1 Introduction

While the usage of online learning strategies has grown over the past few decades, the pandemic has compelled all academic institutions to move online learning environments. The landscape of higher education has witnessed a significant shift towards online learning environments, propelled further by the global COVID-19 pandemic. This paradigmatic change underscores the pressing need to explore and understand the intricacies of student engagement within these digital contexts. While prior research has examined various factors influencing student engagement in traditional educational settings, the unique dynamics of online learning environments necessitate a nuanced investigation that considers the interplay between individual differences, self-regulated learning strategies, and engagement. This study seeks to address this gap by delving into the mediating role of self-regulated online learning in the relationship between five-factor personality traits and student engagement among university students. While previous studies have explored the influence of personality traits and self-regulation on academic outcomes, few have specifically focused on their impact within the context of online learning environments. By examining these relationships, this study contributes to the existing literature by providing novel insights into the mechanisms underlying student engagement in online education. Furthermore, this study builds upon recent literature that highlights the importance of considering individual differences and self-regulated learning strategies in online learning contexts (e.g., Smith et al., 2021; Xu et al., 2022). By incorporating these more recent studies, this study aims to provide a comprehensive understanding of the factors shaping student engagement in the digital age.

1.1 Student engagement

Regardless of the delivery method, much educational research has been concerned about student involvement since it is crucial for students’ academic success (Whitehill et al., 2014; Bond et al., 2020). Student engagement is found to be a key factor in students’ academic progress as well as their overall positive study experiences (Carini et al., 2006). Student engagement can be explained with behavioral, cognitive and emotional engagement (Fredricks et al., 2004): Behavioral engagement happens when students actively engage in educational activities. Cognitive engagement happens when students exhibit a desire to study and demonstrate self-regulation in their learning. Emotional engagement happens when students have favorable attitudes in the learning environment. As Fredricks et al. (2004) stated, teachers and other stakeholders have the chance to develop teaching strategies that make the most of students’ learning behaviors when they are aware of students’ academic involvement. In addition, existing research suggests that self-regulated learning (SRL) and student engagement are critical to students’ academic achievement (Fredricks et al., 2004; Panadero, 2017; Zimmerman, 2000).

Despite the value of student engagement in academic achievement, there is relatively little research on student involvement in online education (Paulsen & McCormick, 2020). However, because of the nature of online learning environment, it is crucial to study and understand the factors influencing online student engagement. Thus, it’s important to comprehend what motivates students to participate in online learning activities.

1.2 Personality traits and self-regulated learning

Self-regulated learning is the process by which learners achieve personal learning goals (Zimmerman, 1989). Self-regulation encompasses a learner’s cognitive, metacognitive, behavioral, motivational, and emotional aspects. Proficient self-regulated learners can monitor their progress and adjust strategies as needed. Conversely, poor self-regulated learners often fail to employ effective learning strategies, leading to feelings of anxiety and fear of failure (Zimmerman, 1989).

A person’s distinct and largely enduring way of reacting to situations is referred to as their personality (Passer & Smith, 2004). In terms of personality traits, the five-factor model (McCrae & Costa, 1989) has been found to be the most frequent and empirically solid model of personality studies, focusing on five traits (agreeableness, extraversion, conscientiousness, openness to experience, and neuroticism) (McCrae & Costa, 1989). Agreeableness evaluates attributes such as kindness, cooperativeness, and consideration, reflecting how an individual interacts with others. This dimension encompasses sub-dimensions including warmth, empathy, generosity, and morality (Goldberg, 1990). Extraversion evaluates an individual’s sociability, enthusiasm, and enjoyment, with those classified as extraverts typically displaying traits such as optimism, a penchant for adventure, talkativeness, vitality, assertiveness, and an active disposition (McCrae & Costa, 1989). Conscientiousness pertains to an individual’s effectiveness, accuracy, perseverance, organizational capabilities, coordination, and diligence (McCrae & Costa, 1989). One’s openness to experience is evidenced by traits such as creativity, artistic inclination, intellectual depth, and insightful thinking (McCrae & Costa, 1989). Neuroticism, characterized by low emotional stability, is linked to diminished well-being, as individuals with emotional instability are prone to encountering negative emotions (McCrae & Costa, 1991).

Empirical research has investigated the relationship between students’ personality traits and SRL strategies in different learning contexts. For instance, Dörrenbächer and Perels (2016) found that low neuroticism was a moderate predictor of SRL components found in students. The study also found that students employed moderate to high SRL techniques when compared to students with lower levels of openness, agreeableness, conscientiousness, and extraversion. In addition, Ghyasi et al. (2013) found that students’ learning strategies were influenced by their personality traits. The researchers reported that high conscientious students were more likely to utilize all techniques, while individuals with high levels of extraversion were more inclined to use help-seeking techniques like peer learning, and those with low extraversion were more inclined to regulate their time and study space.

Apart from these studies examining the relationship between students’ personality traits and their SRL strategies in regular education settings, there have been studies exploring the relationships between online education and self-regulation (e.g., Barak et al., 2016; Cho & Kim, 2013; Wilson & Narayan, 2016). Considering that a high level of learner autonomy is required for students’ learning in online education environments and there is a low level of teacher presence, students’ SRL behaviors gain particular importance in online learning environments (Lehmann et al., 2014). Students must choose when to study and how to approach study materials on their own in online environments with limited teacher presence. Therefore, in such an environment, SRL behaviors become an important factor affecting students’ learning. However, the studies conducted so far had not particularly investigated personality characteristics and the usage of SRL techniques in online learning. A recent study by Bruso et al. (2020) investigated these two concepts for online education, as one of the few studies carried out in the concept of online learning, and discovered that openness, extraversion, agreeableness, and conscientiousness, but not neuroticism, were correlated with a higher likelihood of using SRL methods.

