Do Learning Approaches Set the Stage for Emotional Well-Being in College Students?

: The research aim of this paper was two-fold: to generate evidence that personality factors are linear predictors of the variable approaches to learning (a relevant cognitive-motivational variable of Educational Psychology); and to show that each type of learning approach di ﬀ erentially predicts positive or negative achievement emotions, in three learning situations: class time, study time, and testing. A total of 658 university students voluntarily completed validated questionnaires referring to these three variables. Using an ex post facto design, we conducted correlational analyses, regression analyses, and multiple structural predictions. The results showed that Conscientiousness is associated with and predicts a Deep Approach to learning, while also predicting positive achievement emotions. By contrast, Neuroticism is associated with and signiﬁcantly predicts a Surface Approach to learning, as well as negative achievement emotions. There are important psychoeducational implications in the university context, both for prevention and for self-improvement, and for programs that o ﬀ er psychoeducational guidance.


Introduction
The experience of students in tertiary education is important because of its consequences in their physical and mental health and in their achievement, adaptation, and well-being [1][2][3][4]. For this reason precisely, it has drawn the interest of researchers in Educational Psychology [5]. Given the impact of stress on the academic and social functioning of university students, it is very important to identify factors that predict stress and well-being [6].

Students' Well-Being at University
Analysis of well-being at university has been undertaken in response to the Positive Psychology paradigm [7], and contrasts with the exclusive study of negative stress experiences at university [8].
Well-being at university has become an added value of the university learning experience [9]. Universities compete to produce a comprehensive experience of well-being in their students, beyond the offer of quality training during this academic period [10][11][12].
Achievement-related emotions emerge as a construct specific to the realm of learning and academic achievement, based on expectancy value theory [54]. The universal nature of achievement emotions has been reported recently, being consistent across cultures, although with certain modulating adjustments [55]. Given the specificity of the construct, there are few studies to date that relate the BF factors to types of achievement emotions [56,57].
Recent research has reported a consistent relationship between the Big Five model and achievement emotions. The conscientiousness factor predicts positive achievement emotions, while neuroticism predicts negative emotions [58,59].

Learning Approaches and Achievement Emotions
Different learning approaches reveal different ways of learning and understanding curriculum content and may also be related to different learning and achievement contexts. The learning environment is evidently important in how students engage with learning tasks [60], if we consider that they must make a personal assessment about the teaching context, grading methods, type of course, and the tasks involved in learning [23,61,62] in order to choose their strategies. Recent evidence has established that learning approaches depend on both the student's degree of self-regulation and how well the teaching process promotes regulation [63][64][65].
Certain recent research has demonstrated the specific relationship between learning strategies and achievement emotions when learning [66][67][68]. However, the degree to which this construct is related to the experience of emotional well-being at university is yet to be understood [69,70]. Other recent studies have shown that positive emotions predispose problem-focused coping strategies and engagement, while negative emotions predispose coping strategies geared to managing emotions, ultimately leading to an emotional state of burnout [71]. On this account, it is important to establish the relations between achievement emotions and learning approaches.

Objectives and Hypotheses
The aim of this research was to verify whether personality factors predict learning approaches and whether these two aspects jointly predict positive and negative achievement emotions. For this purpose, we tested the following hypotheses: (1) The positive factors of the Big Five model will significantly predict the Deep Approach, especially so in the case of Conscientiousness. The negative factor in the model, Neuroticism, will predict the Surface Approach. (2) The Deep Approach and its components will predict positive emotions, while the Surface Approach will predict negative emotions. (3) Conscientiousness and Deep Approach will appear as joint predictors of positive emotions, and Neuroticism and the Surface Approach will jointly predict negative emotions.

