Effects of Personality and Motivation on Students’ Academic Engagement and Metacognitive-Cognitive Strategies

Abstract

Several investigations have focused on the relationship between personality and learning performance. However, the relationships among personality traits, motivation, emotional and behavioral engagement, and metacognitive-cognitive strategies remain unexplored. This research introduces a model based on a personality system set comprising metacognition, cognition, motivation, emotional and behavioral engagement. Five core education theories support this model. Three hundred seventy-four Mexican university students who were enrolled in a Biology and Industrial Management bachelor’s degree program participated. Personality traits mainly affect academic engagement but not motivation. Additionally, academic engagement mediates the relationship between personality traits and metacognitive-cognitive strategies. The findings show that agreeableness and neuroticism traits influence emotional engagement and disaffection. Consciousness and extraversion affect behavioral disaffection and engagement, respectively. Meanwhile, motivation affects emotional engagement, disaffection and metacognitive-cognitive strategies. Finally, openness does not affect any of the constructs. Hence, personality traits directly influence academic engagement and disaffection but not motivation and cognitive strategies.

Plain language summary

Effects of Personality and Motivation on Students

Several investigations have focused on the relationship between personality and learning performance. However, the relationship between personality traits and motivation, emotions, behaviours, and metacognition-cognition remains unexplored. This research introduces a model based on the personality systems set. Five core education theories support this model. Three hundred seventy-four Mexican university students participated, who are enrolled in Biology and Industrial Management Bachelor’s degree. Personality mainly affects emotions and behaviours but not motivation. Also, academic engagement mediates between personality traits and metacognition and cognition. The findings show that agreeableness and neuroticism influence emotions. Consciousness and extraversion affect behaviours. Meanwhile, motivation affects emotions and metacognition cognition. Finally, openness did not affect anyone of the constructs.

Keywords

personality behavioral engagement emotional engagement motivation metacognitive-cognitive strategies

The big-five traits are an empirical model of personality (Costa & McCrae, 2008; Xie & Cobb, 2020) that refer to conscientiousness (being disciplined, organized, and achievement-oriented), extraversion (higher degree of sociability, assertiveness, and talkativeness), neuroticism (related to emotional stability, impulse control, and anxiety), openness (intense intellectual curiosity and creativity and preferring novelty and variety) and agreeableness (being helpful, cooperative, and sympathetic toward others). Personality traits have been related to motivation; for example, students who are open to experience should have a better learning performance, but some other studies show no relationship. Similarly, agreeableness showed mixed results about its relationship with motivation Peklaj et al. (2015). Motivation is a process whereby goal-directed activity is instigated and sustained (Ramirez-Arellano et al., 2018). According to the expectancy-value theory of motivation (Eccles, 1983) and the recent extension (Eccles & Wigfield, 2020), the expectancy component is students’ beliefs about their ability to perform a learning task and that they are responsible for their performance; meanwhile, the value component is conceptualized as students’ goals about the importance and interest of the learning task.

Motivation is a precursor of engagement and impacts behavioral, emotional, and cognitive engagement (Acosta-Gonzaga & Ramirez-Arellano, 2021; Acosta-Gonzaga & Ramirez-Arellano, 2022). Furthermore, academic engagement is a meta-construct that includes emotional, behavioral, and cognitive dimensions (Carmona-Halty et al., 2021; Reschly, 2020; Robayo-Tamayo et al., 2020; Yang et al., 2021). Emotional engagement is the state related to students’ involvement in learning activities, such as fun, enthusiasm, enjoyment, and pride. Similarly, behavioral engagement is the motivated action that supposes students’ active participation in learning activities, such as interest, effort, involvement, and attention (Kuchinski-Donnelly & Krouse, 2020; E. Skinner et al., 2008; Thomas & Allen, 2021; Yang et al., 2021).

The cognitive theory of motivation and learning (Linnenbrink & Pintrich, 2000; Pintrich, 1988a, b; Pintrich & De Groot, 1990) defines metacognitive strategies such as metacognitive self-regulation and critical thinking that involve elaborating and organizing information using critical thinking (Pintrich & Garcia, 1991). Motivation instigates cognitive and metacognitive processes in the same way that motivation is a precursor for engagement. This precursor means knowledge acquisition demands a motivated learner to apply cognitive and metacognitive strategies (Pintrich, 1988a).

Personality traits and their influences on motivation, academic engagement, learning outcomes, and metacognition have been studied independently in recent years, but not in a comprehensive model that depict their relationships. Also, as far as the author knows, the effects of personality and motivation on students’ academic engagement and metacognitive-cognitive strategies have been scarcely studied. Thus, this study aims to introduce a model of personality, motivation and academic engagement based on Eccles’ expectancy-value theory and motivation (Eccles, 1983; Eccles & Wigfield, 2020), Skinner’ self-system model (E. Skinner et al., 2008) encompassing emotional and behavioral engagement, Pintrich’ cognitive theory of motivation and learning (Linnenbrink & Pintrich, 2000; Pintrich, 1988a; Pintrich & De Groot, 1990), and Costa’ big-five traits (Costa & McCrae, 1999, 2008). The author hypothesized that personality traits directly influence academic engagement and disaffection but not motivation and cognitive strategies.

Related Work

Personality, Motivation, and Engagement

Personality traits are valuable for establishing the relationships among conscientiousness, extraversion, neuroticism and appraisal (Cummings et al., 2019). For example, conscientiousness is related to learning motivation (Abdullatif & Velázquez-Iturbide, 2020; Brandt et al., 2020; Mammadov et al., 2021) and motivational regulation (Ljubin-Golub et al., 2019). Additionally, conscientiousness and openness are negatively related to the demotivation of English students (Pathan et al., 2021). Students with high openness use elaborative learning strategies and work to link new information with previous knowledge (Brandt et al., 2020).

Previous research has focused on engagement as a predictor of learning achievement and grades rather than as an outcome (Qureshi et al., 2016; Yang et al., 2021). Engagement is positively associated with extraversion, agreeableness, conscientiousness, and openness and negatively associated with neuroticism. Nursing students with high neuroticism have low engagement, and for those with low neuroticism and high levels of the remaining traits, engagement is significant (Villafañe et al., 2022). College students with openness, conscientiousness, and agreeableness show cognitive engagement. Additionally, cognitive engagement is more significant in students with agreeableness than in those with openness and conscientiousness (Mahama et al., 2022).

In contrast with these results, Ongore (2014) found that extraversion, agreeableness, conscientiousness and openness to experience were positively related to cognitive and emotional engagement, but neuroticism was negatively associated. Meanwhile, students with conscientiousness, extraversion, and agreeableness show emotional engagement but not cognitive engagement (Sangeetha et al., 2020).

Furthermore, neuroticism predicts university students’ cognitive and emotional engagement in English language courses (Angelovska et al., 2021). Additionally, extraversion, agreeableness and conscientiousness predict engagement, explaining approximately one-third of the variance in engagement (Qureshi et al., 2016). Similarly, agreeableness and openness predict emotional engagement in online learning (Quigley et al., 2022). Previous research has focused mainly on cognitive and emotional engagement, but behavioral engagement has been neglected.

Personality and Learning Outcomes

The relationship between personality traits and learning outcomes has been widely explored in recent years. Neuroticism, extraversion, and agreeableness are less related to academic performance (Brandt et al., 2020; Lechner et al., 2017; Zhang & Wang, 2023). However, conscientiousness (Dumfart & Neubauer, 2016; Mammadov, 2022) and openness have been found to predict students’ final grades (Mammadov et al., 2021; Meyer et al., 2019; Zhang & Wang, 2023). Additionally, Mammadov et al. (2021) found that extraversion, neuroticism and agreeableness are negatively related to learning performance.