1.3 Personality traits and student engagement

Numerous empirical studies have revealed a connection between personality traits and students’ engagement. For instance, Qureshi et al. (2016) examined whether a student’s personality can influence their involvement and attitude toward employability and found that student participation was linked to a variety of personality factors. Similarly, Caraway et al. (2003) found that students’ engagement is influenced by their personality and self-efficacy. Additionally, previous research showed that engagement and academic progress were moderated by personality traits (Mahama et al., 2022; Ongore, 2014; Strauser et al., 2012; Qureshi et al., 2016).

While prior studies have focused on the effects of personality on engagement proxies, the aforementioned conclusions were reached in a regular classroom setting. Recently, Zhang et al. (2020) explored the impact of the group’s personality composition on students’ engagement, specifically in online discussions. The study found that the group’s personalities may have an impact on student involvement, particularly affective engagement. Considering the study’s findings, the researchers advised instructors to carefully evaluate the personality traits of the group members when organizing students’ online discussions.

While there are studies exploring the relationship between personality traits and student engagement in face-to-face education, studies are limited in the online context. SRL and student engagement have been studied in conjunction with students’ personality traits in several different contexts (Ghyasi et al., 2013; Jensen, 2015; Mahama et al., 2022; Ozan et al., 2012; Qureshi et al., 2016). The studies found that students’ learning strategies were influenced by their personality types as well as their academic engagement (Ghyasi et al., 2013; Jensen, 2015). In addition, there have been studies that focused on only two of these concepts in online learning. For example, personality traits and SRL in online education (e.g., Bruso et al., 2020); personality traits and student engagement in online education (e.g., Zhang et al., 2020) have been studied by a few researchers. However, there has been no research into the impact of personality traits on students’ self-regulation and engagement in the context of online education. Thus, the study intends to explore the relationship between students’ personality traits, self-regulated learning, and academic engagement in online education to produce data that will contribute to the field and fill the gap in the literature.

1.4 Purpose of the study

The unprecedented disruptions caused by the COVID-19 pandemic have underscored the importance of understanding the impact of external factors on online learning. The transition to remote learning has introduced new challenges, such as technological barriers, social isolation, and heightened academic stress, which may influence students’ engagement in online courses (Joubert et al., 2023; Sari & Mediatati, 2023). This study aims to provide timely insights into mitigating these challenges and optimizing student engagement in online learning environments. The notion of self-regulation, rooted in the theoretical foundations of Bandura’s Social Cognitive Theory (Bandura, 1986), involves an individual’s ability to achieve the intended behavior by interacting with their environment. Social cognitive theory provides a comprehensive framework for understanding human behavior by emphasizing the reciprocal interaction between personal factors, environmental influences, and behavior. In the context of online learning, this theory suggests that students’ engagement is influenced not only by their inherent personality traits but also by their perceptions of the online learning environment and their ability to regulate their learning process. In the context of online learning, students’ engagement may be influenced by their perceptions of the effectiveness of online instructional methods, the availability of social support networks, and their own beliefs in their ability to succeed in the online environment. Considering social cognitive theory, this study seeks to explore how students’ personality traits and self-regulatory processes interact to influence their engagement in online learning. By understanding the underlying mechanisms driving student engagement, we can identify strategies to optimize the online learning experience and promote student success in online learning environments.

The general aim of this study is to determine the mediating role of self-regulated online learning in the effect of five factor personality traits on student’s engagement in online learning environment among university students (see Fig. 1). The study fills existing gaps in the literature by elucidating the complex interplay between personality traits, self-regulated online learning, and student engagement in online education. By clarifying the novelty and innovation of the study, articulating its theoretical foundations, and incorporating recent literature, the study aims to contribute to a deeper understanding of how to promote effective online learning experiences for university students. In this direction, the following hypotheses were formed.

  • H1: Five factor personality traits are a significant predictor of self-regulated online learning behaviors.

Recognizing the unique demands of online education, where high learner autonomy is essential due to limited teacher presence, emphasizes the importance of students’ SRL strattegies in online learning environments (Lehmann et al., 2014). Given the necessity for students to independently manage their study schedules and approach study materials, SRL behaviors become pivotal in online environments. Additionally, in the educational context, each student brings a unique blend of personality traits, preferences, and abilities. These components have the potential to influence how adeptly learners utilize skills and strategies to achieve their academic goals. Therefore, personality traits may play a significant role in predicting individuals’ self-regulated learning behaviors within the distinctive context of online education.

  • H2: Self-regulated online learning behaviors are a significant predictor of student’s engagement in online learning environment.

While prior research contributes to the understanding of SRL profiles and the relationship between learner’s self-regulatory activities and their achievement or performance in traditional settings (Barnard-Brak et al., 2010; Pintrich, 2004), a gap exists in understanding the relationship between self-regulated online learning behaviors and student’s engagement especially in higher education settings and online environments. Additionally, it is important to understand the relationships between the dimensions of engagement (behavioral, cognitive, and emotional) and students’ self-regulated online learning behaviors.

  • H3: Five-factor personality traits are a significant predictor of students’ engagement in the online learning environment.

Existing studies have primarily focused on the relationship between personality traits and students’ engagement in traditional learning environments (Mahama et al., 2022; Ongore, 2014; Strauser et al., 2012; Qureshi et al., 2016). Nevertheless, there has been limited attention given to the associations between students’ personality traits and their engagement in the online learning environment. It is crucial to comprehend the relationships between the dimensions of personality traits (agreeableness, openness to experience, neuroticism, extraversion, and conscientiousness) and students’ engagement in the online learning environment.

  • H4: The mediation effect of self-regulated online learning behaviors is significant in the relationship between five factor personality traits and students’ engagement in online learning environment.

