins, and L. A. Pervin. 3rd ed (New York: The Guilford Press), 265–286.

Google Scholar

Clark, L., Watson, D., and Mineka, S. (1994). Temperament, personality, and the mood and anxiety disorders. J. Abnorm. Psychol. 103, 103–116. doi: 10.1037/0021-843X.103.1.103

Crossref Full Text | Google Scholar

Conard, M. (2006). Aptitude is not enough: how personality and behavior predict academic performance. J. Res. Pers. 40, 339–346. doi: 10.1016/j.jrp.2004.10.003

Crossref Full Text | Google Scholar

Costa, V., and Rossignoli, R. (2017). Enseñanza y aprendizaje del álgebra lineal en una facultad de Ingeniería: Aspectos metodológicos y didácticos. Revista Educ. Ingeniería 12, 49–55. doi: 10.26507/rei.v12n23.734

Crossref Full Text | Google Scholar

Cupani, M., Garrido, S., and Tavella, J. (2013). El modelo de los cinco factores de personalidad: contribución predictiva al rendimiento académico. Revista Psicología 9, 67–86.

Google Scholar

De Raad, B., and Schouwenburg, H. (1996). Personality in learning and education: a review. Eur. J. Personal. 10, 303–336. doi: 10.1002/(SICI)1099-0984(199612)10:5<303::AID-PER262>3.0.CO;2-2

Crossref Full Text | Google Scholar

Del Valle, M., Zamora, E., Khalil, Y., and Altamirano, M. (2020). Rasgos de personalidad y dificultades de regulación emocional en estudiantes universitarios. Revist. Psicodebate 20, 56–67. doi: 10.18682/pd.v20i1.1877

Crossref Full Text | Google Scholar

Depue, R., and Collins, P. (1999). Neurobiology of the structure of personality: dopamine, facilitation of incentive motivation, and extraversion. Behav. Brain Sci. 22, 491–517. doi: 10.1017/S0140525X99002046

PubMed Abstract | Crossref Full Text | Google Scholar

Di Leo, I., Muis, K., Singh, C., and Psaradellis, C. (2019). Curiosity… confusion? Frustration! The role and sequencing of emotions during mathematics problem solving. Contemp. Educ. Psychol. 58, 121–137. doi: 10.1016/j.cedpsych.2019.03.001

Crossref Full Text | Google Scholar

Diener, E., Oishi, S., and Lucas, R. (2003). Personality, culture, and subjective well-being: emotional and cognitive evaluations of life. Annu. Rev. Psychol. 54, 403–425. doi: 10.1146/annurev.psych.54.101601.145056

Crossref Full Text | Google Scholar

Digman, J., and Takemoto-Chock, N. (1981). Factors in the natural language of personality: reanalysis, comparision and interpretation of six major studies. Multivar. Behav. Res. 16, 149–170. doi: 10.1207/s15327906mbr1602_2

PubMed Abstract | Crossref Full Text | Google Scholar

Dorier, J. (1991). Sur l'enseignement des concepts élémentaires d'algèbre linéaire à l'université. Recherches Didactique Mathématiques 11, 325–364.

Google Scholar

Dufey, M., and Fernandez, A. (2012). Validez y confiabilidad del positive affect and negative affect schedule (PANAS) en estudiantes universitarios chilenos. Revista Iberoamericana Diagnóstico Evaluación Avaliação Psicol. 2, 157–173.

Google Scholar

Eccles, J. (1989). Bringing young women to math and science. En M. Crawford and M. Gentry (Eds.), Gender and thought: psychological perspectives (p. 36–58). New York: Springer.

Google Scholar

Eysenck, H. (1967). Personality patterns in various groups of businessmen. Occup. Psychol. 41, 249–250.

Google Scholar

Farsides, T., and Woodfield, R. (2003). Individual differences and undergraduate academic success: the roles of personality, intelligence, and application. Personal. Individ. Differ. 34, 1225–1243. doi: 10.1016/S0191-8869(02)00111-3

Crossref Full Text | Google Scholar

Felipe, L. (2016). Comparación de la intención emprendedora entre estudiantes de ingeniería. Universidad Politécnica de Madrid: Estudio entre la Universidad Politécnica de Madrid y Linköping University.