On the other hand, Meyer et al. (2019) show that the relevance of personality traits in forecasting academic performance depends on the subject. Mammadov et al. (2021) and Meyer et al. (2019) confirm the positive role of conscientiousness and find a negative impact of extraversion on global learning achievements, which aligns with previous findings. Other studies have found a difference in the relationship between personality traits and learning achievements by gender (Kuśnierz et al., 2020; Meyer et al., 2019). Conscientiousness is a good predictor of learning achievements for females (Kuśnierz et al., 2020; Meyer et al., 2019). Thus, the impact of personality traits varies or is not significant when the gender and specific context are analyzed; therefore, these relations could be complex and dynamic.

However, personality traits alone do not capture the big picture of personality; thus, including mental skills to predict learning performance motivated the research of Bouiri et al. (2021). They found a significant correlation between mental skills (affective, cognitive, and metacognitive strategies) and learning performance.

Theoretical Framework

Systems of Primary Personality Parts and their Relationships

The personality systems framework (PSF) (Mayer & Allen, 2013) depicts how personality works and the primary functional areas of personality: energy development, knowledge guidance, action implementation, and executive management (Mayer, 2015, 2020; Mayer & Allen, 2013). Energy development includes motivational urges and emotional networks linked in knowledge guidance and containing the models of the self and the self in the world. Knowledge guidance is the cognitive process that guides behavior (Mayer, 2015, 2020; Mayer & Allen, 2013). The action implementation controls behavioral acts (Mayer & Allen, 2013). Finally, executive management monitors personality components and subsystems; thus, personality is self-regulated (Mayer, 2015, 2020; Mayer & Allen, 2013). The PSF provides a new framework for studying personality rather than a new theory; thus, it can be applied to several areas, such as educational psychology.

On the other hand, big-five traits (McCrae & Costa 1987) are an empirical model of personality traits (Costa & McCrae, 2008; Xie & Cobb, 2020) that refer to neuroticism, extraversion, openness, agreeableness, and conscientiousness. The personality traits motivated the formulation of the five personality systems (Costa & McCrae, 1999). The PSF and five factors of personality systems consider the biological basis of cognition, motivation, emotion and external situations that mold personality in a continuous dynamic process (Allen et al., 2021). Personality traits can be conceived as partial descriptors of the personality system components that give us a partial view of the personality system.

Energy development, knowledge guidance, action implementation, and executive management are the primary parts of the personality, according to PSF. They are conceptualized as a system in which subsystems interact. These essential interactions are described in this section. For example, energy development, knowledge guidance, and action implementation interact to progressively mold behavioral urges (Mayer, 2003). However, describing precisely how this dynamic process occurs is not the aim of the PSF.

Knowledge guidance contains information about the self and the world. Self-knowledge includes individuals’ internal states and their experiences throughout life, intelligence, memory, and reasoning processes (Mayer, 2005; Mayer & Allen, 2013); these cognitive mechanisms are depicted in Figure 1. Additionally, knowledge guidance contains models linking operating motivations, emotions, and cognition. The third part, action implementation, includes models of behaving in the environment and stores the methods of communicating and acting (Mayer, 2003; Mayer & Korogodsky, 2011). Executive management oversees the other three systems and is aware of internal representations and external situations. Additionally, it mainly monitors the states and conditions emanating from knowledge guidance. Therefore, executive management can focus consciousness on a course of action and its consequences. For example, executive management can encourage or limit the selection of action in a self-regulated cycle based on the awareness of negative consequences. Thus, metacognitive processes are situated in this system (see Figure 1). Energy development and knowledge guidance influence executive management by activating the monitoring of urges, emotions, and thoughts or even opposing the effort of the self-control of executive management (Mayer, 2003).

Figure 1.

The proposed model is based on the personality systems framework, its functional areas, components, and the primary relationships in an educational context.

The PSF situates motivation, emotions (in energy development), cognition (in knowledge guidance), metacognition (in executive management), and behaviors (in action implementation) (see Figure 1). Additionally, the general relationships among the four systems imply that motivation and emotions are strongly connected in the energy development system. Furthermore, knowledge guidance and energy development mold behavioral urges; thus, emotions, motivation, and cognition are connected in this process. Similarly, executive management (where metacognition occurs) monitors the knowledge guidance, energy development, and action implementation systems in a self-regulated process. The following sections provide an in-depth foundation of the relationships within the proposed model that has its roots in PSF.

Expectancy-Value Theory of Motivation, Emotional and Behavioral Engagement

The expectancy-value theory, proposed by Eccles (1983), supports the detailed relationships between PSF elements, such as the implementation system (encompassing behaviors), energy development (motivations and emotion) and knowledge guidance (where the cognition resides). The expectancy-value theory defines motivation as the expectations of students’ beliefs about their self-efficacy success or failure on a particular task. The value construct of motivation is the value of the task from various perspectives, such as intrinsic and external. Meanwhile, the attainment value reflects how challenging students’ perceived a task to be. Intrinsic value is related to students’ perceptions of engagement in a task. The utility of the task depends on the instrumental motivation of students and is associated with students’ goals (Eccles, 1983; Pintrich, 1988a, b).

The motivations are the instigators of the emotions that guide the appropriate expression of motivation in social realms (Mayer, 2015; Mayer & Allen, 2013), such as in an educational context underpinning the relationship shown in the model of Figure 1. Additionally, self-system theory postulates that motivation is necessary to be engaged. From this perspective, motivation represents intention, and engagement is action (Reschly & Christenson, 2012; E. A. Skinner et al., 2008). The relationship between motivation and engagement could be reciprocal; however, motivation is a strong precursor of engagement (Acosta-Gonzaga & Ramirez-Arellano, 2021; Lei et al., 2024; Reeve, 2012).

Engagement is a meta-construct that comprises emotional, behavioral, and cognitive engagement (learning strategies and metacognitive self-regulation; Kim et al., 2014; King et al., 2023; Reschly & Christenson, 2012; Wolters & Taylor, 2012). Emotional engagement is the emotional state related to students’ involvement in learning activities, such as fun, enthusiasm, enjoyment, and pride. Similarly, behavioral engagement is the motivated action that supposes students’ active participation in learning activities, such as interest, effort, involvement, and attention (Nichols & Dawson, 2012; E. Skinner et al., 2008; Skinner et al., 2009; E. A. Skinner et al., 2008).

The action implementation system (encompassing behaviors) results from influencing motivations and emotions (within energy development) by knowledge guidance (where the cognition resides). Thus, engagement (in an educational context) is an emergent system from motivation, emotions (in the educational context, viewed as emotional engagement), and cognition. The proposed model depicts the relationships between motivation, emotions, and cognition with behaviors; the incoming link to behaviors is shown in Figure 1. For example, a student bored by a learning task likely disengages from classroom participation; thus, emotional engagement predicts behavioral engagement. Additionally, this disengagement can be triggered by a demotivated student (E. A. Skinner et al., 2008). However, the contribution of emotions and cognition in this process are not equal in every situation. For example, a student who perceives a learning activity as challenging could be emotionally neutral (he or she is neither bored nor having fun) but motivated to pay attention to the lecture (behavioral engagement). Similarly, in other settings, this motivated but emotionally neutral student decided to summarize the fundamental concepts introduced by the teacher, so he or she inherently listened to the teacher. In both scenarios, behavioral engagement is a consequence of motivation; in the first scenario, it is directly triggered by motivation, and in the second one, it is triggered by cognition (see Figure 1).

The relationships of the behavior in our model extends the self-system theory (E. Skinner et al., 2008; E. A. Skinner et al., 2008), where motivation and behavioral engagement have an internal dynamic (captured by the reciprocal link between emotions and behavior of Figure 1) and an external one with environmental factors. However, self-system theory neglects interactions with other vital components of personality, such as cognition and metacognition. This gap is filled by incorporating the cognitive theory of motivation and learning (Pintrich, 1988a, 1988b).