Acknowledging the uniqueness of the online learning environment, where autonomy and self-directedness are often emphasized (Azevedo & Hadwin, 2005; Chen et al., 2013), this study seeks to understand the mediation effect of self-regulated online learning on the relationship between five factor personality traits and students’ engagement in the online learning environment.

Fig. 1
figure 1

Hypothetical model.  Note.  Personality: Five factor personality traits: Self-Regulation: Self-Regulation Online Learning.  Engagement: Student’s Engagement in Online Learning Environment

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2 Method

2.1 Participants and procedure

The study was conducted as part of an elective Information Technologies (IT) course designed to illustrate the practical application of IT within specific disciplines. The courses were conducted by two faculty members within the Department of Computer Education and Instructional Technologies. Consistency was maintained across all courses, utilizing the same structure and syllabus. Spanning 14 weeks, the course syllabus was meticulously crafted based on the requisite needs of the curriculum. Course materials aligned with the syllabus, and students were obliged to attend synchronous sessions while concurrently completing weekly assignments that pertained to the application of IT in their respective subject areas. The course, designed in a modular format, shares weekly assignments, readings, or practice files that need to be completed before the class. The students submitted their assignments weekly through the Learning Management System (LMS). The instructional approach encompassed synchronous one-hour sessions complemented by asynchronous activities throughout the semester.

The study comprised a cohort of 437 university students enrolled in IT courses at a public university in Turkey. Participants were selected through convenient sampling, and the recruitment process was facilitated using Google Forms. Out of the 525 students enrolled in the courses, 437 participated in the study. Among the participants, 333 (76.2%) identified as female, while 104 (23.8%) identified as male. The distribution by grade level revealed 137 (31.4%) freshmen, 95 (21.7%) sophomores, 50 (11.4%) juniors, and 155 (35.5%) seniors. Additionally, participants belonged to diverse fields of study, including educational sciences (e.g., guidance and psychological counseling, elementary education), health sciences (e.g., nursing), and social sciences (e.g., theology).

2.2 Data collection tools

2.2.1 The big five inventory (BFI)

The BFI, developed by Benet-Martínez and John (1998) and translated into Turkish by (Sümer et al., 2005), consists of five dimensions (agreeableness, openness to experience, neuroticism, extraversion, and conscientiousness) with 44 items. Confirmatory factor analysis indicated an acceptable level of fit (χ2/df = 3.3, GFI = 0.94, CFI = 0.94, AGFI = 0.97, and RMSEA = 0.05) (Gökler & Taştan, 2018). In the current study, internal consistency was assessed through reliability analysis, revealing values of α = 0.63 for agreeableness, α = 0.73 for conscientiousness, α = 0.76 for extraversion, α = 0.66 for neuroticism, and α = 0.78 for openness. According to these findings, the internal consistencies of neuroticism and agreeableness dimensions were below 0.70, which can be interpreted as low measurement reliability (Nunnally, 1979).

2.2.2 Self-regulated online learning questionnaire (SOL-Q)

The SOL-Q, developed by Jansen et al. (2017), underwent adaptation to Turkish, along with validity and reliability studies conducted by Yavuzalp and Özdemir (2020). Comprising five dimensions (help-seeking, persistence, environmental structuring, metacognitive skills, and time management) with a 36-item structure, SOL-Q demonstrated robust psychometric properties. Exploratory factor analysis revealed a total explained variance of 62.06%, while confirmatory factor analysis indicated an acceptable level of fit (χ2/df = 4.21, RMSEA = 0.07, AGFI = 0.79, NNFI = 0.98, and CFI = 0.99) (Yavuzalp & Özdemir, 2020). The internal consistency reliability analysis findings for the entire SOL-Q yielded α = 0.96 in the original study and α = 0.97 in the current study (Yavuzalp & Özdemir, 2020).

2.3 Student’s engagements scale in online learning environment (SESOLE)

The SESOLE, developed by Sun and Rueda (2012), underwent adaptation to Turkish, including validity and reliability analyses conducted by Ergün and Usluel (2015). Comprising three dimensions (behavioral engagement, cognitive engagement, and affective engagement) with a total of 19 items, SESOLE demonstrated sound psychometric properties. Confirmatory factor analysis was employed for construct validity, yielding goodness-of-fit values at acceptable levels (χ2 (84, N = 398) = 453.93, GFI = 0.90, NNFI = 0.96, AGFI = 0.86, and RMSEA = 0.07) (Ergün & Usluel, 2015). Internal consistency reliability coefficients derived from the reliability study were found to be 0.62 for behavioral engagement, 0.90 for affective engagement, 0.86 for cognitive engagement, and 0.90 for the overall SESOLE (Ergün & Usluel, 2015). In the current study, internal consistency reliability analysis revealed α = 0.61 for behavioral engagement, α = 0.91 for affective engagement, α = 0.90 for cognitive engagement, and α = 0.91 for the overall SESOLE.

2.4 Data analysis

In this study, the assumptions of structural equation modeling were initially assessed, considering normality, bivariate correlations, and multicollinearity (Kline, 2015). Subsequently, a two-stage structural equation modeling approach was employed for data analysis (Anderson & Gerbing, 1988). To evaluate the accuracy of both the measurement and structural models, several metrics were considered, including Goodness of Fit Indices (χ2/df, GFI, AGFI, IFI, CFI, and RMSEA), factor loads (non-standardized and standardized), and t-values. Moreover, mediation analysis was conducted following the steps proposed by Baron and Kenny (1986). The significance of the mediation analysis was ultimately confirmed through bootstrap analysis, demonstrating that the confidence intervals, formed by 1000 resampling, did not encompass zero (Hayes, 2017; Shrout & Bolger, 2002).

