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Article

Exploring Academic Self-Concept, Learning Goal Orientation, and Years of Study as Predictors of University Students’ Affective Learning Outcomes

by
Hermann Astleitner
and
Joerg Zumbach
*
Department of Educational Science, University of Salzburg, 5020 Salzburg, Austria
*
Author to whom correspondence should be addressed.
Educ. Sci. 2026, 16(7), 1072; https://doi.org/10.3390/educsci16071072
Submission received: 7 May 2026 / Revised: 12 June 2026 / Accepted: 30 June 2026 / Published: 4 July 2026
(This article belongs to the Section Higher Education)
Versions Notes

Abstract

Research on learning in higher education focuses heavily on cognitive factors and academic performance. However, affective learning outcomes are crucial for overcoming learning difficulties and for personal development. Consequently, this study explores which personal factors are associated with affective learning outcomes among university students. We consider academic self-concept, learning goal orientation, and years of study as personal factors, and politeness, trustworthiness, cooperation, support, and friendship as affective learning outcomes. We collected data from 200 university students who provided self-ratings on the study variables via an online survey. Correlation and multiple regression analyses revealed that academic self-concept was the most strongly related to affective learning outcomes. Unexpectedly, years of study did not show any significant effects. In our discussion, we address the theoretical, methodological, and practical challenges of research and development activities on affective learning in higher education.

1. Exploring Academic Self-Concept, Learning Goal Orientation, and Years of Study as Predictors of University Students’ Affective Learning Outcomes

The main objective of higher education is for students to acquire knowledge and skills that primarily focus on cognitive and subject-related processes and products. However, social dynamics related to a variety of challenging phenomena (e.g., adapting to university life, financing study costs, or building a professional network) can lead to individual stress among university students that cannot be overcome solely through cognitive performance. Thus, students’ affective learning outcomes appear to be crucial for coping with learning difficulties and similar challenges. In educational research, affective learning outcomes encompass changes in learners’ emotions, attitudes, values, and motivational orientations resulting from their instructional experiences. Building on the revised taxonomy of educational objectives (Krathwohl et al., 1964), the affective domain comprises processes such as receiving, responding, valuing, organizing, and internalizing values. In more recent work, affective outcomes are often operationalized through constructs such as motivation, engagement, emotional experience, and attitudes toward learning (e.g., Pekrun, 2006).
In a multitude of educational and related contexts, there is strong evidence of a connection between affective learning outcomes and academic performance (e.g., Goagoses & Koglin, 2022). Maguire et al. (2017) showed that students’ emotional skills positively predict cognitive and affective engagement during their studies. Although affective goals are included in many higher education subject areas and curricula, relatively little is known about whether and how successfully such goals are achieved during undergraduate study. For instance, Conley et al. (2020) found that affective factors, including psychological functioning, cognitive–affective strategies, and social adjustment, worsened over the first two years of university study. A key factor for the affective learning outcomes of university students is communication and interaction with other students and faculty members (e.g., Walz & Braun, 2022). This aspect plays an important role in university students’ academic success and persistence, as well as their social integration and well-being (e.g., Kim & Kim, 2017). In this study, affective learning outcomes are conceptualized more specifically as politeness, trustworthiness, cooperation, support, and friendship. This operationalization allows us to capture the affective processes most directly linked to students’ engagement and persistence in learning tasks.

2. Exploring Goal Areas of Effective Learning

From a general theoretical perspective, the communication and interaction of university students can be seen, according to Knapp et al.’s (2014) relationship model, as a “coming together” process that encompasses five stages: initiating, experimenting, intensifying, integrating, and bonding. We assume that this five-step process is anchored by five corresponding factors in university students’ everyday behaviors that represent essential affective learning outcomes: politeness, trustworthiness, cooperation, support, and friendship. Politeness seems to play a central role in initiating relationships. This relationship also applies to trustworthiness and experimenting, cooperation and intensifying, support and integrating, and friendship and bonding. In the current study, we explore politeness, trustworthiness, cooperation, support, and friendship as essential goal areas and dependent variables of affective learning in higher education settings.
Politeness is about behaving in a socially correct manner and understanding and caring for other people’s feelings. Politeness judgments are important in personal relationships and essential in relationship initiation. More specifically, for university students, politeness strategies are associated with relationship avoidance or feelings of discomfort (Zhu & Bresnahan, 2018).
Trustworthiness is “the propensity to fulfill another’s positive implicit or explicit expectations regarding a particular action” (Levine et al., 2018, p. 469). Trustworthy behavior is an important element of relationship experimentation, especially in judgments related to social decision-making, which is essential for getting to know or evaluating other people (e.g., Van’t Wout & Sanfey, 2008). Boateng et al. (2017) found a link between trustworthiness and willingness to share information with other students.
Cooperation is a sign of an intensified relationship involving working with others to reach a common goal (e.g., Spagnolo, 1999). Peer relationships and cooperative tendencies are closely related, and cooperative behavior is a common daily task in learning activities (e.g., Wang & Hu, 2021).
Support is about encouraging and helping others and occurs when individuals begin to merge their activities with those of other people. Supportive behavior is more intense than cooperation, as it indicates a stronger relationship and more concern and commitment for the other person. Peer support is an important part of the self-reported academic behaviors of university students (e.g., Purswell et al., 2008).
Friendship refers to a close relationship with another person and involves behaviors that foster strong, positive affective bonds to facilitate the accomplishment of social–emotional goals. Research indicates a relationship among university belonging, psychological adjustment, and the quality of friendships with peers (Pittman & Richmond, 2008).
Based on these results, the question for universities is whether they need to establish appropriate programs or interventions to promote students’ affective learning outcomes (e.g., Grootenboer, 2010). Arguably, the promotion of cognitive personality characteristics alone might indirectly promote affective learning outcomes. Cobo-Rendón et al. (2020) found significant correlations between affective learning outcomes, such as affective balance, psychological well-being, and self-efficacy, and the perceived academic performance of first-year undergraduate students.
The current study is anchored in this context: we assume that the cognitive and affective components of learning are related. We base our assumptions on a cognitive-affective system theory of personality, which conceptualizes behavior as emerging from dynamic interactions among encodings, expectancies, beliefs, affects, goals, and values, as well as competencies and self-regulatory plans (Mischel & Shoda, 1995). A general theoretical foundation is also provided by a model of interpersonal skill performance (Klein et al., 2006), which posits that individual differences, situational characteristics, cognitive filtering processes, and the execution of interpersonal skills jointly influence individual, group, and institutional outcomes. Furthermore, the interactions of cognitive and affective processes are reflected in the cognitive-affective theory of mind models, which assume that mental and emotional states interact to recognize, infer, regulate, and respond to the thoughts, emotions, and behaviors of the self and others (Westby, 2014). These assumptions are also consistent with Schneider et al.’s (2022) cognitive-affective-social theory of learning, which highlights the role of social cues, processes, and schemas in cognitive selection, organization, integration, and retrieval processes.
Building on this theoretical background, we address which personal factors contribute to the acquisition of affective learning outcomes. In the present study, affective learning outcomes are operationalized through interpersonal and socially relevant behaviors, including politeness, trustworthiness, cooperation, support, and friendship, following Knapp et al.’s (2014) relationship model. From a theoretical perspective, these behaviors can be understood as manifestations of underlying affective dispositions shaped by cognitive-motivational structures.
In particular, goal orientation and academic self-concept are central cognitive-motivational factors that influence affective learning outcomes. Goal orientation theory suggests that individuals’ goals (e.g., mastery vs. performance orientation) shape how they interpret and respond to social and learning situations. A mastery-oriented goal structure, for instance, is associated with prosocial and cooperative behaviors, as learners focus on improvement, understanding, and mutual support rather than competition. In contrast, performance-oriented goals may reduce cooperative tendencies and trust, particularly in competitive environments. These goal-related interpretations influence interpersonal behaviors such as cooperation, support, and politeness through cognitive appraisal processes.
Similarly, academic self-concept, defined as individuals’ perceptions of their academic competence, plays a crucial role in regulating both affect and social behavior. Students with a positive academic self-concept are more likely to experience confidence and reduced anxiety, which facilitates open, trusting, and supportive interactions with peers. In contrast, a low academic self-concept may lead to defensive behaviors, reduced trust, and lower engagement in cooperative activities. From a cognitive-affective system perspective, academic self-concept functions as an expectancy-belief structure that shapes both emotional responses and behavioral tendencies in social contexts.
Importantly, the interpersonal outcomes examined in this study—politeness, trustworthiness, cooperation, support, and friendship—can be interpreted as emergent properties of these interacting cognitive, affective, and motivational processes. Politeness and trustworthiness, for example, are socially regulated behaviors grounded in both affective dispositions (e.g., empathy, respect) and cognitive expectations about others. Cooperation and support are linked to shared goal structures and prosocial motivational orientations, while friendship reflects more stable relational outcomes that develop through repeated affective and cognitive interactions.
Taken together, this theoretical integration suggests that cognitively relevant personal factors such as goal orientation and academic self-concept are systematically linked to affective learning outcomes. They influence how learners perceive social situations, regulate emotions, and enact interpersonal behaviors, thereby contributing to the development of positive social and affective competencies in educational contexts.