Google Scholar

Fuchs, L., Fuchs, D., Bentz, J., Phillips, N., and Hamlett, C. (1994). The nature of student interactions during peer tutoring with and without prior training and experience. Am. Educ. Res. J. 31, 75–103. doi: 10.3102/00028312031001075

Crossref Full Text | Google Scholar

Furnham, A., and Chamorro, T. (2004). Estimating one’s own personality and intelligence scores. Br. J. Psychol. 95, 149–160. doi: 10.1348/000712604773952395

PubMed Abstract | Crossref Full Text | Google Scholar

Furnham, A., Chamorro, T., and Mcdougall, F. (2003). Personality, cognitive ability, and beliefs about intelligence as predictors of academic performance. Learn. Individ. Differ. 14, 47–64. doi: 10.1016/j.lindif.2003.08.002

Crossref Full Text | Google Scholar

Furnham, A., Forde, L., and Cotter, T. (1998). Personality and intelligence. Personal. Individ. Differ. 24, 187–192. doi: 10.1016/S0191-8869(97)00169-4

Crossref Full Text | Google Scholar

Goff, M., and Ackerman, P. (1992). Personality intelligence relations: assessment of typical intellectual engagement. J. Educ. Psychol. 84, 537–552. doi: 10.1037/0022-0663.84.4.537

Crossref Full Text | Google Scholar

Goldin, G., Hannula, M., Heyd-Metzuyanim, E., Jansen, A., Kaasila, R., Lutovac, S., et al. (2016). Attitudes, beliefs, motivation and identity in mathematics education. An overview of the field and future directions. 1 sd Edn. New York: Springer Open.

Google Scholar

Gómez, I. (2016). Métodos empíricos para la determinación de estructuras de cognición y afecto en matemáticas. Málaga: Investigación en Educación Matemática.

Google Scholar

Guzmán, C., and Serrano, O. (2011). Las puertas del ingreso a la educación superior: el caso del concurso de selección a la licenciatura de la UNAM. Revista Educ. Superior 40, 31–53.

Google Scholar

Hamilton, J., Burke, T., Stange, J., Kleiman, E., Rubenstein, L., Scopelliti, K., et al. (2017). Trait affect, emotion regulation, and the generation of negative and positive interpersonal events. Behav. Ther. 48, 435–447. doi: 10.1016/j.beth.2017.01.006

PubMed Abstract | Crossref Full Text | Google Scholar

Hembree, R. (1990). The nature, effects, and relief of mathematics anxiety. J. Res. Math. Educ. 21, 33–46. doi: 10.2307/749455

Crossref Full Text | Google Scholar

Ivaniushina, V., and Alexandrov, D. (2018). Anti-school attitudes, school culture and friendship networks. Br. J. Sociol. Educ. 39, 698–716. doi: 10.1080/01425692.2017.1402674

Crossref Full Text | Google Scholar

John, O., Caspi, A., Robins, R., Moffitt, T., and Stouthamer, M. (1994). The "little five": exploring the nomological network of the five-factor model of personality in adolescent boys. Child Dev. 65, 160–178. doi: 10.2307/1131373

Crossref Full Text | Google Scholar

John, O., Robins, R., and Pervin, L. (2011). Handbook of personality, theory and research (third edition).

Google Scholar

Johnson, J. (1994). Clarification of factor five with the help of the AB5C model. Eur. J. Personal. 8, 311–334. doi: 10.1002/per.2410080408

Crossref Full Text | Google Scholar

Judge, T., and Ilies, R. (2002). Relationship of personality to performance motivation: a Meta-analytic review. J. Appl. Psychol. 87, 797–807. doi: 10.1037/0021-9010.87.4.797

PubMed Abstract | Crossref Full Text | Google Scholar

Klein, E., Bieck, S., Bloechle, J., Huber, S., Bahnmueller, J., Willmes, K., et al. (2019). Anticipation of difficult tasks: neural correlates of negative emotions and emotion regulation. Behav. Brain Funct. 15, 4–13. doi: 10.1186/s12993-019-0155-1

PubMed Abstract | Crossref Full Text | Google Scholar

Komarraju, M., and Karau, S. (2005). The relationship between the big five personality traits and academic motivation. Personal. Individ. Differ. 39, 557–567. doi: 10.1016/j.paid.2005.02.013

Crossref Full Text | Google Scholar

Maninat, M. (2021). Afectividad, educación matemática y neurodidáctica: Visión panorámica e implicaciones en el aprendizaje entre cero y seis años. Revista ciencias de la educación 58, 670–693.

Google Scholar

Martínez De Ibarreta, C., Redondo, R., Rua, A., and Fabra, E. (2011). Factores de personalidad (Big Five) y rendimiento académico en asignaturas cuantitativas de ADE. Anales ASEPUMA 19, 1–19.