3 Results

3.1 Preliminary analysis

In this study, the initial step involved testing the assumptions of structural equation modeling. Firstly, the assumption of normality was scrutinized, revealing skewness values ranging between − 0.80 and 0.20, and kurtosis values between − 1.02 and 0.62 (refer to Table 1). As these values fell below − 1.5 and + 1.5, respectively (Tabachnick & Fidell, 2007), it can be concluded that the normality assumption was satisfied. The multicollinearity assumption was then assessed. Tolerance values ranged from 0.31 to 0.82, and VIF values ranged between 1.20 and 3.18. Notably, no tolerance values approached zero, and the VIF values were below the threshold of 5–10 (Kline, 2015). Additionally, the correlation coefficient between the variables (see Table 2) did not reach 0.90 or above, indicating the absence of a multicollinearity problem.

Table 1 Descriptive statistics
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Table 2 Bivariate correlations
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3.2 Testing the two-stage structural equation modeling

3.2.1 Testing the measurement model (first stage)

The measurement model is represented by three latent variables: “five factor personality traits”, “self-regulation online learning”, “student’s engagement in online learning environment.” These are reflected by 13 observed variables: “neuroticism”, “agreeableness”, “extraversion”, “conscientiousness”, “openness”, “environmental structuring”, “persistence”, “time management”, “metacognitive skills”, “help seeking”, “behavioral engagement” cognitive engagement”, and affective engagement.”

The measurement model was assessed, and the goodness-of-fit values were found to be at an acceptable level (χ2/df (290.798/60) = 4.84, p = .00; GFI = 0.91, AGFI = 0.86, IFI = 0.90, CFI = 0.90, RMSEA = 0.09 (90) % = 0.083–0.105. Furthermore, in the examination of the measurement model, standardized factor loads were found to range from − 0.30 to 0.86, and all t-values were found to be statistically significant. Consequently, these results confirm the validation of the measurement model (see Fig. 2; Table 3).

Fig. 2
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Measurement Model.  Note. ***p < .001; **p < .01; *p < .05. Personality: Five factor personality traits: Self-Regulation: Self-Regulation Online Learning. Engagement: Student’s Engagement in Online Learning Environment

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Table 3 Measurement model (unstandardized factor loadings, standard errors, t-values)
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3.2.2 Testing the structural model (second stage)

In the initial stage, the measurement model underwent validation, and subsequently, the two-stage structural model was assessed. Upon testing the structural model, the goodness-of-fit indices were within an acceptable range (χ2/df (290.798/60) = 4.84, p = .00; GFI = 0.91, AGFI = 0.86, IFI = 0.90, CFI = 0.90, RMSEA = 0.09 (90%) = 0.083–0.105) (see Table 4). Furthermore, Fig. 3 illustrates the standardized path coefficients of the structural model, while Table 5 provides details on the non-standardized path coefficients, standard error, and t values.

Table 4 Goodness of fit indices of structural model
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Table 5 Structural model (unstandardized factor loadings, standard errors, t-values)
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Fig. 3
figure 3

Standardized path coefficient of the structural model. Note. ***p < .001; **p < .01; *p < .05. Personality: Five factor personality traits: Self-Regulation: Self-Regulation Online Learning. Engagement: Student’s Engagement in Online Learning Environment

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Upon examination of the results in Fig. 3, it is evident that a one-unit increase in five factor personality traits increases individuals’ self-regulated online learning by 0.48 units (t = 5.76; p < .001). In addition, a one-unit increase in self-regulated online learning increases individuals’ student’s engagement in online learning environment by 0.49 units (t = 8.55; p < .001).

Finally, a one-unit increase in five factor personality traits increases individuals’ student’s engagement in online learning environment by 0.45 units (t = 5.43; p < .001). In addition to these, when examining the explained variances, it is observed that five factor personality traits account for about 23% of self-regulated online learning. Furthermore, when considering both five-factor personality traits and self-regulated online learning, they collectively explain around 67% of students’ engagement in the online learning environment.

3.2.3 Moderator analysis: The role of self-regulation online learning

Mediation analysis was conducted following the steps suggested by Baron and Kenny (1986). Initially, it was determined that the direct effect (β = 68; t = 7.10) of the independent variable (five-factor personality traits) on the dependent variable (student’s engagement in the online learning environment) was significant (p < .001). When self-regulated online learning is introduced, the impact of the independent variable (five-factor personality traits) on the dependent variable (student engagement in the online learning environment) decreases (β = 45; t = 5.43) but remains statistically significant (p < .001). These findings provide evidence that the self-regulated online learning component mediates the effect of the independent variable (five-factor personality traits) on the dependent variable (student’s engagement in the online learning environment).

The bootstrap analysis findings in Table 6 are presented. The direct impact of five factor personality traits in student’s engagement in online environment is significant [β = 0.45, 95% CI (0.31, 57)]. In addition, the partial mediation effect of self-regulation online learning is significant in the relationship between five factor personality traits and student’s engagement in online learning environment [β = 0.23, 95% CI (0.17, 32)]. In conclusion, all these findings support the idea that self-regulation online learning has a partial mediation effect.

Table 6 Bootstrap analysis
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4 Discussion

The study aims to ascertain the mediating influence of self-regulated online learning in the relationship between five-factor personality traits and student engagement within the online learning environment among university students. Through mediation analysis conducted for this overarching goal, it was determined that self-regulated online learning exerted a partial mediation effect in connecting students’ five-factor personality traits with their engagement in the online environment. The interpretation of the current working hypothesis is elaborated upon in the context of existing literature.