3. Affective Learning in Relation to Academic Self-Concept, Learning Goal Orientation, and Years of Study

This research focuses on the personal factors of academic self-concept, learning goal orientation, and number of years of study as independent variables. We selected these personal factors because they have reciprocal developmental effects that could make them particularly relevant for promoting academic performance, as well as affective learning processes and outcomes (e.g., Steinberg et al., 2024; Wu et al., 2021). We assume that the selected independent variables are connected to the dependent variables related to affective learning. However, at the current stage of research in the higher education context, these connections have varying levels of theoretical foundation and empirical evidence.
We first considered academic self-concept to be “the mental representation of one’s own academic abilities in general and in different academic domains” (Arens et al., 2021, p. 35). We hypothesized that the academic self-concept of university students is related to their affective learning outcomes, expecting that a higher academic self-concept would correspond with higher values in politeness, trustworthiness, cooperation, support, and friendship. This assumption is theoretically supported by Kahu’s (2013) model of student engagement. This model assumes that student personal characteristics lead to affective engagement and behavior (e.g., participation), which is associated with proximal and distal academic as well as social consequences. This construction also corresponds with the model of academic self-concept from Gogol et al. (2017), which shows that academic self-concept is related to affective variables such as interest and anxiety.
Based on these theoretical backgrounds and related empirical findings, we assumed that students with a high academic self-concept would experience less stress and pressure in a university context. This characteristic would result in less aggressive and more polite behavior (e.g., Thomas, 2019). We also assumed that a higher academic self-concept would be associated with higher self-efficacy and abilities and would enable students to be more open and effective in addressing academic and related affective problems. Indeed, individuals with a high self-concept (based on high abilities) are more likely to trust others and find it easier to cooperate and support them (DeFreitas & Rinn, 2013).
Second, we focused on learning goal orientation, which is about consistently striving toward the mastery of a skill or task to increase one’s competence (Taing et al., 2013). We hypothesized that university students with a high learning goal orientation might also show higher affective learning outcomes. These students might also be more sensitive and supportive of their peers because they could help them achieve their ambiguous goals. Getting help from others requires an individual to be polite, trustworthy, cooperative, and supportive. Having a high learning goal orientation also means that an individual consistently strives for goals and uses available human and other resources to support their learning. These assumptions are supported by the model from Chiaburu et al. (2007), in which connections between goal orientations and role behaviors in social contexts (i.e., providing help) are assumed (see also Lei, 2024).
Considering the theoretical background and related empirical findings, goal orientations may be associated with perceived benefits of help-seeking (Roussel et al., 2011). Students with a higher learning goal orientation might also have higher academic self-efficacy and, thus, perceive themselves as an academic resource for others (Zander et al., 2018). For example, D. Liu et al. (2015) found relationships between learning goal orientation and self-focused impression management tactics (i.e., being easygoing and polite), Chiaburu et al. (2007) for providing help for others, and Chughtai and Buckley (2011) for aspects of trustworthiness. Further, Shin and Ryan (2014) found that friends had similar achievement goals, suggesting positive correlations between learning goal orientation and the learning mechanisms relevant to friendship. However, Levy-Tossman et al. (2007) found significant negative correlations between performance-approach goals and intimacy, suggesting that people who consistently strive to achieve their goals might create competitive situations that lead to failure and decreased self-esteem, making friendly relationships less likely. Taken together, the findings on goal orientation are contradictory.
Third, we hypothesized that years of study are related to affective learning outcomes. This assumption is based on the model by Klein et al. (2006), which posits that life experience is essential for filtering, communication, and relationship building in interpersonal skill performance. In addition, the model by Beauchamp and Anderson (2010) has suggested that external influences within higher education settings require students to engage in social information processing that is associated with social problem solving, social interaction, and social adaptation. Malinauskas et al. (2014) found that the social skills (as possible indicators of affective learning outcomes) of senior-year students were significantly higher than those of first-year students.
In general, academic learning and teaching take place in largely social settings, where affective learning outcomes are promoted directly or indirectly. Studies show that age is associated with perceptions of politeness, impressions of trustworthiness, prosocial behavior (e.g., cooperation and support), and friendship (e.g., Makarla et al., 2024). In addition, a moderate maturation of affective learning outcomes in university contexts is also suggested between the ages of 20 and 25 (e.g., Dave et al., 2021). However, it could also be the case that affective learning outcomes do not change throughout university because they are not goal-related. Sheldon and Krieger (2004) found a decline in university students’ community service values (associated with affective learning) during their first year. Hu et al. (2022) reported that the development of university students’ social support showed an upward trend, followed by a downward trend. Rogaten et al. (2019) summarized that there is an inconsistent picture regarding the affective gains from years of university study.
In summary, previous studies do not provide a clear theoretical and empirical picture of the three independent variables, although they suggest possible correlations with affective variables. Therefore, our exploratory study aims to gain greater clarity in this area and increase the consistency of the research. We have chosen a broad, exploratory framework with three independent variables and five dependent variables related to affective development. This multivariate approach also allows for the determination of adjusted statistical effects, which should further clarify the specific contribution of the independent variables.

4. Focus of the Study

In our cross-sectional study, we focused on two goals: (1) to explore the associations between the predictors of academic self-concept, learning goal orientation, years of study, and the affective learning outcomes of university students (i.e., politeness, trustworthiness, cooperation, support, and friendship) as dependent variables; (2) to evaluate the predictive power of each of these factors when controlling for the other factors. Hence, we tested three related regression models by stepwise combining our predictors.

5. Method

5.1. Participants and Procedure

Data from 200 university students were obtained (73% female, 26.5% male, and 0.5% diverse). The average age was 24.52 years (SD = 4.52), with students in our sample having studied for 3.77 years (SD = 1.95) on average. Our sample was a convenience sample with a primary focus on students enrolled in educational study programs, including Bachelor’s and Master’s programs (62.3% in teacher education, 14.6% in education, and 23.1% in other subjects, e.g., law or natural sciences). Participation in this online survey was voluntary, and no reward was given. Students had to complete a consent form before starting the survey and could withdraw from the study at any time.