Cognitive Theory of Motivation and Learning

Metacognition is cognition about cognition (Efklides, 2017; Winne, 2018). It can be divided into self-regulation (Acosta-Gonzaga & Ramirez-Arellano, 2022; Brown, 1987; Schuster et al., 2020), such that individuals must be aware of their cognitions and be able to apply metacognitive and cognitive strategies to solve problems (Wiley & Jee, 2010). The PSF states that executive management (including metacognition) monitors energy development (such as motivation and emotion) and knowledge guidance (including cognition) with particular attention to the cognitive process. It coincides with the relationships among motivation, cognition, and metacognition proposed in the cognitive theory of motivation and learning (CTML) (Pintrich, 1988a, b). The CTML defines metacognitive strategies such as metacognitive self-regulation and critical thinking. Cognitive strategies involve elaborating and organizing information using critical thinking (Pintrich & Garcia, 1991). The CTML conceptualizes cognition and metacognition as the components that process and represent information; however, they can be separately conceptualized since metacognition is a higher-order process involved in planning, monitoring, and self-regulating the lower-order cognitive process; thus, metacognition is an executive process tightly linked to cognition (McKeachie et al., 1986). In CTML, expectancy-value theory provides the foundations for conceptualizing the relationships among motivation, cognition, and metacognition. Motivation instigates cognitive and metacognitive processes in the same way that motivation is a precursor for engagement. This precursor means that knowledge acquisition demands a motivated learner to apply cognitive and metacognitive strategies (Pintrich, 1988a), as shown in Figure 1.

We can divide the cognitive process into complex strategies, such as elaboration, comprehension, and organization, and basic strategies, such as rehearsal (McKeachie et al., 1985; Winne, 2018). These processes do not involve an accumulation of information but a synthesis and organization of information. The cognitive process influences students’ learning acquisition and modifies their prior knowledge. Using cognitive strategies requires that students be engaged in learning tasks (Pintrich, 1988a, b); thus, engagement (emotional) is tightly connected to cognition (see Figure 1). For example, students who enjoy the lesson (emotionally engaged) organize the new information (cognitive strategy). As a result, they pay attention to the class and are involved in learning activities (behavioral engagement).

In contrast, bored students can be distracted. Thus, this second example shows that cognition and emotional engagement are related to behavioral engagement. In this process, the interventions are not always sequential. Furthermore, motivation, cognition, and metacognition only sometimes operate simultaneously in the learning process (Pintrich, 1988b; Pintrich & De Groot, 1990; Pintrich & Garcia, 1994). Thus, these relationships are hypothesized in the model of Figure 1. For example, note that motivation can be linked to behaviors through cognition or emotions. There can be a third path from motivation to emotions and cognition.

Metacognitive self-regulation involves planning, monitoring, and regulating the cognitive process (Pintrich et al., 2000; Pintrich, 1988a); for example, a problem-solving task in physics demands the knowledge of equations and theoretical links between the variables of this equation to solve the specific problem. Students are aware of the academic requirements to face this task. Based on this knowledge, they decide to face the task or reinforce some of these requirements, for example, by reading the physical meaning of the equation’s parameters again or immediately computing the equation. Then, they decide to skim the problem description text to gather the information necessary to calculate the equation. However, they notice that the required information is not easy to find (monitoring); thus, they decide to apply a different reading strategy (control, self-regulation). Students who control and self-regulate their cognition promote high learning performance levels; therefore, the relationship between cognition and metacognition is reciprocal. Metacognition is influenced by motivational constructs such as perceived competence (Pintrich, 1988b). In our example, the students know that they do not have a clear understanding of the physical meaning of the equation parameters, so they review this topic. The students’ perceived competence motivates a plan that incorporates the implicit application and monitoring of cognitive strategies (Figure 1).

Although the CTML introduced the relationships among motivation, cognition, and metacognition, the interactions among well-studied emotions, cognition, and metacognition were not depicted. Efklides (2011) and Efklides (2017) filled this gap by introducing a model including relationships between emotions and cognition and between emotions and metacognition. These relationships form the basis of relationships among these constructs in the proposed model. These interactions are argued to vary with personal profile and are stable in the short and long term. Furthermore, it is posited that metacognition is directly influenced by motivation, emotions, and cognition (Efklides, 2012). In particular, Efklides (2012) suggests that metacognition is affected by emotions at three levels. In the first level, interest and anxiety are related to the learning task. The second relates to the processing of the task, such as joy, boredom, and anger. The third is the outcome of the task, such as pride and shame. Emotions provide bottom-up relevant information about the cognitive process’s self-regulation; thus, emotions play a crucial role in metacognition (Efklides, 2017).

Methods

Participants and Context

The study was conducted on the campuses of a public university in Mexico City. A total of 150 students were majoring in biology, and 252 were majoring in management. The survey results were collected from students in mathematics applied to biological sciences (biology major) and probability subjects (industrial management major) in the first semester of the 2022 academic year. A total of 402 undergraduate university students took the courses, with 140 males and 262 females. The minimum and maximum ages were 18 and 23, respectively, and the mean was 20 years. The participants provided informed consent and agreed that their responses could be used for this research. The teacher gathered informed consent at the beginning of the study. A total of 374 of the 402 students answered all the questions, as detailed later; thus, the 28 remaining students were excluded from this study. Three examinations were administered at the beginning, middle, and end of the semester. Additionally, these examinations and several learning activities, quizzes, and homework were considered to compute the overall grade of the course. The lectures were held twice a week. The academic administration office provided the overall grade of the students.

Procedure and Design

The analysis of this research is quantitative; thus, a structural equation model was employed to test the relationships among motivation, emotions and behaviors in the conceptual models shown in Figure 1. The motivation constructs were gathered using the Motivated Strategies for Learning Questionnaire (MSLQ) (Pintrich, 1991; Pintrich & De Groot, 1990), and emotional and behavioral engagement and their counterpart disengagement (SEBED) were obtained using the survey from (E. Skinner et al., 2008). The personality traits were assessed by the NEO-Five-Factor Inventory (NEOFFI) (Costa & McCrae, 1992).

The constructs gathered at a specific time for structural equation modelling (SEM) were included according to Table 1. For example, personality traits were assessed at time one, while motivation was assessed at time 2. The structural equation model follows the theoretical relationship where motivation is a precursor of engagement (Acosta-Gonzaga & Ramirez-Arellano, 2021; Reeve, 2012). Additionally, the PSF states that behaviors emerge from motivation and emotions, which coincides with Mayer and Allen (2013) and E. Skinner et al. (2008). In the educational context, a reciprocal relationship between emotional and behavioral engagement has been proposed (E. Skinner et al., 2008). The limitations of SEM analysis allow us to test only the relationship from emotional engagement to behavioral engagement or vice versa one at a time.

Table 1.

Schedule for Gathering the SEBED, MSLQ, and NEOFFI Several Times.

Time 1	Time 2	Time 3	Time 4	Time 5
NEOFFI	MSLQ	SEBED	MSLQ	SEBED
Personality traits	Motivation	Emotional engagement	Cognitive and metacognitive strategies	Behavioral engagement

Measures

The personality traits were assessed by the NEO-Five-Factor Inventory (NEOFFI) (Costa & McCrae, 1992). The personality traits included neuroticism, extraversion, openness, agreeableness, and conscientiousness and were assessed on a 5-point scale from 0 (strongly disagree) to 4 (strongly agree). We obtained the raw scores of each trait by summing the individual scores of each question (12 items). The NEOFFI has been applied to gather personal traits in Mexican adult subjects (Rodríguez-Gonzalez et al., 2022) and university students (Brambila-Tapia et al., 2022; Hernández-Peña et al., 2023).

The motivation and metacognitive-cognitive strategies were gathered using the MSLQ (Pintrich, 1991; Pintrich & De Groot, 1990). The motivational construct comprises intrinsic goal orientation, extrinsic goal orientation, task value, control of learning beliefs, self-efficacy for learning, and test anxiety. They scored the answers to this instrument from 1 (not at all true of me) to 7 (very true of me). Metacognitive-cognitive strategies include metacognitive self-regulation, critical thinking, rehearsal, elaboration, and organization. The adaptation of the MSQL has been applied to university students in Colombia (Villarreal & Giraldo, 2022), Spain (Ramírez et al., 2022) and Mexico (Acosta-Gonzaga, 2023; Acosta-Gonzaga & Ramirez-Arellano, 2022).