4.1 Personality traits and self-regulated online learning

The first hypothesis of the study (H1), “five factor personality traits is a significant predictor of self-regulated online learning,” suggests that the personality traits of students participating in the research are determined to be a significant predictor of their online self-regulated learning behaviors, and the hypothesis has been confirmed. This finding aligns with earlier studies that have explored the correlation between personality traits and self-regulated learning behaviors (Babakhani, 2014; Bidjerano & Dai, 2007; Bruso et al., 2020; Chamorro-Premuzic & Furnham, 2003; De la Fuente et al., 2020; Dörrenbächer & Perels, 2016; Eilam et al., 2009; Ghyasi et al., 2013; Jensen, 2015; Mahama et al., 2022). Consequently, personal characteristics are identified as a pivotal factor influencing individuals’ adoption of self-regulated learning behaviors.

The five-factor personality model (McCrae & Costa, 1989) was examined in terms of its dimensions, revealing a positive relationship between conscientiousness, agreeableness, openness to experience, extraversion, and self-regulated learning. Additionally, a negative relationship was observed between neuroticism and self-regulated learning. Consequently, students possessing personality traits other than neuroticism tend to benefit from self-regulated learning behaviors in online environments. High levels of conscientiousness are associated with the extensive use of various self-regulated learning behaviors (metacognitive, time management, environmental structuring, effort regulation, and seeking help) compared to their less conscientious counterparts. Conscientiousness, which encompasses characteristics such as goal orientation, self-control, diligence, determination, regularity, and punctuality, constitutes the overarching domain of self-regulation (Koestner et al., 1992). Relevant literature indicates that students with high levels of conscientiousness frequently utilize strategies such as goal setting, adjusting appropriate study environments, managing time effectively, and putting effort into their learning (Bidjerano & Dai, 2007; Bruso et al., 2020; Komarraju & Karau, 2005). Even in unforeseen circumstances with limited control, individuals with high conscientiousness tend to maintain a more focused approach towards their goals (Morfaki & Skotis, 2023) and exhibit enhanced adaptation to online learning environments, as observed during events such as the Covid-19 pandemic (Besser et al., 2022).

Students who score high on the agreeableness personality dimension effectively utilize all self-regulated learning behaviors similarly to those with high levels of conscientiousness. The trait of agreeableness, characterized by harmony and cooperation, allows individuals to adjust their work habits based on the learning environment. This suggests that agreeable students are likely to voice fewer objections regarding learning environments and generally adhere more to teacher instructions (Vermetten et al., 2001). These individuals predominantly demonstrate self-regulated learning behaviors in collaborative learning environments (Bruso et al., 2020; De la Fuente et al., 2020; Eilam et al., 2009), with a particular emphasis on utilizing effort regulation, time management, and help-seeking strategies (Bidjerano & Dai, 2007; Bruso et al., 2020; Ghyasi et al., 2013).

Individuals who score high on the openness to experience personality dimension generally benefit from self-regulated learning behaviors. Open individuals possess strong problem-solving, critical thinking, and detailing skills, coupled with high metacognitive abilities (Bidjerano & Dai, 2007; Ghazi et al., 2013; Komarraju et al., 2011). These students, perceiving themselves as deep thinkers, intellectual, innovative, and imaginative, tend to exhibit better performance in the learning environment compared to those with low levels of openness to experience (Bidjerano & Dai, 2007; De la Fuente et al., 2020). However, students with this trait may face challenges in time management. Indeed, Ghazi et al. (2013) concluded that there is a negative relationship between openness to experience and self-regulation strategies involving time and study environment. While open individuals can concentrate intensely during their studies, characteristics such as their constant pursuit of new insights and deep thinking may lead them to lose track of time within a state of flow.

Extroverted individuals frequently use metacognitive and help-seeking strategies but show lower usage of time management, environmental structuring, and effort regulation strategies. This finding aligns with the results of other studies indicating that extroverted students predominantly utilize help-seeking strategies (Bidjerano & Dai, 2007; Bruso et al., 2020; Ghazi et al., 2013). Students with high levels of extroversion are academically more motivated and have higher learning goal orientations compared to those with low levels of extroversion (Payne et al., 2007). However, when working independently, they tend to seek socially stimulating activities that can distract their attention (Bernard, 2010). These students may struggle with tasks that require self-study and reflection, as their sociability, impulsivity, and distractibility hinder effective time management and effort regulation (Bidjerano & Dai, 2007; Bruso et al., 2020; Ghazi et al., 2013). Considering the inclination of extroverted individuals to prioritize social needs over academic needs, it suggests that socially distant online learning environments may pose challenges for these students (Morfaki & Skotis, 2023).

A negative relationship was observed between neuroticism and self-regulated learning. Individuals with higher levels of neuroticism tend to be less successful in using self-regulation strategies. This outcome aligns with the expectations, as several studies have highlighted the association between self-regulation and low neuroticism (e.g., Babakhani, 2014; Eilam et al., 2009; Ghyasi et al., 2013; Jensen, 2015). In this study, despite differences in the utilization of the help-seeking strategy compared to other strategies, no significant positive relationship was found, contrary to other research indicating that these individuals tend to seek help more frequently than other personality types (Bidjerano & Dai, 2007; De la Fuente et al., 2020). The unique structural features of online learning, different from traditional face-to-face learning environments, or the instructional format of the course may have contributed to these individuals experiencing anxiety and displaying hesitancy in seeking help.

Conscientiousness and agreeableness in students correlate with proficiency in utilizing nearly all self-regulated learning behaviors. Students demonstrating openness to experience, in particular, tend to employ metacognitive, help-seeking, and effort regulation strategies, while extroverted students predominantly benefit from the help-seeking strategy. Conversely, students with a neurotic personality face challenges in employing self-regulated learning behaviors. Consequently, it can be inferred that the design of online learning environments should aim to enhance the existing learning strategies of each learner, considering diverse individual characteristics, and encourage them to utilize strategies they may use less frequently or not at all.