5.2. Measures

Participants had to rate all the items (except for years in university) based on a five-point Likert-type scale ranging from 1 = “completely disagree” to 5 = “completely agree” (all in German). Cronbach’s alpha (CA) was used as the internal consistency indicator for all the scales (see Table 1). The scales on cooperation, support, and friendship were developed in a previous study on collegiality in higher education (Astleitner & Zumbach, 2023).
Academic self-concept. Academic self-concept refers to the evaluation of success in learning settings and was measured using five items based on Dickhäuser et al. (2002; e.g., “I am gifted at studying”; CA = 0.86). We conducted an exploratory factor analysis (with principal axis factoring and Promax rotation) and considered reference values for scale development (i.e., Kaiser-Meyer-Olkin (KMO): >0.60; Bartlett’s test of sphericity: p < 0.05; minimum factor loading: 0.30–0.40; commonalities: 0.40–0.70; Goretzko et al., 2021). Factor analysis on the five academic self-concept items resulted in one factor with an eigenvalue > 1, which explained 54.99% of the variance (KMO: 0.85; Bartlett’s test: p < 0.001; minimum factor loading: 0.67; commonalities: 0.45–0.62).
Learning goal orientation. Learning goal orientation refers to how hard students focus on broadening their skills and was measured using eight items adapted from Dickhäuser et al. (2007); e.g., “My studies are about getting new ideas”; CA = 0.87; one factor with an eigenvalue > 1; 47.97% explained variance; KMO: 0.87; Bartlett’s test: p < 0.001; minimum factor loading: 0.50; commonalities: 0.25–0.64.
Years of study. Years of study refers to how long the students were enrolled at their university.
Politeness. Politeness refers to agreeable and respectful behavior and was measured with four items adapted from Schimmel et al. (2013); e.g., “Good interactions with fellow students are important to me”; CA = 0.79; one factor with an eigenvalue > 1; 56.68% explained variance; KMO: 0.65; Bartlett’s test: p < 0.001; minimum factor loading: 0.53; commonalities: 0.28–0.83.
Trustworthiness. Trustworthiness refers to being conscientious in interacting with others and upholding agreements (four items adapted from Johnston et al., 2012; e.g., “I am reliable when dealing with fellow students”; CA = 0.76; one factor with an eigenvalue > 1; 49.21% explained variance; KMO: 0.67; Bartlett’s test: p < 0.001; minimum factor loading: 0.42; commonalities: 0.17–0.87).
Cooperation. Cooperation refers to working together to achieve a common goal and was measured with four items from Astleitner and Zumbach (2023); e.g., “I work with other students on assignments in courses (e.g., presentations, term papers)”; CA = 0.69; one factor with an eigenvalue > 1; 38.18% explained variance; KMO: 0.70; Bartlett’s test: p < 0.001; minimum factor loading: 0.40; commonalities: 0.16–0.52.
Support. Support refers to encouraging and assisting other students in learning and personal development (four items from Astleitner & Zumbach, 2023; e.g., “I show other students praise and appreciation”; CA = 0.81; one factor with an eigenvalue > 1; 52.77% explained variance; KMO: 0.75; Bartlett’s test: p < 0.001; minimum factor loading: 0.58; commonalities: 0.34–0.71).
Friendship. Friendship refers to behavior indicating a close positive relationship with other students and was measured with four items from Astleitner and Zumbach (2023); e.g., “I have a warm and supportive relationship with other students”; CA = 0.81; one factor with an eigenvalue > 1; 52.65% explained variance; KMO: 0.76; Bartlett’s test: p < 0.001; minimum factor loading: 0.67; commonalities: 0.44–0.62.
Taken together, our internal consistency tests showed acceptable results (CA ≥ 0.69). Factor analyses revealed that all the tested scales were unidimensional (based on one extracted factor). Good Kaiser-Meyer-Olkin and Bartlett values indicated that our data were suitable for factor analysis and strong enough to warrant the use of a dimension-reduction technique. The minimum factor loadings indicated at least a moderate to high correlation between the items and factors. There were some low communalities, indicating that some items were (not strongly) related to others or that an additional factor should be explored.

5.3. Statistical Analyses

In preparation for our statistical analyses, we tested six assumptions for conducting multiple regression analyses (Hemmerich, 2024). This approach involved creating a sum score from all five dependent variables (as an acceptable alternative to other possibilities such as factor scores; Y. Liu & Pek, 2024). This dependent variable sum score was used as an approximation method to check for outlier values, the independence of residuals, multicollinearity, homoscedasticity, the normal distribution of the residuals, and the linear relationship between variables.
To test the linear relationship between the variables, we conducted a multiple regression (dependent variable: sum score; independent variables: academic self-concept, learning goal orientation, years of study) and produced a scatterplot containing studentized residuals (Y-axis) and unstandardized predicted values (X-axis). The scatterplot showed that the data points were, to a large extent, positioned around the zero line (Y = 0), indicating highly probable linear relationships. To identify outliers, we computed studentized deleted residuals, finding a minimum of −2.86 and a maximum of 1.79 (within an acceptable range from −3 to +3). This finding indicated that there were no outlier values in the data.
To test for residual independence, we computed a Durbin–Watson statistic of 0.66, indicating problems with the positive autocorrelation of residuals (reference value = 2). We tried to estimate the size of this problem by computing robust standard errors. The results showed that the regression coefficient p-values were comparable to those of our final results (see Table 2): self-concept (t = 2.74, p < 0.01), learning goal orientation (t = 1.73, p < 0.10), and years of study (t = −0.05, p > 0.05). We assessed multicollinearity by considering tolerance and VIF (variance inflation factor) values within collinearity statistics. We found no tolerance values < 0.1 (minimum: 0.83) and no VIF values > 10 (maximum: 1.21), which indicated no significant multicollinearity between predictors. We then measured the homoscedasticity of residuals by checking the scatterplot of the studentized residuals and unstandardized predicted values. This approach showed that the data points were evenly distributed across the horizontal axis, indicating equal variances (and homoscedasticity). To test for the normal distribution of the residuals, the Shapiro–Wilk test on the studentized residuals was applied (statistics: 0.97, df = 200, p < 0.001), indicating a significant deviation from normal distribution. However, there are different opinions as to whether such deviation has a particularly negative impact on the results, especially for large sample sizes (n > 10 per estimated parameter).
In our final model, we had 15 parameters and a sample size of n = 200, resulting in n = 13 per estimated parameter (Schmidt & Finan, 2018). Therefore, the non-normal distribution of the residuals was not considered to be problematic. A power analysis with G*Power 3.1 (average effect size for all variables f2 = 0.0490, average r2 = 0.0467 (from Table 1), Alpha = 0.05, sample size = 200, number of predictors = 3) found an acceptable power of 0.74 for our study conditions (Faul et al., 2009). In summary, the data met the requirements for multiple regressions.

6. Results

6.1. Descriptive Statistics

The descriptive statistics for all our variables are depicted in Table 1. The means and standard deviations indicate that the university students in our sample considered themselves to be highly polite, trustworthy, and cooperative. Support for and friendship with other students were at a medium level. Academic self-concept was also at a medium level, whereas learning goal orientation was relatively high. The average years of study indicated that the students were likely to be at the end of a Bachelor’s degree or the beginning of a Master’s degree program. As expected, four out of the five affective learning outcomes were positively correlated; the exception was trustworthiness, which did not correlate significantly with support for or friendship with other students. As expected, our independent variable, academic self-concept, correlated significantly with learning goal orientation and four of the five affective learning outcomes; it showed no significant relationship with politeness. Learning goal orientation was also significantly related to four of the five affective learning outcomes; it was not significantly correlated with trustworthiness. Years of study revealed a rather unexpected pattern: this dependent variable did not correlate significantly with any of the others, and most of the correlations were very low.
Table 2. Results of the Multiple Regression Analyses for Predicting Affective Learning Outcomes.
Table 2. Results of the Multiple Regression Analyses for Predicting Affective Learning Outcomes.
Dependent Variables
and Steps		b*R2
(Step 1, Step 2, Final)		R2 Change
Step 1Step 2Final
Politeness
1. Academic self-concept0.130.050.050.02
2. Learning goal orientation 0.21 **0.21 **0.06 **0.04
3. Years of study 0.010.06 *0.00
Trustworthiness
1. Academic self-concept0.15 *0.140.130.02 *
2. Learning goal orientation 0.020.030.020.00
3. Years of study 0.090.030.01
Cooperation
1. Academic self-concept0.18 *0.130.130.03 *
2. Learning goal orientation 0.110.110.04 *0.01
3. Years of study 0.020.04 *0.00
Support
1. Academic self-concept0.26 ***0.23 **0.23 **0.07 ***
2. Learning goal orientation 0.090.090.08 ***0.01
3. Years of study −0.040.08 **0.00
Friendship
1. Academic self-concept0.22 **0.19 *0.20 *0.05 **
2. Learning goal orientation 0.070.060.05 **0.00
3. Years of study −0.030.05 *0.00
Note. N = 200. * p < 0.05. ** p < 0.01. *** p < 0.001.