E. Skinner et al. (2008) used the SEBED to assess engagement. Emotional engagement includes fun, enthusiasm, enjoyment, and pride; emotional disengagement comprises negative emotions, such as boredom, frustration, anxiety, and sadness. Positive behaviors (behavioral engagement) include interest, effort, involvement, and attention; negative behaviors include passive, distracted, disinterested, and mentally disengaged. The answers were scored using a five-point Likert-type scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The adaptation of the SEBED has been used to gather engagement and disaffection data from Mexican university students (Acosta-Gonzaga & Ramirez-Arellano, 2021; Jones et al., 2023).

The NEOFFI was gathered at the beginning of the semester (time 1). The MSLQ measuring motivation was administered in the middle of the semester during the first session of that week (time 2). Then, in the second session (time 3), the SEBED was administered, measuring emotional engagement and disengagement. Then, at the first session of the second week, the MSLQ measuring metacognitive-cognitive strategies was administered (time 4). Finally, the SEBED (time 5) was employed to measure emotional engagement and disengagement. This schedule considers the temporality priority to establish the structural equation model.

Data Analysis

The model of Figure 1 was mapped to its structural equation model counterpart in Figure 2, considering the time when the construct was gathered (see Table 1) and the stages detailed in (Hair et al., 2022). Corroborating all the reciprocal relationships of the conceptual model of Figure 1 is beyond the scope of this research. The data analysis was carried out in two stages. First, the measurement model was evaluated using convergent validity (AVE), internal consistency (α, CR), and discriminant validity (Fornell-Larcker criterion). Then, we assessed the structural model to examine each hypothesis and assess its predictive power. In this stage, the relationships between openness and the other constructs were insignificant; hence, it was not included in the model of Figure 1. Since answers collected using the SEBED, MSLQ and NEOFFI are evaluated by different scales, they were centred before the SEM. The SEM aimed to establish the fit of the hypothetical constructs using SMART-PLS 4.

Figure 2.

The structural model of personality traits, motivation, engagement and metacognitive-cognitive strategies.

Cronbach’s alpha (α) was used to test the reliability of each construct, and values above .70 are acceptable (Bonett & Wright, 2015; Tavakol & Dennick, 2011). Additionally, the composite reliability (CR) and average variance extracted (AVE) were used to assess convergent validity. Values higher than 0.7 for CR and 0.5 for AVE are acceptable (Hair et al., 2022, p. 121). Finally, the Fornell-Larcker criterion assesses discriminant validity (Hair et al., 2022).

Results

Measurement Model

The measurement model consisted of personality traits formed by a single item, as shown in Table 2. The mean and standard deviation of the raw score of neuroticism, extraversion, openness, agreeableness, and conscientiousness and the overall grade are shown in Table 2. Emotional and behavioral engagement and its counterpart disengagement and metacognitive-cognitive strategies are multi-items. The descriptive statistic, α, CR, and AVE of the observed variables of emotional and behavioral engagement and their counterpart disengagement are shown in Table 3. Table 4 presents the mean and standard deviation of motivation, metacognitive-cognitive strategies, and α, CR, and AVE. The Fornell-Larcker criterion is shown in Table 5; the values in the diagonal show the square root of AVE that has to be greater than the rest of the correlations off the diagonal to verify the discriminant validity. Additionally, no multicollinearity issues were found because the variance inflation factors were below 5. The results of multiple mediation analyses are summarized in Table 6. The effects of agreeableness, neuroticism, and motivation are mediated mainly by emotional engagement and disaffection.

Table 2.

Descriptive Statistics of Raw Scores of Neuroticism, Extraversion, Openness, Agreeableness, Conscientiousness, and Overall Grade.

Variable	Mean	SD
Neuroticism	28.03	7.337
Extraversion	32.87	5.767
Openness	34.37	4.420
Agreeableness	38.24	6.371
Conscientiousness	37.29	6.282
Overall grade	7.83	1.53

Table 3.

Descriptive Statistics, α, CR and AVE of the Observed Variables of the Constructs Emotional and Behavioural Engagement and their Counterpart Disengagement.

Construct	α	CR	AVE	Variable	Mean	SD
Emotional engagement (EE)	.96	0.89	0.75	Fun (FU)	2.97	0.933
				Enthusiasm (ET)	2.76	1.110
				Enjoyment (EJ))	2.53	1.358
				Pride (PR)	2.41	1.477
Emotional disengagement (ED)	.88	0.97	0.708	Boredom (BO)	2.62	0.719
				Frustration (FR)	2.58	0.751
				Anxiety (AN)	2.62	0.742
				Sadness (SA)	2.79	1.010
Behavioral engagement (BE)	.82	0.86	0.74	Effort (EF)	4.33	0.539
				Involvement (IN)	3.78	0.625
				Attention (AT)	4.01	0.505
Behavioral disengagement (BD)	.89	0.89	0.75	Passive (PA)	2.86	0.916
				Distracted (DI)	3.05	0.860
				Disinterested (DS)	2.90	0.933
				Mentally disengaged (MD)	2.78	0.918

Table 4.

Descriptive Statistics α, CR and AVE of the Observed Motivation, Cognition, and Metacognition Variables.

Construct	α	CR	AVE	Variable	Mean	SD
Motivation (MO)	.98	0.98	0.93	Intrinsic goal orientation (IG)	5.526	0.910
				Extrinsic goal orientation (EG)	5.257	1.025
				Task value (TV)	5.512	0.942
				Control of learning beliefs (CLB)	4.061	0.771
				Self-efficacy for learning (SEL)	5.120	0.830
Metacognitive-cognitive strategies (MCS)	.98	0.98	0.93	Rehearsal (RE)	5.230	0.945
				Elaboration (EL)	5.028	0.959
				Organization (OR)	5.111	1.028
				Critical thinking (CT)	4.763	0.965
				Metacognitive self-regulation (MSR)	4.803	0.759

Table 5.

Discriminant Validity was Assessed by the Fornell-Larcker Criterion.

Construct	AG	BD	BE	CO	ED	EE	EX	MCS	MO	NE
AG	1
BD	−0.194	0.867
BE	0.083	−0.531	0.858
CO	0.229	−0.24	0.289	1
ED	0.12	0.066	−0.126	0.202	0.841
EE	0.072	−0.432	0.111	−0.116	−0.47	0.953
EX	0.218	−0.179	0.351	0.343	0.081	−0.031	1
MCS	−0.015	0.21	0.064	0.113	0.295	−0.65	0.032	0.974
MO	0.003	0.212	0.022	0.112	0.317	−0.632	0.043	0.963	0.966
NE	−0.301	0.223	−0.225	−0.503	−0.221	0.015	−0.448	−0.018	−0.053	1

AG = agreeableness; BD = behavioural disengagement; BE = behavioural engagement; CO = consciousness; ED = emotional disengagement, EE = emotional engagement; EX = extraversion; MCS = metacognitive-cognitive strategies; MO = motivation; NE = neuroticism.

Table 6.

Specific Indirect Effects on Multiple Mediation Analysis.

Relationships	Original sample	p-Value	Mediation type
Agreeableness → EE → BD → BE	0.0278	.009	Serial mediating effect
Agreeableness → EE → MCS → BE	0.002	.095	No effect
ED → EE → BD → BE	−0.076	4.216E-05	Serial mediating effect
ED → EE → MCS → BE	−0.006	.059	No effect
MO → ED → EE → BD	−0.045	3.888E-04	Serial mediating effect
MO → ED → EE → BD → BE	0.023	.002	Serial mediating effect
MO → ED → EE → MCS	0.008	.014	Serial mediating effect
MO → ED → EE → MCS → BE	0.001	.087	No effect
MO → ED → MCS → BE	0.002	.097	No effect
MO → EE → BD → BE	0.128	1.563E-11	Serial mediating effect
MO → EE → MCS → BE	0.010	.029	Serial mediating effect
Neuroticism → ED → EE → BD	0.030	1.468E-04	Serial mediating effect
Neuroticism → ED → EE → BD → BE	−0.016	6.009 E-04	Serial mediating effect
Neuroticism → ED → EE → MCS	−0.005	.015	Serial mediating effect
Neuroticism → ED → EE → MCS → BE	−0.001	.075	No effect
Neuroticism → ED → MCS → BE	−0.010	.092	No effect

BD = Behavioral disengagement; BE = Behavioral engagement; ED = Emotional disengagement; EE = Emotional engagement; MCS = Metacognitive-cognitive strategies; MO = Motivation.