4.2 Self-regulated learning and engagement

The second hypothesis of the study (H2), “self-regulated online learning behaviors is a significant predictor of student’s engagement in the online learning environment,” was confirmed, indicating that the online self-regulated learning behaviors of participating students are a significant predictor of their commitment to the online learning environment. In other words, students with advanced self-regulated learning skills exhibit higher engagement levels in the learning process. This finding aligns with the outcomes of other research studies demonstrating a close relationship between self-regulated learning and student engagement in the online environment (Binali et al., 2021; Cho & Jonassen, 2009; Cho & Kim, 2013; Mahama et al., 2022; Salas-Pilco et al., 2022; Sun & Rueda, 2012). Zimmerman and Schunk (2001) emphasize that students who apply self-regulated learning skills can become active participants in their learning. Individuals with high self-regulated learning skills are more inclined to explore, leading to increased engagement in the learning environment (Schwonke, 2015). According to Tao and colleagues (2020), students with low engagement levels invest less effort in the learning process and exhibit lower levels of self-regulated learning behaviors such as help-seeking, time management, task strategies, and self-assessment.

The dimensions that form the framework of both self-regulated learning and engagement are examined and discovered that the dimensions of the two variables overlap in many ways and are intertwined. Engagement is about learners’ efforts to research, apply, receive feedback, analyze and solve problems (Kuh, 2003). In self-regulated learning, learners engage in their own learning processes motivationally and strategically in line with their metacognitive strategies (Zimmerman, 1990). Thus, the individual’s effort is at the forefront in both self-regulated learning and engagement. Self-regulated learning is an umbrella concept that focuses on how students organize the behavioral, cognitive, affective, and motivational aspects of learning to achieve academic success (Pintrich, 2004). Commitment involves the active participation and involvement of students in activities related to success (Boekaerts, 2016), encompassing behavioral, cognitive, and emotional dimensions within a meta structure (Fredricks et al., 2004). Both conceptual frameworks are multidimensional and can be said to involve similar learning processes.

In this study, student engagement in online classes, a concept widely accepted in the literature, has been examined in three dimensions: behavioral, cognitive, and emotional (Fredricks et al., 2004). Behavioral engagement, which is the most researched type of engagement, encompasses participation in academic activities such as attending classes, dedicating time to academic tasks, and engaging in learning processes (asking questions, participating in discussions, etc.), as well as participation in social or extracurricular activities (Fredricks et al., 2004; Li & Lerner, 2013). Students who use self-regulated learning behaviors more frequently have demonstrated higher levels of behavioral engagement. Research results indicating that these students participate more frequently in online learning activities, spend more time in the learning environment by watching course videos or working with online materials (Salas-Pilco et al., 2022), support the findings of this study.

Cognitive engagement involves using self-regulated learning and making the necessary effort to understand complex ideas through deep learning strategies (Fredricks et al., 2004; Zimmerman, 1990). Although self-regulated learning and cognitive engagement are different constructs, they are intertwined concepts. Most self-regulated learning theories that rely on cyclical feedback systems to explain students’ learning processes are inherently cognitive (Cleary & Zimmerman, 2012). In a model proposed by Li and Lajoie (2022) that integrates self-regulated learning with cognitive engagement, it is argued that there is a role for cognitive engagement in each stage ofZimmerman’s (2000) three-stage self-regulated learning model (forethought, performance, and self-reflection). For instance, in the forethought stage, students plan the strategies to be used and the effort required to achieve their goals. In the performance stage, they choose cognitive strategies and control the level of using a strategy. In the self-reflection stage, students evaluate their current cognitive engagement to determine whether it is sufficient to achieve the expected performance. This demonstrates that cognitive engagement and self-regulated learning are intertwined concepts at each stage of Zimmerman’s model.

Emotional engagement refers to students’ attitudes towards educators, peers, and courses, as well as their appreciation for the subject and learning conditions, satisfaction, and feeling good about themselves (Salas-Pilco et al., 2022). Online learning requires a higher level of internal motivation and self-discipline during the learning process compared to the classroom environment. Therefore, students who are more interested in online learning activities and are more capable of using various learning strategies tend to enjoy online learning more and have more positive learning experiences (Zhu et al., 2020).

An education program typically requires students to be busy with certain tasks and activities, and to make decisions about how much time, where and how to study, especially those studying in higher education (Kahn, 2014). According to Conrad and Donaldson (2011), student involvement in the online environment encourages them to take more ownership of their education. In addition to having effective learning techniques, successful learners also have a strong desire to learn. These students who are behaviorally, cognitively and emotionally involved in learning tasks can be called self-regulated learners (Fredricks et al., 2004). In a setting where students learn online, learners should be engaged by providing timely student-instructor interaction, creating a collaborative learning environment, and designing activities that enrich students’ development (Robinson & Hullinger, 2008). The more responsibility students are given in the learning process and the more they are kept busy, the more their learning will improve, and engagement will increase (Bond et al., 2020). In addition, challenging tasks will encourage learners to use their metacognitive strategies (Babakhani, 2014; Ergulec & Agmaz, 2023).

4.3 Personality traits and engagement

The third hypothesis of the study (H3) states that “five factor personality traits is a significant predictor of student’s engagement in online learning environment.” According to the research findings, it has been determined and confirmed that students’ self-regulated learning behaviors are significant predictors of their commitment to the online learning environment. This finding is consistent with other research results that indicate learners’ personality traits play a crucial role in their academic engagement (Ghyasi et al., 2013; Jensen, 2015; Keller & Karau, 2013; Komarraju & Karau, 2005; Mahama et al., 2022; Morfaki & Skotis, 2023; Ongore, 2014; Quigley et al., 2022; Qureshi et al., 2016; Strauser et al., 2012; Yu, 2021; Zhang et al., 2020).