6.2. Analytical Approach and Exploring the Predictors

We followed an exploration strategy informed by current research and used exploratory multiple regression analysis (Braun & Oswald, 2011). All our independent variables were divided into three blocks to explore their relationships with our five dependent variables: the affective learning outcomes of politeness, trustworthiness, cooperation, support, and friendship. As shown in Table 2, we used a three-step multiple regression. In the first step, we included one independent variable (academic self-concept). In the second step, we included two independent variables (academic self-concept and learning goal orientation). In the final step, we considered all three independent variables (academic self-concept, learning goal orientation, and years of study). We selected this approach to data analysis as we were interested in exploring the variation in the dependent variables explained by the addition of new independent variables. Our analytical approach also allows us to statistically control for confounding effects.
Based on prior research, we concluded that academic self-concept was the strongest influencing factor (predictor). We then looked to determine whether learning goal orientation and years of study could explain further variance in the affective learning outcomes. Based on the results, we expected learning orientation to be the second strongest predictor and years of study to be the weakest. As shown in Table 2, politeness was significantly clarified in the final step (F = 3.84, p < 0.05). However, only learning goal orientation reached significance (b* = 0.21, t = 2.79, p < 0.01), such that politeness was higher for those with a higher learning goal orientation (final explanatory variance (EV) = 5.57%).
In the final step, our three independent variables did not significantly predict trustworthiness (F = 2.09, p > 0.10), but there was a significant effect of academic self-concept in the first step (b* = 0.15, t = 2.15, p < 0.05; F = 4.60, p < 0.05; EV = 2.28%); university students with higher academic self-concept also had higher trustworthiness. This first-step effect was also evident for cooperation, which was higher for those with a higher academic self-concept (b* = 0.18, t = 2.56, p < 0.05; F = 6.54, p < 0.05; EV = 3.20%).
Our analyses were the most successful in clarifying support. However, the only significant effect came from academic self-concept in all three steps (final step: b* = 0.23, t = 3.09, p < 0.01; F = 5.53, p < 0.01; EV = 7.78%); the results showed that support for other students was higher when students had a higher academic self-concept. Finally, higher friendship values corresponded with higher academic self-concept at all three steps (final step: b* = 0.20, t = 2.57, p < 0.05; F = 3.61, p < 0.05; EV = 5.24%). Overall, the explained variances we obtained for the affective learning outcome variables broadly corresponded to medium effect size equivalents when associated with the personal factors in our study (Lipsey, 1990, p. 58).

7. Discussion

The primary goal of this study was to explore the personal factors associated with the affective learning outcomes of university students, which have received much less attention in higher education research compared to cognitive outcomes. After controlling for other factors, we found that academic self-concept was significantly related to trustworthiness, cooperation, support, and friendship. We also found that learning goal orientation predicted politeness. Contrary to our expectations, we found no effect of years of study on any of our affective learning outcome indicators. These findings have implications for understanding affective development in higher education settings, underscoring the need to implement visible and effective educational research and development activities.
The average self-assessments of the university students also revealed medium to high scores for academic self-concept and learning goal orientation. Hence, in this case, our results may have little implications for students with low or negative academic self-concept or learning goal orientation. On average, students in this study had been at university for three to four years. Therefore, they were likely to be more experienced than students in their first semester or first year of study, who are often the focus of higher education research (e.g., Reason et al., 2006).
Correlational findings revealed significant relationships between all the affective learning outcome indicators, except between trustworthiness and support for others, and trustworthiness and friendship with others. These results are consistent with other studies (e.g., Morán et al., 2015).
We found correlations between politeness and other learning outcomes, consistent with findings by Percival and Pulford (2020). In summary, our correlation results imply and are consistent with other findings that affective learning outcomes have linked dimensions, and these can also be measured accordingly. Furthermore, it appears necessary that, when researching affective learning, particular attention be paid to theoretical approaches that take this fact into account, such as Knapp et al.’s (2014) relationship model and Kahu’s (2013) model of student engagement.
Findings regarding the relationship between trustworthiness and cooperation are in line with those of Ferrin et al. (2008), suggesting the spiraling of perceived trustworthiness and cooperation in interpersonal relationships. Our unexpected findings of insignificant correlations between trustworthiness and support and trustworthiness and friendship require further clarification, especially in theoretical terms. It is possible that the corresponding finding in our study implies that trustworthiness is not a central factor in advanced student relationships, and that trustworthiness is less important with people we find likable and therefore want to support and be friends with. From a theoretical perspective, students probably have different standards for relationships with each other. For example, Hall (2012) found that standards for friendships are about symmetrical reciprocity, agency, enjoyment, instrumental aid, similarity, and communion, but not trustworthiness. Also, Felmlee and Muraco (2009) found that trustworthiness was not a friendship norm among older adults. In support of our significant findings, Cillessen et al. (2005) found correlations among companionship, helping, and closeness, which are similar concepts to cooperation, support, and friendship, as examined in our study.
Our reported correlations between academic self-concept and different affective learning outcomes correspond to findings from Luo et al. (2021) and Guo et al. (2022), among others, suggesting that academic self-concept and similar concepts are related to interpersonal difficulties. The academic self-concept of university students might affect interpersonal trust, sensitivity, emotion management, and the quality of interpersonal relationships, which should reduce interpersonal difficulties and increase affective learning outcomes. Academic self-concept might also be related to perceptions of the learning environment and student engagement. In turn, higher student engagement might lead to more learning experiences, which could increase academic achievement and affective outcomes such as communication skills. Regarding the relationship between academic self-concept and politeness, we found no significant correlation. We argue that this issue needs further clarification. For example, self-concept facets might be related to dark personality characteristics and related behavior such as egocentrism, prestige, or dominance (e.g., Körner et al., 2023). Taken together, our results imply that academic self-concept, although more of a cognitive variable, also shows affective and social effects within an academic context. The theoretical approach of Kahu (2013) may be appropriate here, but it also seems necessary to anchor additional reported mechanisms found by others (Guo et al., 2022; Luo et al., 2021).
The obtained correlations between learning goal orientation and our affective learning outcomes are comparable to the results of studies with younger students and the effects of their goal orientations on peer group relationships, inclusion, and conflict (Sakiz, 2011). Regarding the deeper mechanisms of this association, Levy et al. (2004) discovered that goal orientations are related to evaluations of their personal relevance for learning or social status. Implications suggest that learning goal orientations could be used to further differentiate models on student characteristics related to affective learning outcomes in higher education settings (Kahu, 2013).
Our findings contradicted our expectations by showing that years of study were not correlated with affective learning outcomes. Such findings provocatively challenge the commonly held notion that increased maturation of learning will uniformly and positively impact affective learning outcomes during emerging adulthood (Dave et al., 2021). Our finding that university education might not be particularly effective for learning outcomes is consistent with studies reporting minor or even negative effects of university study programs (e.g., Arum & Roska, 2011; Wang & Hu, 2021). The outcomes are also in line with studies indicating that university students do not consistently demonstrate maturation to more adaptive affective regulation (Park et al., 2020).
This lack of relationship between years of study and affective learning outcomes warrants further investigation. On the one hand, affective learning outcomes might not change because they are not the subject of university course goals, implementations, and examinations (e.g., Deil-Amen, 2006). On the other hand, students might have relatively high or stable affective learning outcomes right from the start of their studies, which makes it challenging to achieve further improvement (e.g., Wibrowski et al., 2017).
From a theoretical perspective, the findings of this study can be interpreted as further evidence of the close interdependence of cognitive, affective, and social processes in learning. In line with cognitive-affective system theory (Mischel & Shoda, 1995), the results suggest that goal orientation and academic self-concept function as central cognitive-motivational units that shape learners’ affective and interpersonal behaviors. More specifically, the observed relationships between these personal factors and outcomes such as politeness, trustworthiness, cooperation, support, and friendship indicate that learners’ goal structures and self-perceptions influence how social situations are appraised and enacted. From a goal orientation perspective, mastery-oriented students may be more likely to engage in cooperative and supportive behaviors because they interpret learning contexts as opportunities for shared growth rather than competition. Similarly, a positive academic self-concept appears to foster trust and prosocial engagement by reducing defensive or self-protective tendencies in social interactions. These findings are also consistent with control-value theory (Pekrun, 2006), which posits that affective experiences are shaped by individuals’ appraisals of control and value and, in turn, influence engagement and behavior.
Notably, the results extend existing theoretical models by demonstrating that affective learning outcomes can be meaningfully operationalized through observable interpersonal behaviors. While prior research has often treated affective constructs (e.g., motivation, emotions) as internal states, the present study highlights their behavioral expression in social contexts. In doing so, it connects cognitive-affective theories with models of interpersonal competence (Klein et al., 2006) and social learning processes (Schneider et al., 2022). This integration suggests that affective learning outcomes are not only internally experienced but are also enacted through interactions with others, thereby contributing to social relationships and learning environments. Future research should further investigate these mechanisms, particularly the extent to which affective processes mediate the relationship between cognitive factors and long-term social and academic outcomes.
Our study also has limitations. First, although the overall quality of our measurements was good, the reliability coefficients and communalities were relatively low, particularly for the cooperation construct. These issues are related to a reduced explained variance and a greater instability of the factor, suggesting a suboptimal fit between theoretical assumptions and measurement dimensions. Future research could employ alternative scales to measure cooperation (e.g., León-del-Barco et al., 2018). Second, we had a small convenience sample of students, most of whom had an educational background. These students are likely to possess higher levels of affective and social skills compared to students in other fields of study (e.g., law or economics). Due to the size and composition of our sample, our results cannot be generalized to other such academic disciplines.
Our correlational results must be put into perspective against the regression analysis results. Our study was exploratory in nature. The study design and the statistical analysis based on correlations and regression analyses do not allow us to examine causal relationships. For that, we would have needed a longitudinal design. The regression results showed that academic self-concept was a relevant predictor when other factors were controlled. Previous higher education studies of academic self-concept have heavily focused on cognitive elements and effects (e.g., Mynott, 2018). Taking this further, our results suggest that academic self-concept has an important affective component and related effects. Filiz et al. (2020) noted the traditional distinction between academic self-concepts (e.g., mathematics self-concept) and non-academic self-concepts, which concern social, physical, and emotional characteristics. Preckel et al. (2013) found significant correlations and related overlaps between academic self-concept and social self-concept. Against this background, we assume that, based on our results, students with high academic self-concept also use and promote their affective learning outcomes more intensively. It may be that students recognize that affective learning outcomes are also relevant to academic performance, as they make it easier to collaborate with others, benefiting students. Another explanation could be that students with high academic self-concept feel more committed to their university as an institution, thereby favoring its members (e.g., Cao et al., 2015). In a university context, greater commitment to others could foster a more socially engaged and sensitive approach, which, in turn, could positively affect affective learning outcomes. Future research activities should focus on this in more detail.