Multigroup Analysis

The relationships described in the model of Figure 2 between students who passed and those who failed the course were compared using a multigroup analysis. For this purpose, the PLS-SEM multigroup analysis (PLS-MGA) tests whether the path coefficients differ significantly between the students who failed the course (73) and those who passed (301). Before carrying out the PLS-MGA, the measurement model invariance needs to be established by the measurement invariance of composite models (MICOM) procedure. The MICOM involves the following steps: (a) configural invariance, (b) compositional invariance, and (c) equality of composite mean values and variances. The partial measurement invariance was determined (steps one and two), as shown in Table 7.

Table 7.

Partial Measurement Invariance was Established for All Constructs.

	Compositional invariance			Equality of composite mean and variance
Construct	Correlation	5.0% quantile	p-Value	Difference	95% CI	p-Value
Agreeableness	1	1	.435	−0.0956739	[−0.264, 0.252]	.484
BD	0.999	0.998	.272	0.20564899	[−0.261, 0.245]	.111
BE	0.996	0.994	.11	−0.2261037	[−0.249, 0.254]	.086
Consciousness	1	1	.426	−0.2510032	[−0.248, 0.26]	.073
ED	0.999	0.959	.982	−0.1446942	[−0.275, 0.252]	.302
EE	0.999	0.999	.728	0.00528279	[−0.26, 0.283]	.976
Extraversion	1	1	.105	−0.077545	[−0.267, 0.262]	.56
MCS	0.999	0.999	.326	−0.1179969	[−0.28, 0.264]	.368
MO	0.999	0.999	.52	−0.1017927	[−0.284, 0.251]	.467
Neuroticism	1	1	.643	0.15575693	[−0.281, 0.246]	.251

BD = Behavioral disengagement, BE = Behavioral engagement; ED = Emotional disengagement; EE = Emotional engagement; MCS = Metacognitive-cognitive strategies; MO = Motivation.

The correlations between the composite scores of the first and second groups (correlation column) with the 5% quantile reveal that the quantile is always smaller than or equal to the correlation for all the constructs. Additionally, it is supported by a p-value higher than .05, indicating that the correlations are insignificant.

The equality of composite mean values and variances (step three) was established since the differences in Table 7 are within the confidence interval (CI). Additionally, the p-value shows no significant difference between the mean of latent variables across the two groups. Hence, the measurement invariance was fully established, reflecting the suitability of the data for multigroup analysis.

Although using the permutation test is the recommended method for multigroup analysis, the multimethod (PLS-MGA, parametric test and Welch-Satterthwait test) approach provides additional confidence in the final results obtained. Table 8 shows the multimethod result of the multigroup analysis for students who failed and passed the course. The differences between BD → BE, EE → MCS and MO → MCS were significant between the two groups. Note that the p values of the permutation, PLS-MGA, parametric test and Welch-Satterthwaite test are below 0.05. Additionally, the relationships where personality traits are involved do not differ between students who failed and passed.

Table 8.

Results of Multigroup Analysis.

Relationship\Analysis	Permutation		PLS-MGA		Parametric test		Welch-Satterthwait test
Relationship	Fail	Pass	Difference	2.5%	97.5%	p-Value	p-Value	p-Value	p-Value
Agreeableness → EE	0.089	0.112	−0.024	−0.183	0.189	.821	.765	.806	.777
BD → BE	−0.754	−0.463	−0.290	−0.213	0.209	.008	.015	.007	.015
Consciousness → BD	−0.316	−0.257	−0.059	−0.217	0.223	.593	.602	.613	.599
ED → BE	−0.326	−0.157	−0.169	−0.265	0.298	.251	.241	.224	.231
ED → EE	−0.293	−0.319	0.026	−0.227	0.225	.834	.810	.819	.799
ED → MCS	−0.037	−0.038	0.001	−0.078	0.073	.987	.988	.979	.975
EE → BD	−0.538	−0.443	−0.094	−0.248	0.271	.514	.422	.485	.430
EE → MCS	0.236	0.059	0.177	−0.141	0.146	.017	.025	.009	.015
Extraversion → BE	0.323	0.271	0.051	−0.235	0.205	.658	.628	.624	.622
MCS → BE	0.313	0.216	0.098	−0.333	0.332	.595	.564	.558	.563
MO → ED	0.406	0.285	0.121	−0.293	0.283	.449	.281	.402	.287
MO → EE	−0.581	−0.524	−0.058	−0.225	0.257	.660	.632	.634	.649
MO → MCS	0.807	0.939	−0.131	−0.096	0.086	.007	.016	.002	.010
Neuroticism → ED	0.305	0.183	0.122	−0.199	0.245	.275	.238	.298	.241

BD = Behavioural disengagement; BE = Behavioural engagement; ED = Emotional disengagement; EE = Emotional engagement; MCS = Metacognitive-cognitive strategies; MO = Motivation.

However, behavioral disaffection negatively impacts behavioral engagement, which is higher in students who passed than in those who failed. Similarly, motivation significantly affects metacognitive-cognitive strategies in students who passed more than in those who failed. In contrast, emotional engagement has a higher effect on metacognitive-cognitive strategies in students who failed than in those who passed.

Discussion

Personality traits directly affect academic engagement and disaffection but not motivation or metacognitive-cognitive strategies, which contrasts with the findings of Pathan et al. (2021) that conscientiousness and openness are negatively related to demotivation. Similarly, Abdullatif and Velázquez-Iturbide (2020), Brandt et al. (2020), and Mammadov et al. (2021) found that conscientiousness is related to learning motivation. Furthermore, students with high openness were found to use elaborative learning strategies (Brandt et al., 2020). However, our results contrast with this finding in that this personality trait did not affect the levels of motivation or academic engagement or the use of metacognitive-cognitive strategies in our data.

Disciplined and organized students are less passive and distracted in class. In contrast, neuroticism increased anxiety, boredom, frustration and sadness, as Villafañe et al. (2022) and Ongore (2014) found. Students who are cooperative, helpful and sympathetic toward others are enthusiastic and find learning activities fun and enjoyable (Ongore, 2014). Sociable, assertive, and talkative students (extraversion) show interest, pay attention and exert effort in class (behavioral engagement), which differs from the findings of Sangeetha et al. (2020) that extroverted students enjoy being with people and are enthusiastic and emotionally engaged with exciting tasks but not with behavioral engagement. Additionally, being empathetic and preferring cooperation over conflict (agreeableness) triggers positive emotions such as fun, pride, and enjoyment (emotional engagement), as reported previously (Quigley et al., 2022).

Personality traits had no relationship with motivation, as opposed to the findings of Lei et al. (2024), Pathan et al. (2021), and Peklaj et al. (2015). However, the relationships between motivation, emotional engagement, disengagement, and metacognitive-cognitive strategies were significant, consistent with previous findings (Acosta-Gonzaga & Ramirez-Arellano, 2022; Ramirez-Arellano et al., 2018). Additionally, no direct effects of conscientiousness, neuroticism, agreeableness, extraversion or openness on metacognitive-cognitive strategies were found.

The mediating analysis shows that agreeableness indirectly correlates with behavioral disengagement and metacognitive-cognitive strategies through emotional engagement. Similarly, neuroticism indirectly correlates with behavioral and emotional engagement mediated by emotional disaffection. The relationship between consciousness and behavioral disengagement is mediated by behavioral engagement. Thus, agreeableness, neuroticism, and consciousness play a substantial role in the educational context, especially through indirect effects on emotional and behavioral engagement and disaffection, as well as on metacognitive-cognitive strategies. The SEM and mediating analysis of openness suggest that this personality trait does not affect motivation, academic engagement, disaffection, or metacognitive-cognitive strategies. Hence, it was not included in the final model of Figure 2.