Participants
The study sample contained a total of 642 undergraduate students who were enrolled at one of two universities in Spain. The students pursued degrees in Psychology, Primary Education, and Educational Psychology; 85.5% were female and 14.5% were male. Ages ranged from 19 to 25, with a mean of 21.33 years. The students were evenly split between the two universities, 324 attended one and 318 attended the other. An incidental, nonrandomized study design was used. Each university's Guidance Department invited participation from the teachers, and the teachers invited their students to participate, on an anonymous, voluntary basis. Each class subject was considered one specific teaching-learning process, questionnaires were completed online for each subject.
Learning Approaches. This variable was measured using the revised two-factor study process questionnaire, R-SPQ-2F [74,75], in its Spanish validated version [76]. There are four subscales (Deep Motivation, Deep Strategy, Surface Motivation, Surface Strategy) that measure the two dimensions of Deep and Surface approaches to learning, respectively. Items are answered on a 5-point Likert scale, from 1 ('rarely true of me') to 5 ('always true of me'). A second factor structure with two factors was produced by using confirmatory factor analysis (Chi-Square = 2645.77; df = 169, CFI = 0.95, GFI = 0.91, AGFI = 0.92, RMSEA = 0.07). Reliability coefficients were also acceptable (Deep, α = 0.81; Surface, α = 0.77), similar to what the original authors found.
They can also be classified according to the source of the emotion: the activity in progress (enjoyment, boredom, anger), a prospective outcome (hope, anxiety, hopelessness), or a retrospective outcome (pride, relief, shame). A factor structure that corresponds to the AEQ Model was confirmed in this sample through Confirmatory Factor Analysis [78]: (1) Achievement Emotions in Class

Procedure
Informed consent was obtained from all participants. Scales were completed on a voluntary basis, using an online platform [79]. Over a two-year period, students reported on five specific teaching-learning processes, each one referring to a different university subject they were taking during this time. The September-October assessment, in 2018 and 2019, covered Presage variables. Process variables were assessed in the following February-March, and Product variables in May-June. The procedure was approved by the respective Ethics Committees of the two universities, in the larger context of an R&D Project (2018-2021).

Data Analysis
Hypothesis 1. The positive factors of the Big Five model will significantly predict the Deep Approach, especially so in the case of Conscientiousness. The negative factor in the model, Neuroticism, will predict the Surface Approach.

Hypothesis 2.
The Deep Approach and its components will predict positive emotions, while the Surface Approach will predict negative emotions.

Hypothesis 3. Conscientiousness and Deep
Approach will appear as joint predictors of positive emotions, and Neuroticism and the Surface Approach will jointly predict negative emotions.
Previous analyses. In order to ensure that the university variable did not affect the analyses, we confirmed that there were no significant differences between the variables analyzed, using different one-way and multi-way ANOVAs.
Multiple regression. For Hypothesis 2, we conducted a multiple regression analysis, also using SPSS (V. 25).
Confirmatory Factor Analysis and Reliability. For Hypothesis 3, a Structural Equation Model (SEM) was used to test in this sample. Data were aggregated by the determination of factors obtained in the previous exploratory and confirmatory factor analyses-not in a summational fashion, in order to avoid false positives. We assessed model fit by first examining the ratio of chi-square to degrees of freedom, SRMR, then the Comparative Fit Index (CFI), Normed Fit Index (NFI), Incremental Fit Index (IFI), and Relative Fit Index (RFI). Ideally, these should all be greater than 0.90. Sample size adequacy was checked using the Hoelter Index [80]. The analyses were conducted using AMOS (version 22, IBM Corporation, Chicago, IL, USA).

Bivariate Association
Bivariate association results showed that the factors of Conscientiousness (C), Openness (O), Extraversion (E), and Agreeableness (A) had a significant, positive association with the Deep Approach (DA), and negative association with the Surface Approach (SA). A significant, negative relationship also appeared between Neuroticism (N) and the Deep Approach; its relationship with the Surface Approach was positive. Association strength was greatest between the personality factors and the factor Deep Motivation (DM). Direct values are presented in Table 1.

Multiple Regression
Regression results were consistent with previous results, showing that the personality factors C and O positively predicted the Deep Approach and negatively predicted the Surface Approach. Factor N positively predicted the Surface Approach and negatively predicted the Deep Approach. Statistical effect size appeared in the prediction of the Deep Approach. See Table 2 for more details. Note. E = Extraversion; C = Conscientiousness; N = Neuroticism; A = Agreeableness; O = Openness; * p < 0.05; ** p < 0.01; *** p < 0.001.

Bivariate Association
Bivariate association results showed that the Deep Approach (DA) and its components had a consistent, significant, positive association with positive emotions, and were negatively associated with negative emotions. In the case of the Surface Approach (SA), the inverse effect appeared, correlating negatively with positive emotions, and correlating positively with negative emotions. The strongest associations were seen with the positive emotion of enjoyment, and the negative, deactivating emotions of boredom and hopelessness. As seen in Table 3, this behavioral pattern is stable across the three academic situations examined. Table 3. Bivariate correlations between Learning Approaches and Achievement Emotions, in three situations (n = 658).