8. Implications for Higher Education Practice

Overall, the affective domain within universities remains an “undiscovered country” and needs more research-based development activities (Pierre & Oughton, 2007). It is difficult for universities to make room for the development of affective learning outcomes, especially given the dynamics and pressures for adaptation in many non-affective disciplines. However, in the absence of integration into the regular curriculum, developing affective learning outcomes can be outsourced to extracurricular activities, elective subjects, or internships (Winstone et al., 2022), topics that require further research. In addition, it is crucial to learn more about the affective development of university students so that this can be taken into account in admission tests and teaching evaluations. For example, Ogden (2003) offered an instrument that measures cooperation, assertion, and self-control, which could be used flexibly in teaching and learning contexts.
Our study indicates that attending university does not automatically promote affective learning outcomes. Rather, it seems that affective learning outcomes must be explicitly listed and anchored in university curricula, courses, and learning materials. For example, Savitz-Romer et al. (2015) presented a list of affective learning outcomes on intrapersonal and social skills related to the development of strong personal values, personal responsibility, belonging, empathy, and social awareness. Others, such as Okur-Berberoglu (2024), have proposed an expanded humanistic curriculum focused on affective outcomes such as concern for others, social dependence, and group dynamics. Furthermore, Astleitner (2022) listed different types of assignments, including mental-contrasting or collaborative problem-solving tasks, that can be used in courses that support affective learning outcomes, and Niemiec (2018) designed a comprehensive intervention program on affective learning outcomes based on character strengths.
Another promising instructional design approach to affective learning in higher education practice might utilize Kolb’s learning cycles: concrete experience, reflective observation, abstract conceptualization, and active experimentation (D. A. Kolb, 2015; A. Y. Kolb & Kolb, 2005). If affective topics are used during learning activities, rather than cognitive or subject-related ones, this gives rise to “affective learning cycles” (e.g., Ben-Eliyahu, 2019; Härtel, 2008). This pedagogical shift would require university teachers to take on expanded roles as coaches, facilitators, subject experts, standard setters, and/or evaluators (A. Y. Kolb & Kolb, 2017). An affective instructional design approach can be grounded in affective events (e.g., stressful or conflictual situations with others) that stimulate affective and behavioral responses through appraisal and coping processes (Ashkanasy et al., 2004).
Since our study found that academic self-concept was related to affective learning outcomes, it seems relevant to jointly promote or combine cognitive and affective learning outcomes. It is conceivable, for example, to use tasks in courses that are both cognitively aligned with teaching goal taxonomies and, in affective terms, with models of affective learning outcomes such as intercultural sensitivity or social and moral values (e.g., Bennett & Bennett). Students would thereby process assignments that have both cognitive and affective challenges. As an interdisciplinary principle, this would support the achievement of cognitive and affective goals in an integrated manner and without extracurricular activities.