Competitive indirect relationships were found, suggesting that the constructs in direct impact and indirect effect have a suppressor role. For example, motivation decreases emotional disengagement; then, emotional disengagement limits the use of metacognitive-cognitive strategies (Figure 2). On the other hand, the direct effect of motivation on metacognitive-cognitive strategies fosters the latter; thus, this relationship tries to suppress the negative indirect impacts of motivation, emotional disengagement and metacognitive-cognitive strategies. The indirect path of motivation, emotional disengagement and emotional engagement is similar in that is competes with the positive direct effect of motivation on emotional engagement. Additionally, the path of emotional disengagement, emotional engagement and metacognitive-cognitive strategies has a suppressive effect on the negative impact of emotional disaffection on metacognitive-cognitive strategies. In contrast, the relationship between motivation and metacognitive-cognitive strategies and the path between motivation, emotional engagement, and metacognitive-cognitive strategies complement the positive effects of motivation on metacognitive-cognitive strategies.

The multiple mediation analysis shows that agreeableness positively affects behavioral engagement through emotional engagement and behavioral disaffection. Similarly, neuroticism positively and indirectly impacts behavioral disengagement but negatively impacts behavioral engagement and metacognitive-cognitive strategies; all these indirect effects are mediated by emotional disaffection and behavioral engagement. Hence, agreeableness and neuroticism indirectly affect academic behaviors mediated by emotional engagement and disaffection.

Furthermore, comparing the path coefficient of students who failed and those who passed suggests that learning performance does not determine a stronger influence of personality traits (consciousness, neuroticism, agreeableness, and extraversion) on academic engagement. The difference in path coefficients indicates that the negative repercussions of being distracted, disinterested and passive (behavioral disaffection) on effort, attention, and interest (behavioral engagement) are higher in students who failed the course. On the other hand, motivation strongly fosters the use of metacognitive-cognitive strategies in students who passed compared to those who failed.

Conclusion

This research demonstrated that personality traits directly influence academic engagement and disaffection but not motivation or metacognitive-cognitive strategies. Additionally, several relationships between personality traits, motivation, academic engagement and cognitive-metacognitive strategies were introduced in a conceptual model.

Academic engagement mediates the relationship between personality traits and metacognitive-cognitive strategies. The effects of personality traits on academic engagement do not differ according to academic outcomes. However, the negative repercussions of behavioral disaffection on behavioral engagement are higher in students who failed the course, highlighting the central role of behavioral disaffection in learning performance. Additionally, motivation strongly fosters the use of metacognitive-cognitive strategies in students who passed compared to those who failed. This evidence suggests that behavioral disaffection and motivation should be the targets to design interventions to decrease the former and increase the latter.

The mediating analysis shows competitive and complementary indirect relationships. First, the adverse indirect effects are limited by the direct ones. In addition, both indirect and direct relationships positively impact the target construct. The finding shows that these two mediating relationships are widely presented in the results, supporting the conjecture that complex relationships occur among personality traits, motivation, academic engagement, and metacognitive-cognitive strategies. The results support the hypothesis that personality traits directly influence academic engagement and disaffection but not motivation and cognitive strategies. It paves the way for analyzing these interactions from the point of view of dynamic systems. however, the research is limited by the constructs being measured using students’ self-reports and all participants being enrolled in the same university.

Footnotes

Acknowledgements

A special mention is given to Lawrence Whitehill, who tuned the English language of this article.

Author Contributions

Conceptualization, A.R.-A.; formal analysis, A.R.-A.; investigation, A.R.-A.; supervision, A.R.-A.; writing—original draft, A.R.-A.; writing—review & editing, A.R.-A. All authors have read and agreed to the published version of the manuscript.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Instituto Politécnico Nacional, grant number 20240278, funded this research.

Ethical Approval

Ethical review and approval were not required for the study on human participants in accordance with the local legislation and institutional requirements.

Informed Consent Statements

The students provided their written informed consent to participate in this study.

ORCID iD

Aldo Ramirez-Arellano

Data Availability Statement

The data supporting this study’s findings are available from the corresponding author upon reasonable request.

References

Abdullatif

Velázquez-Iturbide

J. Á

. (2020). Relationship between motivations, personality traits and intention to continue using MOOCs. Education and Information Technologies, 25(5), 4417–4435. https://doi.org/10.1007/s10639-020-10161-z

Acosta-Gonzaga

(2023). The effects of self-esteem and academic engagement on university students’ performance. Behavioral Sciences, 13(4), 348. https://doi.org/10.3390/bs13040348

Acosta-Gonzaga

Ramirez-Arellano

(2021). The influence of motivation, emotions, cognition, and metacognition on students’ learning performance: A comparative study in higher education in blended and traditional contexts. SAGE Open, 11(2), 21582440211027561. https://doi.org/10.1177/21582440211027561

Acosta-Gonzaga

Ramirez-Arellano

(2022). Scaffolding matters? Investigating its role in motivation, engagement and learning achievements in higher education. Sustainability, 14(20), 13419. https://doi.org/10.3390/su142013419

Allen

J. L.

Sylaska

Mayer

J. D.

(2021). Dynamic pathways of personality: A further development of the personality systems framework. In Rauthmann

J. F.

(Ed.), The handbook of personality dynamics and processes (pp. 689–707). Academic Press.

Angelovska

Mercer

Talbot

K. R.

(2021). Personality traits as predictors of language learner engagement. Language Learning in Higher Education, 11(2), 285–310. https://doi.org/10.1515/cercles-2021-2026

Bonett

D. G.

Wright

T. A.

(2015). Cronbach’s alpha reliability: Interval estimation, hypothesis testing, and sample size planning. Journal of Organizational Behavior, 36(1), 3–15. https://doi.org/10.1002/job.1960

Bouiri

Lotfi

Talbi

(2021). Correlative study between personality traits, student mental skills and educational outcomes. Education Sciences, 11(4), 153. https://doi.org/10.3390/educsci11040153

Brambila-Tapia

A. J. L.

Miranda-Lavastida

A. J.

Vázquez-Sánchez

N. A.

Franco-López

N. L.

Pérez-González

M. C.

Nava-Bustos

Gutiérrez-Rodríguez

F. J.

Mora-Moreno

F. F.

(2022). Association of health and psychological factors with academic achievement and non-verbal intelligence in university students with low academic performance: The influence of sex. International Journal of Environmental Research and Public Health, 19(8), 4804. https://doi.org/10.3390/ijerph19084804

10.

Brandt

N. D.

Lechner

C. M.

Tetzner

Rammstedt

(2020). Personality, cognitive ability, and academic performance: Differential associations across school subjects and school tracks. Journal of Personality, 88(2), 249–265. https://doi.org/10.1111/jopy.12482

11.

Brown

(1987). Metacognition, executive control, self-regulation, and other more mysterious mechanisms. In Weinert

F. E.

Kluwe

(Eds.), Metacognition, motivation, and understanding (pp. 65–116). L. Erlbaum Associates.

12.

Carmona-Halty

Salanova

Llorens

Schaufeli

W. B.

(2021). Linking positive emotions and academic performance: The mediated role of academic psychological capital and academic engagement. Current Psychology, 40(6), 2938–2947. https://doi.org/10.1007/s12144-019-00227-8

13.

Costa

P. T.

McCrae

R. R.

(1992). Neo PI-R professional manual. Psychological Assessment Resources.

14.

Costa

P. T.

McCrae

R. R.

(1999). A five-factor theory of personality. In Pervin

L. A.

John

O. P.

(Eds.), Handbook of personality: Theory and research (pp. 139–153). Guilford Press.

15.

Costa

P. T.

McCrae

R. R.

(2008). The revised NEO personality inventory (NEO-PI-R). Sage Publications.

16.

Cummings

D. J.

Loxton

N. J.

Poropat

A. E.

(2019). The relevance/significance of stimuli appraisals for personality traits in an academic context. Learning and Individual Differences, 69, 225–231. https://doi.org/10.1016/j.lindif.2018.04.008

17.