Multiple Regression
Results of the multiple regression analyses between Achievement Emotions (IVs) and Learning Approaches (DV) showed that: (1) the positive emotions enjoyment and hope positively predict the Deep Approach (DA), especially the DM component. However, the emotions of boredom, anger, and hopelessness negatively predict DA. The negative emotion of shame also predicts this approach in study and testing situations. (2) The emotions that positively predict Surface Approach (SA) are boredom and hopelessness, particularly so in the aspect of Surface Strategies (SS). There are some differences between the situations, however. In the study and testing situations, enjoyment negatively predicts the Surface Approach, while boredom and anger are positive predictors. The hopelessness emotion also positively predicts the Surface Approach, in the class situation as well as in testing. See Table 4.

Bivariate Association
Bivariate association results showed that Big Five (BF) and its components (E,C,A, and O) had a consistent, significant, positive association with positive emotions, as well as a negative association with negative emotions. In the case of Neuroticism (N), the inverse effect appeared, correlating negatively with positive emotions, and correlating positively with negative emotions. The strongest associations were seen with the negative emotions of anxiety, anger, and hopelessness. As seen in Table 5, this behavioral pattern is stable across the three academic situations examined. The strongest positive association between N and negative emotions was produced in study and testing situations.

Multiple Regression
Results of the multiple regression analyses between Achievement Emotions (IVs) and the Big Five (DVs) showed that: (1) the positive emotions enjoyment and hope positively predicted E, C, and A, and negatively predicted N, in class and testing, but not in the study situation. Predictions that differ according to the situation are worth noting. In the class situation, the factors mostly strongly predicted by emotions are C and E; in the study and testing situations, C and N are most strongly predicted. In no situation was C predicted by enjoyment. Negative emotions (anger or anxiety) were usually predictors of the N factor; however, in the study and testing situations, for example, they are predictive of both C and E. See Table 6. Note. E = Extraversion; C = Conscientiousness; N = Neuroticism; A = Agreeableness; O = Openness; * p < 0.05; ** p < 0.01; *** p < 0.001.

Structural Predictions: Personality, Learning Approaches and Achievement Emotions
Multiple prediction analysis, using SEM, showed three consistent prediction models; their statistics are presented in Table 7. Model 0 (three situations) tested the relationship with the complete construct (5 BF factors), and the statistical values obtained were less adequate. Models 1 to 3 selected only C and N as predictors; these models showed adequate significance.

Direct Effects
There were several significant, direct predictive effects. Conscientiousness (C) was a significant positive predictor of the Deep Approach (while negatively predicting the Surface Approach); the Deep approach in turn predicted Positive Emotions (PE). However, Neuroticism (N) was a significant positive predictor of the Surface Approach (SA) and of Negative Emotions (NE). There was also a positive direct effect of DA on Positive Emotions, and of SA on Negative Emotions. See Table 8 for more details.

Indirect Effects
There were several indirect positive effects of the Conscientiousness factor on DA factors and on Positive Emotions, as well as negative effects on SA factors and on Negative Emotions. In the case of Neuroticism, the positive effect was on SA factors and on Negative Emotions. Also worth noting is the negative indirect effect of the DA factor on the SA factor, as well as on Negative Emotions, and the positive effect of the SA factor on Negative Emotions. See Table 9 for more details. Figure 1 graphically illustrates these effects.    Table 10 for further details.   Table 10 for further details.

Indirect Effects
In a complementary fashion, the C factor had numerous positive indirect effects on the DA factor and its components, as well as on positive emotions. The N factor showed these effects in the opposite direction. Similarly, the Deep Approach showed an indirect predictive effect on emotions, positively predicting Positive Emotions, and negatively predicting Negative Emotions. See Table 11 and Figure 2 for further details.

Direct Effects
The C factor negatively predicted N and SA, while positively predicting DA and Positive Emotions. The N factor positively predicted SA and negatively predicted Negative Emotions. The Deep Approach positively predicted Positive Emotions, while the Surface Approach predicted Negative Emotions. See Table 12 and Figure 3 for further details.

Direct Effects
The C factor negatively predicted N and SA, while positively predicting DA and Positive Emotions. The N factor positively predicted SA and negatively predicted Negative Emotions. The Deep Approach positively predicted Positive Emotions, while the Surface Approach predicted Negative Emotions. See Table 12 and Figure 3 for further details.

Indirect Effects
The C factor had a negative predictive effect on N, SA, and Negative Emotions. It also had a positive effect toward DM, its components, and Positive Emotions. The N factor produced effects in the opposite direction. As for predictive effects on DA, C was a positive predictor, and N was a negative predictor. See Table 13 and Figure 3 for further details.