The five-factor personality model (McCrae & Costa, 1989) was analyzed with respect to its dimensions, revealing a positive correlation between conscientiousness, agreeableness, openness to experience, extraversion, and self-regulated learning. Additionally, a negative correlation was observed between neuroticism and engagement. Accordingly, students possessing personality traits other than neuroticism tend to exhibit high levels of commitment to online classes. When examined by dimensions, especially students with characteristics of conscientiousness and agreeableness show higher levels of participation. Related studies emphasized that conscientiousness and agreeableness from personality traits are important predictors of engagement (Keller & Karau, 2013; Quigley et al., 2022; Qureshi et al., 2016). High- conscientiousness students are expected to engage better by using strategies directly or indirectly related to academic progress, including assignment completion and question-asking (Caprara et al., 2011; Chamorro-Premuzic & Furnham, 2003). These individuals can plan, manage their time, organize their materials, and perform effectively (Jensen, 2015).

Characteristics associated with the agreeableness personality dimension encompass feelings of collaboration and obedience that enable students to follow instructions and pay attention to assignment deadlines (Bruso et al., 2020). Qureshi and colleagues (2016) assert that commitment to the course is linked to trust and attaching importance to the educational process; thus, adaptive traits such as self-sacrifice, honesty, and flexibility positively impact student engagement in the learning process. Morfaki and Skotis (2023) attribute the higher engagement of agreeable students in the online learning environment to the opportunities for more efficient work provided by isolation and the less competitive learning environment it creates for these students.

Individuals open to experience are inclined to ask questions and embrace new ideas (Babakhani, 2014), thereby becoming more engaged in the learning process (Komarraju & Karau, 2005). Their willingness to try different things, perceive new situations as positive and exciting rather than threatening, has facilitated their adaptation to the urgent remote learning process during the COVID-19 pandemic (Morfaki & Skotis, 2023). Consistent with the findings of this research, learners with personality traits characterized by responsibility, agreeableness, and openness to experience generally seem to outperform those with extraversion and neuroticism in the online learning environment (Keller & Karau, 2013; Mahama et al., 2022; Yu, 2021).

Despite demonstrating active participation in the online environment, extroverted learners generally do not reach the same level of commitment as conscientious and agreeable students. According to research, extroverted learners establish better connections with their peers and educators (Babakhani, 2014; Komarraju & Karau, 2005). On the other hand, these same students are more prone to engage in activities other than studying, leading to lower performance levels (Eysenck, 1992; Chamorro-Premuzic & Furnham, 2009). Since online learning is often an individual activity and may provide limited opportunities for social interaction, extroverted individuals may find social engagement in online environments insufficient and may not find it appealing (Keller & Karau, 2013).

Students exhibiting higher levels of neuroticism tend to display particularly lower levels of behavioral and emotional engagement in online environments. Those with high neuroticism levels experience anxiety and feel more vulnerable to emotional stress when living in an environment that changes from what they are accustomed to, such as online learning; therefore, they tend to avoid the stress of learning in unfamiliar situations (Watjatrakul, 2016). Considering the increased tendency for isolation and the risk of dropping out in students with higher neuroticism traits, it becomes essential to take additional measures to reduce their stress and anxiety levels and strengthen their commitment to learning.

Student engagement is affected by both personal and external factors (Kahu & Nelson, 2018; Martin & Borup, 2022). Engagement of learners can be facilitated or hindered by the online learning environment and personal environment as well as the personal characteristics of the learners (Borup et al., 2020). Thus, learners engage in the learning process in different ways according to their personality traits. While some students prefer a detailed structured learning environment regarding the tasks to be completed, some may prefer a flexible learning environment that will reveal their productivity (Payne, 2019). Therefore, it is recommended to use various teaching methods and adapt the online environment according to the student needs in order to ensure maximum participation of learners in activities (Komarraju & Karau, 2005; Kuh et al., 2008). In addition, individuals can be encouraged to develop themselves and share their ideas through activities in which a sense of belonging is felt in the educational setting (Komarraju & Karau, 2005).

4.4 Mediating role of self-regulated learning

The fourth hypothesis of the study (H4) posits that “The mediation effect of self-regulated online learning behaviors is significant between five-factor personality traits and students’ engagement in the online learning environment.” The results confirm that self-regulated online learning behaviors plays a mediating role in the relationship between students’ personality traits and their engagement in the online learning environment.

The partial mediation effect of self-regulated learning is important in the relationship between the five-factor personality traits and the student’s engagement in the online learning process. Self-regulated learning is a very important skill especially in the online environment where the learner is autonomous and for higher education students who are expected to take responsibility for learning. It is already known that self-regulated learning activities have a mediating role in learner success in the academic environment (Barnard-Brak et al., 2010; Pintrich, 2004), and research results show that they mediate the relationship between personal characteristics and academic achievement (Cho & Kim, 2013; Eilam et al., 2009).

In this study, it is found that learner engagement was affected by the fixed personal characteristics of the learners and the self-regulated learning skills that could change according to the context. In terms of social cognitive theory, the interplay of individual, behavioral, and environmental processes is what is referred to as self-regulation (Bandura, 1986). Similarly, Zimmerman (1998) draws attention to the fact that self-regulation should not be seen as a fixed feature of learners but as context-specific processes. Lizzio and colleagues (2002) mention the importance of the educational setting in increasing academic achievement and state that the changes to be made in the setting affect the learning approaches of the learners. Thus, any arrangement to be made in the design of the learning environment will support the use of students’ self-regulated learning skills, and accordingly, would facilitate engagement shaped by both internal and external factors such as self-regulated learning. Accordingly, in order to ensure effective engagement of students in the educational setting, there is a need for online learning environments that encourage self-regulated learning, considering individual differences.