Author Contributions

Conceptualization, J.Z.; methodology, J.Z. and H.A.; software, J.Z. and H.A.; validation, J.Z. and H.A.; formal analysis, H.A.; investigation, J.Z.; resources, J.Z.; data curation, J.Z.; writing—original draft preparation, H.A.; writing—review and editing, J.Z. and H.A.; visualization, H.A.; supervision, J.Z. and H.A.; project administration. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Arens, A. K., Jansen, M., Preckel, F., Schmidt, I., & Brunner, M. (2021). The structure of academic self-concept: A methodological review and empirical illustration of central models. Review of Educational Research, 91(1), 34–72. [Google Scholar] [CrossRef]
  2. Arum, R., & Roska, J. (2011). Limited learning on college campuses. Society, 48, 203–207. [Google Scholar] [CrossRef]
  3. Ashkanasy, N. M., Ashton-James, C. E., & Jordan, P. J. (2004). Performance impacts of appraisal and coping with stress in workplace settings: The role of affect and emotional intelligence. In P. L. Perrewé, & D. C. Ganster (Eds.), Emotional and physiological processes and positive intervention strategies (pp. 1–43). Emerald. [Google Scholar]
  4. Astleitner, H. (2022). Classroom assignments for fostering resilience—An instructional design model on affective personality development. Journal of Instructional Research, 11, 26–43. [Google Scholar]
  5. Astleitner, H., & Zumbach, J. (2023). What makes a collegial higher education student? How personality and goal orientation contribute to social behavior. Higher Education Research & Development, 42(8), 1840–1854. [Google Scholar] [CrossRef]
  6. Beauchamp, M. H., & Anderson, V. (2010). SOCIAL: An integrative framework for the development of social skills. Psychological Bulletin, 136(1), 39–64. [Google Scholar] [CrossRef] [PubMed]
  7. Ben-Eliyahu, A. (2019). Academic emotional learning: A critical component of self-regulated learning in the emotional learning cycle. Educational Psychologist, 54(2), 84–105. [Google Scholar] [CrossRef]
  8. Boateng, H., Agyemang, F. G., Okoe, A. F., & Mensah, T. D. (2017). Examining the relationship between trustworthiness and students’ attitudes toward knowledge sharing. Library Review, 66(1/2), 16–27. [Google Scholar] [CrossRef]
  9. Braun, M. T., & Oswald, F. L. (2011). Exploratory regression analysis: A tool for selecting models and determining predictor importance. Behavior Research Methods, 43, 331–339. [Google Scholar] [CrossRef] [PubMed]
  10. Cao, X., Chen, L., Tian, L., Diao, Y., & Hu, X. (2015). Effect of professional self-concept on burnout among community health nurses in Chengdu, China: The mediator role of organisational commitment. Journal of Clinical Nursing, 24(19–20), 2907–2915. [Google Scholar] [CrossRef] [PubMed]
  11. Chiaburu, D. S., Marinova, S. V., & Lim, A. S. (2007). Helping and proactive extra-role behaviors: The influence of motives, goal orientation, and social context. Personality and Individual Differences, 43(8), 2282–2293. [Google Scholar] [CrossRef]
  12. Chughtai, A. A., & Buckley, F. (2011). Work engagement: Antecedents, the mediating role of learning goal orientation and job performance. Career Development International, 16(7), 684–705. [Google Scholar] [CrossRef]
  13. Cillessen, A., Jiang, X. L., West, T., & Laszkowski, D. (2005). Predictors of dyadic friendship quality in adolescence. International Journal of Behavioral Development, 29(2), 165–172. [Google Scholar] [CrossRef]
  14. Cobo-Rendón, R., Pérez-Villalobos, M. V., Páez-Rovira, D., & Gracia-Leiva, M. (2020). A longitudinal study: Affective wellbeing, psychological wellbeing, self-efficacy and academic performance among first-year undergraduate students. Scandinavian Journal of Psychology, 61(4), 518–526. [Google Scholar] [CrossRef] [PubMed]
  15. Conley, C. S., Shapiro, J. B., Huguenel, B. M., & Kirsch, A. C. (2020). Navigating the college years: Developmental trajectories and gender differences in psychological functioning, cognitive-affective strategies, and social well-being. Emerging Adulthood, 8(2), 103–117. [Google Scholar] [CrossRef]
  16. Dave, H. P., Keefer, K. V., Snetsinger, S. W., Holden, R. R., & Parker, J. D. (2021). Stability and change in trait emotional intelligence in emerging adulthood: A four-year population-based study. Journal of Personality Assessment, 103(1), 57–66. [Google Scholar] [CrossRef] [PubMed]
  17. DeFreitas, S. C., & Rinn, A. (2013). Academic achievement in first generation college students: The role of academic self-concept. Journal of the Scholarship of Teaching and Learning, 13(1), 57–67. [Google Scholar]
  18. Deil-Amen, R. (2006). To teach or not to teach “social” skills: Comparing community colleges and private occupational colleges. Teachers College Record, 108(3), 397–421. [Google Scholar] [CrossRef]
  19. Dickhäuser, O., Butler, R., & Tönjes, B. (2007). Das zeigt doch nur. dass ich’s nicht kann. Zielorientierung und Einstellung gegenüber Hilfe bei Lehramtsanwärtern [That just shows I can’t do it: Goal orientation and attitudes concerning help amongst pre service teachers]. Zeitschrift für Entwicklungspsychologie und Pädagogische Psychologie, 39, 120–126. [Google Scholar] [CrossRef]
  20. Dickhäuser, O., Schöne, C., Spinath, B., & Stiensmeier-Pelster, J. (2002). Die Skalen zum akademischen Selbstkonzept: Konstruktion und Überprüfung eines neuen Instrumentes [The academic self concept scales: Construction and evaluation of a new instrument]. Zeitschrift für Differentielle und Diagnostische Psychologie: ZDDP, 23(4), 393–405. [Google Scholar] [CrossRef]
  21. Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149–1160. [Google Scholar] [CrossRef] [PubMed]
  22. Felmlee, D., & Muraco, A. (2009). Gender and friendship norms among older adults. Research on Aging, 31(3), 318–344. [Google Scholar] [CrossRef] [PubMed]
  23. Ferrin, D. L., Bligh, M. C., & Kohles, J. C. (2008). It takes two to tango: An interdependence analysis of the spiraling of perceived trustworthiness and cooperation in interpersonal and intergroup relationships. Organizational Behavior and Human Decision Processes, 107(2), 161–178. [Google Scholar] [CrossRef]
  24. Filiz, M., Early, E., Thurston, A., & Miller, S. (2020). Measuring and improving university students’ statistics self-concept: A systematic review. International Journal of Educational Research Open, 1, 100020. [Google Scholar] [CrossRef]
  25. Goagoses, N., & Koglin, U. (2022). The association between social goals and academic variables in school: A systematic review. Oxford Review of Education, 48(2), 234–251. [Google Scholar] [CrossRef]
  26. Gogol, K., Brunner, M., Martin, R., Preckel, F., & Goetz, T. (2017). Affect and motivation within and between school subjects: Development and validation of an integrative structural model of academic self-concept, interest, and anxiety. Contemporary Educational Psychology, 49, 46–65. [Google Scholar] [CrossRef]
  27. Goretzko, D., Pham, T. T. H., & Bühner, M. (2021). Exploratory factor analysis: Current use, methodological developments and recommendations for good practice. Current Psychology, 40, 3510–3521. [Google Scholar] [CrossRef]
  28. Grootenboer, P. (2010). Affective development in university education. Higher Education Research & Development, 29(6), 723–737. [Google Scholar] [CrossRef]
  29. Guo, J. P., Yang, L. Y., Zhang, J., & Gan, Y. J. (2022). Academic self-concept, perceptions of the learning environment, engagement, and learning outcomes of university students: Relationships and causal ordering. Higher Education, 83, 809–828. [Google Scholar] [CrossRef]
  30. Hall, J. A. (2012). Friendship standards: The dimensions of ideal expectations. Journal of Social and Personal Relationships, 29(7), 884–907. [Google Scholar] [CrossRef]