Dumfart

Neubauer

A. C.

(2016). Conscientiousness is the most powerful noncognitive predictor of school achievement in adolescents. Journal of Individual Differences, 37, 8–15. https://doi.org/10.1027/1614-0001/a000182

18.

Eccles

(1983). Expectancies, values and academic behaviors. In Spence

J. T.

(Ed.), Achievement and achievement motives: Psychological and sociological approaches (pp. 75–146). Free Man.

19.

Eccles

J. S.

Wigfield

(2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61, 101859. https://doi.org/10.1016/j.cedpsych.2020.101859

20.

Efklides

(2011). Interactions of metacognition with motivation and affect in self-regulated learning: The MASRL model. Educational Psychologist, 46(1), 6–25. https://doi.org/10.1080/00461520.2011.538645

21.

Efklides

(2012). Metacognition, motivation, and affect in the school context: Metacognitive experiences in the regulation of learning. In Kreitler

(Ed.), Cognition and motivation: Forging an interdisciplinary perspective (pp. 383–406). Cambridge University Press.

22.

Efklides

(2017). Affect, epistemic emotions, metacognition, and self-regulated learning. Teachers College Record, 119(13), 1–22. https://doi.org/10.1177/016146811711901302

23.

Hair

Hult

G. T. M.

Ringle

Sarstedt

(2022). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.

24.

Hernández-Peña

Gea-García

G. M.

García-Fuentes

J. P.

Martínez-Aranda

L. M.

Menayo Antúnez

(2023). Personality trait changes in athletic training students during their university career: Effects of academic stress or COVID-19 pandemic? Sustainability, 15(1), 486. https://doi.org/10.3390/su15010486

25.

Jones

B. D.

Khajavy

G. H.

Mohamed

H. E.

Reilly

(2023). Examining the cross-cultural validity of the MUSIC model of academic motivation inventory in English language courses. SAGE Open, 13(1), 21582440231156583. https://doi.org/10.1177/21582440231156583

26.

Kim

Park

S. W.

Cozart

(2014). Affective and motivational factors of learning in online mathematics courses. British Journal of Educational Technology, 45(1), 171–185. https://doi.org/10.1111/j.1467.2012.01382.x

27.

King

R. B.

Zhang

Wen

Xie

Cai

(2023). Gratitude is its own reward: How grateful students have better motivation and engagement. Educational Psychology, 43(4), 284–303. https://doi.org/10.1080/01443410.2023.2205069

28.

Kuchinski-Donnelly

Krouse

A. M.

(2020). Predictors of emotional engagement in online graduate nursing students. Nurse Educator, 45(4), 214–219. https://doi.org/10.1097/nne.0000000000000769

29.

Kuśnierz

Rogowska

A. M.

Pavlova

(2020). Examining gender differences, personality traits, academic performance, and motivation in Ukrainian and Polish students of physical education: A cross-cultural study. International Journal of Environmental Research and Public Health, 17(16), 5729. https://doi.org/10.3390/ijerph17165729

30.

Lechner

Danner

Rammstedt

(2017). How is personality related to intelligence and achievement? A replication and extension of Borghans et al. and Salkever. Personality and Individual Differences, 111, 86–91. https://doi.org/10.1016/j.paid.2017.01.040

31.

Lei

Chen

Luo

(2024). The examination of the relationship between learning motivation and learning effectiveness: A mediation model of learning engagement. Humanities and Social Sciences Communications, 11(1), 137. https://doi.org/10.1057/s41599-024-02666-6

32.

Linnenbrink

E. A.

Pintrich

P. R.

(2000). Chapter 8 - Multiple pathways to learning and achievement: The role of goal orientation in fostering adaptive motivation, affect, and cognition. In Sansone

Harackiewicz

J. M.

(Eds.), Intrinsic and extrinsic motivation (pp. 195–227). Academic Press.

33.

Ljubin-Golub

Petričević

Rovan

(2019). The role of personality in motivational regulation and academic procrastination. Educational Psychology, 39(4), 550–568. https://doi.org/10.1080/01443410.2018.1537479

34.

Mahama

Dramanu

B. Y.

Eshun

Nandzo

Baidoo-Anu

Amponsah

M. A.

(2022). Personality traits as predictors of self-regulated learning and academic engagement among college students in Ghana: A dimensional multivariate approach. Education Research International, 2022, 2255533. https://doi.org/10.1155/2022/2255533

35.

Mammadov

(2022). Big Five personality traits and academic performance: A meta-analysis. Journal of Personality, 90(2), 222–255. https://doi.org/https://doi.org/10.1111/jopy.12663

36.

Mammadov

Cross

T. L.

Olszewski-Kubilius

(2021). A look beyond aptitude: The relationship between personality traits, autonomous motivation, and academic achievement in gifted students. Roeper Review, 43(3), 161–172. https://doi.org/10.1080/02783193.2021.1923595

37.

Mayer

J. D.

(2003). Structural divisions of personality and the classification of traits. Review of General Psychology, 7(4), 381–401. https://doi.org/10.1037/1089-2680.7.4.381

38.

Mayer

J. D.

(2005). A tale of two visions: Can a new view of personality help integrate psychology? American Psychologist, 60(4), 294–307. https://doi.org/10.1037/0003-066x.60.4.294

39.

Mayer

J. D.

(2015). The personality systems framework: Current theory and development. Journal of Research in Personality, 56, 4–14. https://doi.org/10.1016/j.jrp.2015.01.001

40.

Mayer

J. D.

(2020). An integrated approach to personality assessment based on the personality systems framework. Journal of Personality Assessment, 102(4), 443–456. https://doi.org/10.1080/00223891.2018.1555539

41.

Mayer

J. D.

Allen

J. L.

(2013). A personality framework for the unification of psychology. Review of General Psychology, 17(2), 196–202. https://doi.org/10.1037/a0032934

42.

Mayer

J. D.

Korogodsky

(2011). A really big picture of personality. Social and Personality Psychology Compass, 5(2), 104–117. https://doi.org/10.1111/j.1751-9004.2010.00336.x

43.

McCrae

R. R.

Costa

P. T.

(1987). Validation of the five-factor model of personality across instruments and observers. Journal of Personality and Social Psychology, 52(1), 81-90. https://doi.org/10.1037//0022-3514.52.1.81

44.

McKeachie

W. J.

Pintrich

P. R.

Lin

Y. G.

(1985). Teaching learning strategies. Educational Psychologist, 20(3), 153–160. https://doi.org/10.1207/s15326985ep2003_5

45.

McKeachie

W. J.

Pintrich

P. R.

Lin

Y. G.

Smith

D. A. F.

(1986). Teaching and learning in the college classroom: A review of the research literature. The University of Michigan.

46.

Meyer

Fleckenstein

Retelsdorf

Köller

(2019). The relationship of personality traits and different measures of domain-specific achievement in upper secondary education. Learning and Individual Differences, 69, 45–59. https://doi.org/10.1016/j.lindif.2018.11.005

47.

Nichols

S. L.

Dawson

H. S.

(2012). Assessment as a context for student engagement. In Christenson

S. L.

Reschly

A. L.

Wylie

(Eds.), Handbook of research on student engagement (pp. 457–477). Springer.

48.

Ongore

(2014). A study of relationship between personality traits and job engagement. Procedia-Social and Behavioral Sciences, 141, 1315–1319. https://doi.org/10.1016/j.sbspro.2014.05.226

49.

Pathan

Z. H.

Ismail

S. A. M. M.

Fatima

(2021). English language learning demotivation among Pakistani university students: Do resilience and personality matter? Journal of Applied Research in Higher Education, 13(4), 1024–1042. https://doi.org/10.1108/JARHE-04-2020-0087

50.

Peklaj

Podlesek

Pečjak

(2015). Gender, previous knowledge, personality traits and subject-specific motivation as predictors of students’ math grade in upper-secondary school. European Journal of Psychology of Education, 30(3), 313–330. https://doi.org/10.1007/s10212-014-0239-0

51.