There is empirical evidence showing that lesson structure is significantly related to learners’ self-regulated learning (Cho & Kim, 2013). In particular towards the start of the teaching process, learners are expected to have information about the general view of the course, what is expected of them and what they must do to succeed in the course, and this would positively impact their motivation and behavior (Ergulec et al., 2022). Technologies such as course structure, directions and collaboration tools provide opportunities for students to learn the subject in depth through interactive activities (Järvelä et al., 2015). In Ma and colleagues (2015) study, the instructor’s preparation for the lesson was significantly related to the students’ involvement in the learning process and the guidance of the instructor has a major impact on how well learning activities are completed. Draus and colleagues (2014) concluded that instructor-created video content increased the frequency and length of students’ discussion posts, thus increasing behavioral engagement. Guo and colleagues (2014) concluded that instructor-generated feedback positively affected cognitive engagement. Activities such as assignments that require inventive and critical thinking (Fredricks et al., 2004), interesting, challenging and authentic activities (Fredricks, 2011), clear instructions (Schindler et al., 2017), simultaneous discussion and discussion forums (Kanuka, 2005) can potentially facilitate students’ self-regulation. Time management can be encouraged with gradable assignments in stages (Ergulec, 2019). Gamification tools such as badges and progress bars can motivate learners to complete their tasks (Bruso et al., 2020). Online learning makes it easier for learners to take more responsibility for their learning and to be active, thus making it easier for self-regulated learning strategies to work. It also offers more opportunities for learners to spend more time preparing for the lesson, interact and use different learning strategies (Paulsen & McCormick, 2020).

5 Conclusion

The study’s conclusion highlights several significant relationships. Firstly, it establishes that five-factor personality traits serve as a siginificant predictor of self-regulated online learning behaviors. Additionally, self-regulated online learning behaviors emerges as a significant predictor of student engagement in the online learning environment. Moreover, the findings indicate that five-factor personality traits also significantly predict student engagement in the online learning environment. Lastly, the study reveals that self-regulated online learning behaviors plays a partial mediating role in the influence of five-factor personality traits on students’ engagement in the online learning environment.

Learners with positive personality traits benefit uniquely from self-regulated learning strategies in online environments. Those with responsibility and adaptability skills particularly excel in utilizing these strategies. Thus, emphasizing the importance of self-management and fostering a sense of responsibility through structured online courses is vital. Moreover, personalized and flexible learning environments tailored to individual differences, alongside appropriate pedagogical approaches and technological tools, would enhance engagement.

Highly self-regulated learners exhibit increased behavioral, cognitive, and emotional engagement, employing strategies like goal setting and time management. Educators play a pivotal role in promoting these strategies, which in turn boosts student engagement. Detailed course outlines and effective guidance on self-regulated learning further support student involvement. Creating opportunities for opinion-sharing through forums and group work fosters social and emotional learning aspects, enhancing cognitive and behavioral engagement. While students vary in participation levels based on personality traits, personalized teaching approaches can bolster commitment, particularly among stressed or anxious students.

In distance education, where students bear greater responsibility for learning, fostering self-regulated learning becomes essential. Integrating tools that encourage active participation, alongside thoughtful course design, supports engagement. However, it’s crucial not to rely solely on technology, as participation is influenced by internal and external factors. Ultimately, emphasizing real-life skill development, such as self-management and communication, alongside addressing social and emotional aspects of learning, enriches the overall learning experience for all students (Morfaki & Skotis, 2023).

The study primarily focused on a specific educational level, higher education, limiting the generalizability of the findings to a broader population. Future research endeavors could incorporate diverse variables across various educational levels to enhance the external validity of the results. Future research can broaden its scope by investigating self-regulated learning behaviors and student engagement across diverse educational levels. By comparing findings across different educational contexts, a more comprehensive understanding of how these variables interact and influence each other can be gained.

The study employed a cross-sectional design, capturing data at a single point in time. This design may restrict the establishment of causal relationships between variables. Future studies with longitudinal, experimental, or qualitative designs could provide a more robust understanding of the dynamics. Utilizing longitudinal designs would allow researchers to track changes in self-regulated learning behaviors and engagement over an extended period. By collecting data at multiple time points, researchers can establish temporal relationships between these variables and gain insights into how they evolve over time.

Experimental inquiries may be undertaken to elucidate the impact of self-regulated learning behaviors on student engagement. Conducting experimental studies can help elucidate causal relationships between specific interventions and changes in self-regulated learning behaviors and engagement. Researchers can design interventions aimed at enhancing self-regulated learning skills or modifying instructional strategies and measure their impact on student engagement.

Qualitative investigations can be formulated to comprehend the factors influencing the engagement of learners with distinct personal characteristics in online environments. Qualitative research methods, such as interviews and focus groups, can provide deeper insights into the underlying factors influencing student engagement in online learning environments. By exploring students’ perceptions, experiences, and motivations, researchers can uncover nuanced understandings of how individual characteristics and contextual factors shape engagement.

Furthermore, the study was conducted within specific online learning courses with specific instructors, and the findings may not be applicable to other online educational environments with distinct features and structures. Replicating the study across various platforms would enhance the applicability of the results. Replicating the study across various online learning platforms and educational settings can enhance the generalizability of findings. Different platforms may have unique features and structures that influence how self-regulated learning behaviors are enacted and their impact on engagement. By comparing findings across platforms, researchers can identify common patterns and factors that contribute to effective online learning.

Additionally, external factors and contextual variables, such as technological infrastructure and varying levels of online learning experience, were not extensively considered. Future research should consider contextual factors such as technological infrastructure, access to resources, and prior online learning experience. Understanding how these factors interact with self-regulated learning behaviors and engagement can inform the development of tailored interventions and instructional strategies to support diverse learner populations. Incorporating these factors in future studies could provide a more comprehensive understanding of the dynamics influencing self-regulated learning and engagement. These inquiries would aim to ascertain the self-regulated learning behaviors favored by learners who engage differentially and at varying proficiency levels.