  31. Härtel, C. E. J. (2008). How to build a healthy emotional culture and avoid a toxic culture. In C. L. Cooper, & N. M. Ashkanasy (Eds.), Research companion to emotion in organization (pp. 1260–1291). Edwin Elgar Publishing. [Google Scholar]
  32. Hemmerich, W. (2024). StatistikGuru: Multiple lineare regression [Statisticsguru: Multiple linear regression]. Available online: https://statistikguru.de/spss/multiple-lineare-regression (accessed on 10 January 2025).
  33. Hu, S., Cai, D., Zhang, X. C., & Margraf, J. (2022). Relationship between social support and positive mental health: A three-wave longitudinal study on college students. Current Psychology, 41(10), 6712–6721. [Google Scholar] [CrossRef]
  34. Johnston, P. C., Schimmel, T., & O’Hara, H. (2012). Revisiting the AAUP recommendation: The viability of collegiality as a fourth criterion for university faculty evaluation. College Quarterly, 15(1), 1. [Google Scholar]
  35. Kahu, E. R. (2013). Framing student engagement in higher education. Studies in Higher Education, 38(5), 758–773. [Google Scholar] [CrossRef]
  36. Kim, B., & Kim, Y. (2017). College students’ social media use and communication network heterogeneity: Implications for social capital and subjective well-being. Computers in Human Behavior, 73, 620–628. [Google Scholar] [CrossRef]
  37. Klein, C., DeRouin, R. E., & Salas, E. (2006). Uncovering workplace interpersonal skills: A review, framework, and research agenda. International Review of Industrial and Organizational Psychology, 21, 79–126. [Google Scholar] [CrossRef]
  38. Knapp, M. L., Vangelisti, A. L., & Caughlin, J. P. (2014). Interpersonal communication and human relationships (7th ed.). Allyn & Bacon. [Google Scholar]
  39. Kolb, A. Y., & Kolb, D. A. (2005). Learning styles and learning spaces: Enhancing experiential learning in higher education. Academy of Management Learning & Education, 4(2), 193–212. [Google Scholar] [CrossRef]
  40. Kolb, A. Y., & Kolb, D. A. (2017). Experiential learning theory as a guide for experiential educators in higher education. Experiential Learning & Teaching in Higher Education, 1(1), 7–44. [Google Scholar]
  41. Kolb, D. A. (2015). Experiential learning: Experience as the source of learning and development (2nd ed.). Pearson. [Google Scholar]
  42. Körner, R., Heydasch, T., & Schütz, A. (2023). Dominance and prestige as self-concept facets. Journal of Personality Assessment, 105(5), 590–609. [Google Scholar] [CrossRef] [PubMed]
  43. Krathwohl, D. R., Bloom, B. S., & Masia, B. B. (1964). Taxonomy of educational objectives, handbook II: Affective domain. David McKay Company. [Google Scholar]
  44. Lei, Z. (2024). Dimensionalized goal orientation, innovation climate, and knowledge sharing behavior in higher education research teams. Heliyon, 10(7), e27853. [Google Scholar] [CrossRef] [PubMed]
  45. León-del-Barco, B., Mendo-Lázaro, S., Felipe-Castaño, E., Fajardo-Bullón, F., & Iglesias-Gallego, D. (2018). Measuring responsibility and cooperation in learning teams in the university setting: Validation of a questionnaire. Frontiers in Psychology, 9, 326. [Google Scholar] [CrossRef] [PubMed]
  46. Levine, E. E., Bitterly, T. B., Cohen, T. R., & Schweitzer, M. E. (2018). Who is trustworthy? Predicting trustworthy intentions and behavior. Journal of Personality and Social Psychology, 115(3), 468–494. [Google Scholar] [CrossRef] [PubMed]
  47. Levy, I., Kaplan, A., & Patrick, H. (2004). Early adolescents’ achievement goals, social status, and attitudes towards cooperation with peers. Social Psychology of Education, 7(2), 127–159. [Google Scholar] [CrossRef]
  48. Levy-Tossman, I., Kaplan, A., & Assor, A. (2007). Academic goal orientations, multiple goal profiles, and friendship intimacy among early adolescents. Contemporary Educational Psychology, 32(2), 231–252. [Google Scholar] [CrossRef]
  49. Lipsey, M. W. (1990). Design sensitivity. Statistical power for experimental research. Sage. [Google Scholar]
  50. Liu, D., Wang, S., & Wayne, S. J. (2015). Is being a good learner enough? An examination of the interplay between learning goal orientation and impression management tactics on creativity. Personnel Psychology, 68(1), 109–142. [Google Scholar] [CrossRef]
  51. Liu, Y., & Pek, J. (2024). Summed versus estimated factor scores: Considering uncertainties when using observed scores. Psychological Methods. Advance online publication. [Google Scholar] [CrossRef] [PubMed]
  52. Luo, Y., Shi, Z., & Zhong, Y. (2021). Self-esteem and interpersonal difficulties of college students: Influences of self-control and self-concept. Journal of Psychology in Africa, 31(6), 615–621. [Google Scholar] [CrossRef]
  53. Maguire, R., Egan, A., Hyland, P., & Maguire, P. (2017). Engaging students emotionally: The role of emotional intelligence in predicting cognitive and affective engagement in higher education. Higher Education Research & Development, 36(2), 343–357. [Google Scholar] [CrossRef]
  54. Makarla, P. R., Joergensen, G. H., Tyner, K. B., Sprinkle, C., & Rothermich, K. (2024). Differences in politeness perception of irony and prosocial lies: Exploring the role of age, gender, and geographic location. Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale, 78(2), 100–113. [Google Scholar] [CrossRef] [PubMed]
  55. Malinauskas, R., Dumciene, A., & Lapeniene, D. (2014). Social skills and life satisfaction of Lithuanian first-and senior-year university students. Social Behavior and Personality: An International Journal, 42(2), 285–293. [Google Scholar] [CrossRef]
  56. Mischel, W., & Shoda, Y. (1995). A cognitive-affective system theory of personality: Reconceptualizing situations, dispositions, dynamics, and invariance in personality structure. Psychological Review, 102(2), 246–268. [Google Scholar] [CrossRef] [PubMed]
  57. Morán, V. E., Olaz, F. O., & Del Prette, Z. A. (2015). Social skills questionnaire for Argentinean college students (SSQ-U) development and validation. The Spanish Journal of Psychology, 18, E95. [Google Scholar] [CrossRef] [PubMed]
  58. Mynott, G. J. (2018). The academic self-concept of business and management students: A review of the literature. The International Journal of Management Education, 16(3), 515–523. [Google Scholar] [CrossRef]
  59. Niemiec, R. M. (2018). Character strengths interventions: A field guide for practitioners. Hogrefe. [Google Scholar]
  60. Ogden, T. (2003). The validity of teacher ratings of adolescents’ social skills. Scandinavian Journal of Educational Research, 47(1), 63–76. [Google Scholar] [CrossRef]
  61. Okur-Berberoglu, E. (2024). Affective learning: Why we need a humanistic curriculum. World Futures, 80(1), 56–69. [Google Scholar] [CrossRef]
  62. Park, C. L., Williams, M. K., Hernandez, P. R., Agocha, V. B., Lee, S. Y., Carney, L. M., & Loomis, D. (2020). Development of emotion regulation across the first two years of college. Journal of Adolescence, 84, 230–242. [Google Scholar] [CrossRef] [PubMed]
  63. Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18, 315–341. [Google Scholar] [CrossRef]
  64. Percival, N. M., & Pulford, B. D. (2020). Do say “thank you”: Verbal expressions of politeness and gratitude influence interpersonal perceptions. The Journal of General Psychology, 147(3), 228–243. [Google Scholar] [CrossRef] [PubMed]
  65. Pierre, E., & Oughton, J. (2007). The affective domain: Undiscovered country. College Quarterly, 10(4), 1–7. [Google Scholar] [CrossRef]
  66. Pittman, L. D., & Richmond, A. (2008). University belonging, friendship quality, and psychological adjustment during the transition to college. The Journal of Experimental Education, 76(4), 343–362. [Google Scholar] [CrossRef]