Pintrich

Wolters

Baxter

(2000). Assessing metacognition and self-regulated learning. In Schraw

Impara

(Eds.), Issues in the measurement of metacognition (pp. 43–97). Buros Institute of Mental Measurements.

52.

Pintrich

P. R.

(1988a). A process-oriented view of student motivation and cognition. New Directions for Institutional Research, 1988(57), 65–79. https://doi.org/10.1002/ir.37019885707

53.

Pintrich

P. R.

(1988b). Student learning and college teaching. New Directions for Teaching and Learning, 1988(33), 71–86. https://doi.org/10.1002/tl.37219883308

54.

Pintrich

P. R.

(1991). A Manual for the use of the motivated strategies for learning questionnaire (MSLQ). University of Michigan; U.S. Dept. of Education, Office of Educational Research and Improvement, Educational Resources Information Center.

55.

Pintrich

P. R.

De Groot

E. V.

(1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82, 33–40. https://doi.org/10.1037/0022-0663.82.1.33

56.

Pintrich

P. R.

Garcia

(1991). Self-regulated learning in college students: Knowledge, strategies, and motivation. In Pintrich

P. R.

Brown

Weinstein

C. E.

(Eds.), Student motivation, cognition, and learning (pp. 113–134). Lawrence Erlbaum Associates.

57.

Pintrich

P. R.

Garcia

(1994). Self-regulated learning in college students: Knowledge, strategies, and motivation. In Pintrich

P. R.

Brown

D. R.

Weinstein

C. E.

(Eds.), Student motivation, cognition, and learning: Essays in honor of Wilbert J McKeachie (pp. 113–133). Lawrence Erlbaum Associates.

58.

Quigley

Bradley

Playfoot

Harrad

(2022). Personality traits and stress perception as predictors of students’ online engagement during the COVID-19 pandemic. Personality and Individual Differences, 194, 111645. https://doi.org/10.1016/j.paid.2022.111645

59.

Qureshi

Wall

Humphries

Balani

A. B.

(2016). Can personality traits modulate student engagement with learning and their attitude to employability? Learning and Individual Differences, 51, 349–358. https://doi.org/10.1016/j.lindif.2016.08.026

60.

Ramirez-Arellano

Acosta-Gonzaga

Bory-Reyes

Hernández-Simón

L. M.

(2018). Factors affecting student learning performance: A causal model in higher blended education. Journal of Computer Assisted Learning, 34(6), 807–815. https://doi.org/10.1111/jcal.12289

61.

Ramírez

Larruzea-Urkixo

Garay

(2022). Adaptation to the Spanish university context and psychometric properties of the MSLQ: Contributions to the measurement and analysis of gender differences of self-regulated learning. Anales de Psicología, 38(2), 295–306. https://doi.org/10.6018/analesps.444851

62.

Reeve

(2012). A self-determination theory perspective on student engagement. In Christenson

S. L.

Reschly

A. L.

Wylie

(Eds.), Handbook of research on student engagement (pp. 149–172). Springer.

63.

Reschly

A. L.

(2020). Interventions to enhance academic engagement. In Reschly

A. L.

Pohl

A. J.

Christenson

S. L.

(Eds.), Student engagement: Effective academic, behavioral, cognitive, and affective interventions at school (pp. 91–108). Springer International Publishing.

64.

Reschly

A. L.

Christenson

S. L.

(2012). Jingle, jangle, and conceptual haziness: Evolution and future directions of the engagement construct. In Christenson

S. L.

Reschly

A. L.

Wylie

(Eds.), Handbook of research on student engagement (pp. 3–19). Springer.

65.

Robayo-Tamayo

Blanco-Donoso

L. M.

Román

F. J.

Carmona-Cobo

Moreno-Jiménez

Garrosa

(2020). Academic engagement: A diary study on the mediating role of academic support. Learning and Individual Differences, 80, 101887. https://doi.org/10.1016/j.lindif.2020.101887

66.

Rodríguez-Gonzalez

Anderson

Osorio

Lafontaine

M. F.

Greenman

P. S.

Calatrava

Andrade

Lybbert

Martínez-Diaz

Steffen

De Irala

Sandberg

(2022). Efficacy of emotionally focused therapy among Spanish-speaking couples: Study protocol of a randomized clinical trial in Argentina, Costa Rica, Guatemala, Mexico, and Spain. Trials, 23(1), 891. https://doi.org/10.1186/s13063-022-06831-7

67.

Sangeetha

Kannan

Savarimuthu

(2020). Personality as a predictor for student engagement. International Journal of Advanced Science and Technology, 29(6), 3412–3420.

68.

Schuster

Stebner

Leutner

Wirth

(2020). Transfer of metacognitive skills in self-regulated learning: An experimental training study. Metacognition and Learning, 15(3), 455–477. https://doi.org/10.1007/s11409-020-09237-5

69.

Skinner

Furrer

Marchand

Kindermann

(2008). Engagement and disaffection in the classroom: Part of a larger motivational dynamic? Journal of Educational Psychology, 100, 765–781. https://doi.org/10.1037/a0012840

70.

Skinner

E. A.

Kindermann

T. A.

Connell

J. P.

Wellborn

J. G.

(2009). Engagement and disaffection as organizational constructs in the dynamics of motivational development. In Handbook of motivation at school (pp. 223–245). Routledge/Taylor & Francis Group.

71.

Skinner

E. A.

Kindermann

T. A.

Furrer

C. J.

(2008). A motivational perspective on engagement and disaffection: Conceptualization and assessment of children’s behavioral and emotional participation in academic activities in the classroom. Educational and Psychological Measurement, 69, 493–525. https://doi.org/10.1177/0013164408323233

72.

Tavakol

Dennick

(2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd

73.

Thomas

C. L.

Allen

(2021). Driving engagement: Investigating the influence of emotional intelligence and academic buoyancy on student engagement. Journal of Further and Higher Education, 45(1), 107–119. https://doi.org/10.1080/0309877X.2020.1741520

74.

Villafañe

López

L. E.

Martínez

D. S.

Del Pino Sánchez López

(2022). Relationships between academic engagement and personality factors in nursing students. Revista Fuentes, 24(2), 137–148. https://doi.org/10.12795/revistafuentes.2022.19025

75.

Villarreal

Giraldo

(2022). Adaptation and validity of the scale of motivation of the motivated scale learning questionnaire (MSLQ) in Colombian university students. Electronic Journal of Research in Educational Psychology, 20(56), 119–150. https://doi.org/10.25115/ejrep.v20i56.4394

76.

Wiley

Jee

B. D.

(2010). Cognition: Overview and recent trends. In Peterson

Baker

McGaw

(Eds.), International encyclopedia of education (pp. 245–250). Elsevier.

77.

Winne

P. H.

(2018). Cognition and metacognition within self-regulated learning. In Handbook of self-regulation of learning and performance (pp. 36–48). Routledge/Taylor & Francis Group.

78.

Wolters

C. A.

Taylor

D. J.

(2012). A self-regulated learning perspective on student engagement. In Christenson

S. L.

Reschly

A. L.

Wylie

(Eds.), Handbook of research on student engagement (pp. 635–651). Springer.

79.

Xie

Cobb

C. L.

(2020). Revised NEO personality inventory (NEO-PI-R). In Carducci

B. J.

Nave

C. S.

Mio

J. S.

Riggio

R. E.

(Eds.), The Wiley encyclopedia of personality and individual differences: Measurement and assessment (pp. 335–350). John Wiley & Sons.

80.

Yang

Yuan

Tan

Wang

(2021). The linkages between Chinese children’s both cognitive engagement and emotional engagement and behavioral engagement: Mediating effect of perceptions of classroom interactions in math. Psychology in the Schools, 58(10), 2017–2030. https://doi.org/10.1002/pits.22571

81.

Zhang

Wang

(2023). Effect of English learning motivation on academic performance among English majors in China: The moderating role of certain personality traits. Psychology Research and Behavior Management, 16, 2187–2199. https://doi.org/10.2147/prbm.s407486