Google Scholar

Matsumoto, D. (2006). Are cultural differences in emotion regulation mediated by personality traits? J. Cross-Cult. Psychol. 37, 421–437. doi: 10.1177/0022022106288478

Crossref Full Text | Google Scholar

McAdams, K., and Donnellan, B. (2009). Facets of personality and drinking in first year college students. Personal. Individ. Differ. 46, 207–212. doi: 10.1016/j.paid.2008.09.028

Crossref Full Text | Google Scholar

McGraw, R., Lubienski, S., and Strutchens, M. (2006). A closer look at gender in NAEP mathematics achievement and affect data: intersections with achievement, race/ethnicity, and socioeconomic status. J. Res. Math. Educ. 37, 129–150. doi: 10.2307/30034845

Crossref Full Text | Google Scholar

McLeod, D. (1988). Affective issues in mathematical problem solving: some theoretical considerations. J. Res. Math. Educ. 19, 134–141. doi: 10.5951/jresematheduc.19.2.0134

Crossref Full Text | Google Scholar

McLeod, D. (1994). Research on affect and mathematics learning in the JRME: 1970 to the present. J. Res. Math. Educ. 25, 637–647. doi: 10.2307/749576

Crossref Full Text | Google Scholar

Meyer, G., and Shack, J. (1989). Structural convergence of mood and personality: evidence for old and new directions. J. Pers. Soc. Psychol. 57, 691–706. doi: 10.1037/0022-3514.57.4.691

Crossref Full Text | Google Scholar

Middleton, J., and Spanias, P. (1999). Motivation for achievement in mathematics: findings generalizations and criticism of the research. J. Res. Math. Educ. 30, 65–88. doi: 10.2307/749630

Crossref Full Text | Google Scholar

Mitsopoulou, E., and Giovazolias, T. (2015). Personality traits, empathy and bullying behavior: a meta-analytic approach. Aggress. Violent Behav. 21, 61–72. doi: 10.1016/j.avb.2015.01.007

Crossref Full Text | Google Scholar

Nettle, D. (2006). Psychological profiles of professional actors. Personal. Individ. Differ. 40, 375–383. doi: 10.1016/j.paid.2005.07.008

Crossref Full Text | Google Scholar

O’Connor, M., and Paunonen, S. (2007). Big five personality predictors of post-secondary academic performance. Personal. Individ. Differ. 43, 971–990. doi: 10.1016/j.paid.2007.03.017

Crossref Full Text | Google Scholar

Oktaç, A., and Gaisman, M. (2010). ¿Cómo se aprenden los conceptos de álgebra lineal? Revist. Latinoam. Investig. Matemática Educ. 13, 373–385.

Google Scholar

Orjuela, C., Hernández, R., and Cabrera, L. (2019). Actitudes hacia la matemática: algunas consideraciones en su relación con la enseñanza y el aprendizaje de la misma. Revista Educ. Matemática 34, 23–38. doi: 10.33044/revem.25287

Crossref Full Text | Google Scholar

Paunonen, S. (1998). Hierarchical organization of personality and prediction of behavior. J. Pers. Soc. Psychol. 74, 538–556. doi: 10.1037/0022-3514.74.2.538

Crossref Full Text | Google Scholar

Pekrun, R., Lichtenfeld, S., Marsh, H., Murayama, K., and Goetz, T. (2017). Achievement emotions and academic performance: longitudinal models of reciprocal effects. Child Dev. 88, 1653–1670. doi: 10.1111/cdev.12704

PubMed Abstract | Crossref Full Text | Google Scholar

Pinxten, M., Marsh, H., De Fraine, B., Vane den Noortgate, W., and Van Danne, J. (2014). Enjoying mathematics or feeling competent in mathematics? Reciprocal effects on mathematics achievement and perceived math effort expenditure. Br. J. Educ. Psychol. 84, 152–174. doi: 10.1111/bjep.12028

Crossref Full Text | Google Scholar

Pollock, N., McCabe, G., Southard, A., and Zeigler-Hill, V. (2016). Pathological personality traits and emotion regulation difficulties. Personal. Individ. Differ. 95, 168–177. doi: 10.1016/j.paid.2016.02.049

Crossref Full Text | Google Scholar

Poropat, A. (2014). Other-rated personality and academic performance: evidence and implications. Learn. Individ. Differ. 34, 24–32. doi: 10.1016/j.lindif.2014.05.013

Crossref Full Text | Google Scholar

Pulgar, J., Candia, C., and Leonardi, P. (2020). Social networks and academic performance in physics: undergraduate cooperation enhances ill-structured problem elaboration and inhibits well-structured problem solving. Physic. Rev. Phys. Educ. Res. 16:010137-1-010137-13. doi: 10.1103/PhysRevPhysEducRes.16.010137

Crossref Full Text | Google Scholar

Pulgar, J., Ramírez, D., Umanzor, A., Candia, C., and Sánchez, I. (2022). Long-term collaboration with strong friendship ties improves academic performance in remote and hybrid teaching modalities in high school physics. Physical Rev. Phys. Educ. Res. 18:010146-1-010146-19. doi: 10.1103/PhysRevPhysEducRes.18.010146

Crossref Full Text | Google Scholar

Reisenzein, R., and Weber, H. (2009). “Personality and emotion” in The Cambridge Handbook of personality psychology Eds. Philip J. Corr and Gerald Matthews. (United Kingdom: Cambridge University Press).