  67. Preckel, F., Niepel, C., Schneider, M., & Brunner, M. (2013). Self-concept in adolescence: A longitudinal study on reciprocal effects of self-perceptions in academic and social domains. Journal of Adolescence, 36(6), 1165–1175. [Google Scholar] [CrossRef] [PubMed]
  68. Purswell, K. E., Yazedjian, A., & Toews, M. L. (2008). Students’ intentions and social support as predictors of self-reported academic behaviors: A comparison of first- and continuing-generation college students. Journal of College Student Retention: Research, Theory & Practice, 10(2), 191–206. [Google Scholar] [CrossRef]
  69. Reason, R. D., Terenzini, P. T., & Domingo, R. J. (2006). First things first: Developing academic competence in the first year of college. Research in Higher Education, 47, 149–175. [Google Scholar] [CrossRef]
  70. Rogaten, J., Rienties, B., Sharpe, R., Cross, S., Whitelock, D., Lygo-Baker, S., & Littlejohn, A. (2019). Reviewing affective, behavioural and cognitive learning gains in higher education. Assessment & Evaluation in Higher Education, 44(3), 321–337. [Google Scholar] [CrossRef]
  71. Roussel, P., Elliot, A. J., & Feltman, R. (2011). The influence of achievement goals and social goals on help-seeking from peers in an academic context. Learning and Instruction, 21(3), 394–402. [Google Scholar] [CrossRef]
  72. Sakiz, G. (2011). Mastery and performance approach goal orientations in relation to academic self-efficacy beliefs and academic help seeking behaviors of college students in Turkey. Educational Research, 2(1), 771–778. [Google Scholar]
  73. Savitz-Romer, M., Rowan-Kenyon, H. T., & Fancsali, C. (2015). Social, emotional, and affective skills for college and career success. Change: The Magazine of Higher Learning, 47(5), 18–27. [Google Scholar] [CrossRef]
  74. Schimmel, T., Johnston, P. C., & Stasio, M. (2013). Can “Collegiality” be measured? Further validation of a faculty assessment model of discretionary behaviors. Research in Higher Education Journal, 21, 1–7. [Google Scholar]
  75. Schmidt, A. F., & Finan, C. (2018). Linear regression and the normality assumption. Journal of Clinical Epidemiology, 98, 146–151. [Google Scholar] [CrossRef] [PubMed]
  76. Schneider, S., Beege, M., Nebel, S., Schnaubert, L., & Rey, G. D. (2022). The cognitive-affective-social theory of learning in digital environments (CASTLE). Educational Psychology Review, 34(1), 1–38. [Google Scholar] [CrossRef] [PubMed]
  77. Sheldon, K. M., & Krieger, L. S. (2004). Does legal education have undermining effects on law students? Evaluating changes in motivation, values, and well-being. Behavioral Sciences & the Law, 22(2), 261–286. [Google Scholar] [CrossRef] [PubMed]
  78. Shin, H., & Ryan, A. M. (2014). Friendship networks and achievement goals: An examination of selection and influence processes and variations by gender. Journal of Youth and Adolescence, 43, 1453–1464. [Google Scholar] [CrossRef] [PubMed]
  79. Spagnolo, G. (1999). Social relations and cooperation in organizations. Journal of Economic Behavior & Organization, 38(1), 1–25. [Google Scholar] [CrossRef]
  80. Steinberg, O., Kulakow, S., & Raufelder, D. (2024). Academic self-concept, achievement, and goal orientations in different learning environments. European Journal of Psychology of Education, 39, 3893–3917. [Google Scholar] [CrossRef]
  81. Taing, M. U., Smith, T., Singla, N., Johnson, R. E., & Chang, C. H. (2013). The relationship between learning goal orientation, goal setting, and performance: A longitudinal study. Journal of Applied Social Psychology, 43(8), 1668–1675. [Google Scholar] [CrossRef]
  82. Thomas, R. (2019). College student peer aggression: A review with applications for colleges and universities. Aggression and Violent Behavior, 48, 218–229. [Google Scholar] [CrossRef]
  83. Van’t Wout, M., & Sanfey, A. G. (2008). Friend or foe: The effect of implicit trustworthiness judgments in social decision-making. Cognition, 108(3), 796–803. [Google Scholar] [CrossRef] [PubMed]
  84. Walz, K., & Braun, E. (2022). A competency level model for communication skills. Higher Education Forum, 19, 45–69. [Google Scholar]
  85. Wang, G., & Hu, W. (2021). Peer relationships and college students’ cooperative tendencies: Roles of interpersonal trust and social value orientation. Frontiers in Psychology, 12, 656412. [Google Scholar] [CrossRef] [PubMed]
  86. Westby, C. E. (2014). Social neuroscience and theory of mind. Folia Phoniatrica et Logopaedica, 66(1–2), 7–17. [Google Scholar] [CrossRef] [PubMed]
  87. Wibrowski, C. R., Matthews, W. K., & Kitsantas, A. (2017). The role of a skills learning support program on first-generation college students’ self-regulation, motivation, and academic achievement: A longitudinal study. Journal of College Student Retention: Research, Theory & Practice, 19(3), 317–332. [Google Scholar] [CrossRef]
  88. Winstone, N., Balloo, K., Gravett, K., Jacobs, D., & Keen, H. (2022). Who stands to benefit? Wellbeing, belonging and challenges to equity in engagement in extra-curricular activities at university. Active Learning in Higher Education, 23(2), 81–96. [Google Scholar] [CrossRef]
  89. Wu, H., Guo, Y., Yang, Y., Zhao, L., & Guo, C. (2021). A meta-analysis of the longitudinal relationship between academic self-concept and academic achievement. Educational Psychology Review, 33, 1749–1778. [Google Scholar] [CrossRef]
  90. Zander, L., Brouwer, J., Jansen, E., Crayen, C., & Hannover, B. (2018). Academic self-efficacy, growth mindsets, and university students’ integration in academic and social support networks. Learning and Individual Differences, 62, 98–107. [Google Scholar] [CrossRef]
  91. Zhu, Y., & Bresnahan, M. J. (2018). Collective face, politeness strategies, and discomfort: Communication of American domestic students and Chinese international students. Journal of Intercultural Communication Research, 47(2), 141–159. [Google Scholar] [CrossRef]
Table 1. Descriptive Statistics, Cronbach’s Alpha (CA), and Correlations Among the Study Variables. 
Table 1. Descriptive Statistics, Cronbach’s Alpha (CA), and Correlations Among the Study Variables. 
VariableMSDCACorrelations
12345678
1. Politeness4.640.430.79–
2. Trustworthiness4.640.460.760.36 ***–
3. Cooperation4.160.670.690.41 ***0.27 ***–
4. Support3.340.970.810.30 ***0.120.51 ***–
5. Friendship3.750.930.810.31 ***0.080.50 ***0.62 ***–
6. Academic self-concept3.640.700.860.130.15 *0.18 *0.26 ***0.22 **–
7. Learning goal orientation4.360.620.870.23 **0.080.17 *0.18 *0.14 *0.40 ***–
8. Years of study3.771.95–0.010.100.03−0.02−0.010.11−0.04–
Note. N = 200. * p < 0.05. ** p < 0.01. *** p < 0.001.
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Astleitner, H.; Zumbach, J. Exploring Academic Self-Concept, Learning Goal Orientation, and Years of Study as Predictors of University Students’ Affective Learning Outcomes. Educ. Sci. 2026, 16, 1072. https://doi.org/10.3390/educsci16071072

AMA Style

Astleitner H, Zumbach J. Exploring Academic Self-Concept, Learning Goal Orientation, and Years of Study as Predictors of University Students’ Affective Learning Outcomes. Education Sciences. 2026; 16(7):1072. https://doi.org/10.3390/educsci16071072

Chicago/Turabian Style

Astleitner, Hermann, and Joerg Zumbach. 2026. "Exploring Academic Self-Concept, Learning Goal Orientation, and Years of Study as Predictors of University Students’ Affective Learning Outcomes" Education Sciences 16, no. 7: 1072. https://doi.org/10.3390/educsci16071072

APA Style

Astleitner, H., & Zumbach, J. (2026). Exploring Academic Self-Concept, Learning Goal Orientation, and Years of Study as Predictors of University Students’ Affective Learning Outcomes. Education Sciences, 16(7), 1072. https://doi.org/10.3390/educsci16071072

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