Google Scholar

Romero, E., Luengo, M., Gómez-Fraguela, J., and Sobral, J. (2002). La estructura de los rasgos de personalidad en adolescentes: el modelo de cinco factores y los cinco alternativos. Psicothema, 14:134–143.

Google Scholar

Rothstein, M., Paunonen, S., Rush, J., and King, G. (1994). Personality and cognitive ability predictors of performance in graduate business school. J. Educ. Psychol. 86, 516–530. doi: 10.1037/0022-0663.86.4.516

Crossref Full Text | Google Scholar

Ruiz, M., Pardo, A., and San Martín, R. (2010). Modelos de ecuaciones estructurales. Papeles Psicól. 31, 34–45.

Google Scholar

Sánchez, R., Retana, B., and Carrasco, E. (2008). Evaluación psicológica del entendimiento emocional: diferencias y similitudes entre hombres y mujeres. Revista Iberoam. Diagnóstico Eval. Psicol. 26, 193–216.

Google Scholar

Sandín, B., Chorot, P., Lostao, L., Joiner, T., Santed, M., and Valiente, R. (1999). Escalas PANAS de afecto positivo y negativo: validación factorial y convergencia transcultural. Psicothema 11, 37–51.

Google Scholar

Schmidt, C. (2008). Construcción de un cuestionario de emociones positivas en población entrerriana. Revista Iberoam. Diagnóstico Eval. Psicol. 26, 117–139.

Google Scholar

Simón, M. (2018). Transversalidad de género en la enseñanza de las matemáticas. Revista Feminista Divulgación Científica 3, 17–26.

Google Scholar

Simón-Ramos, M., Farfán-Márquez, R., and Rodríguez-Muñoz, C. (2022). Una perspectiva de género en matemática educativa. Revista Colombiana Educ. 86, 235–254. doi: 10.17227/rce.num86-12093

Crossref Full Text | Google Scholar

Spinath, B., Freudenthaler, H., and Neubauer, A. (2010). Domain-specific school achievement in boys and girls as predicted by intelligence, personality and motivation. Personal. Individ. Differ. 48, 481–486. doi: 10.1016/j.paid.2009.11.028

Crossref Full Text | Google Scholar

Tatiani, G. (2023). School bullying and personality traits from elementary school to university. Int. J. Bullying Prev., 5:1–13. doi: 10.1007/s42380-023-00174-w

Crossref Full Text | Google Scholar

Tobin, R., Graziano, W., Vanman, E., and Tassinary, L. (2000). Personality, emotional experience, and efforts to control emotions. J. Pers. Soc. Psychol. 79, 656–669. doi: 10.1037/0022-3514.79.4.656

Crossref Full Text | Google Scholar

Toro, R., Alzate, L., Santana, L., and Ramírez, I. (2018). Afecto negativo como mediador entre intolerancia a la incertidumbre, ansiedad y depresión. Ansiedad Estrés 24, 112–118. doi: 10.1016/j.anyes.2018.09.001

Crossref Full Text | Google Scholar

Tudge, J., Winterhoff, P., and Hogan, D. (1996). The cognitive consequences of collaborative problem solving with and without feedback. Child Dev. 67, 2892–2909. doi: 10.2307/1131758

Crossref Full Text | Google Scholar

van Geel, M., Goemans, A., Toprak, F., and Vedder, P. (2017). Which personality traits are related to traditional bullying and cyberbullying? A study with the big five, dark triad and sadism. Personal. Individ. Differ. 106, 231–235. doi: 10.1016/j.paid.2016.10.063

Crossref Full Text | Google Scholar

Watson, D., Clark, L., and Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: the PANAS scales. J. Pers. Soc. Psychol. 54, 1063–1070. doi: 10.1037/0022-3514.54.6.1063

PubMed Abstract | Crossref Full Text | Google Scholar

Watson, D., and Pennebaker, J. (1989). Health complaints, stress, and distress: exploring the central role of negative affectivity. Psychol. Rev. 96, 234–254. doi: 10.1037/0033-295X.96.2.234

PubMed Abstract | Crossref Full Text | Google Scholar

Zhang, J., and Ziegler, M. (2015). Interaction effects between openness and fluid intelligence predicting scholastic performance. J. Intelligence 3, 91–110. doi: 10.3390/jintelligence3030091

Crossref Full Text | Google Scholar

Zhang, J., and Ziegler, M. (2018). Why do personality traits predict scholastic performance? A three-wave longitudinal study. J. Res. Pers. 74, 182–193. doi: 10.1016/j.jrp.2018.04.006

Crossref Full Text | Google Scholar