Indirect Effects
The C factor had a negative predictive effect on N, SA, and Negative Emotions. It also had a positive effect toward DM, its components, and Positive Emotions. The N factor produced effects in the opposite direction. As for predictive effects on DA, C was a positive predictor, and N was a negative predictor. See Table 13 and Figure 3 for further details.

Discussion
Generally speaking, the results support our hypotheses. Regarding the first hypothesis, on the potential association and linear prediction between personality variables and learning approaches, the relationships found here confirmed prior evidence. While Conscientiousness positively predicted the Deep Approach and negatively predicted the Surface Approach and its components, Neuroticism positively predicted the SA approach and its components. These results are similar to those reported in previous research, where these two factors appear as protective vs. risk factors with respect to achievement [14,49,51,52,59,81,82]. These results remain unchanged in the three situations analyzed (class, study, testing); This would suggest a constant effect of personality factors on motivation, with either positive or negative directionality [83].
Regarding the second hypothesis, on the possible association and predictive relationship of learning approaches and achievement emotions, there was a consistent relationship between DA and Positive Emotions, and between SA and Negative Emotions. These results are novel because they offer precise evidence of how learning approaches also possess an unmistakable emotional component [84]. The classical view of learning approaches, as eminently cognitive-motivational variables, should therefore incorporate these affective-type results [85] (Sharp, Sharp, & Young, 2020). It seems reasonable to assume that positive emotions (enjoyment, etc.) positively reinforce one's motivational state during class, study and testing; while the feedback of negative emotions (anger, anxiety, etc.) interferes with learning [86][87][88]. The latter would contribute to greater avoidance and flight responses because of the negative emotional component of this learning profile. It is furthermore interesting to note that, while the positive emotions of DA are more associated with the DM component, in the case of SA, emotions are linearly associated with SS more than with SM, indicating that the emotional state affects the cognitive processes of surface strategy, and not only the surface motivational state [66]. The negative deactivating emotion of boredom has greater weight in class and study situations, while the positive deactivating emotion of relief is more relevant in testing situations. However, the relationship is maintained in the three situations, revealing stability in the students' emotional responses, according to their learning approaches. This would confer on learning approaches a personalistic component, or a stable motivational-affective style [50,88,89].
Regarding the third hypothesis, the results present three structural predictive models, which are quite similar in the three situations analyzed. This demonstrates that the relationship between the BF characteristics (C and N, as essential factors), learning approaches (as a mediating variable), and achievement emotions is stable in the three situations (class, study, testing), despite the differences between them. The differences in emotional response between one situation and another, according to their level of additional stress, has been analyzed previously [90].
That emotions would be jointly predicted by personality variables and learning approaches, the structural models have shown two consistent triangles-seen graphically in our figures. One triangle represents protective variables of learning (C-> DA-> PE) and one triangle represents risk variables (N-> SA-> NE). Moreover, the two triangles are produced consistently across the three learning situations. Although this result is modulated by the statistically greater predictive strength of the personality variables (C and N), it clearly shows the role of learning approaches in predicting positive vs. negative achievement emotions [91]. This relationship shows that personality factors would also have a direct predictive effect on learning approaches, and an indirect effect on achievement emotions; in this relationship, learning approaches would also predict different types of emotions. If we join this relationship to prior evidence that shows that positive emotions predispose problem-focused coping strategies and engagement, while negative emotions predispose emotion-focused strategies and burnout [71], we can conclude that surface learning approaches would tend toward burnout, while deep approaches tend toward engagement. This complex relationship introduces new emotional factors that have not been sufficiently addressed in the research to date.

Conclusions
In summary, this research has shown that learning approaches also have a positive or negative emotional dimension that is worthy of consideration. Thus, while the metacognitive component of learning approaches helps regulate cognitive strategies [70,92,93] (there also seems to be an emotion-regulating component. The Deep Approach not only involves cognitive regulation but also emotion regulation, thanks to the Conscientiousness factor. However, in the case of the Surface Approach, the opposite occurs, that is, it is associated with a lack of cognitive and emotional regulation, due to the effect of Neuroticism.

Limitations and Future Research
The present research study also has limitations. The first limitation of this study refers to its limited sample. Future research should expand on student characteristics and different university origins. Another relevant limitation to consider is that the characteristics of the teaching process under way have not been taken into account when measuring the learning approaches. Recent research has shown how the teaching process induces modifying effects on motivation [94] (Kaplan & Patrick, 2016).
Another important limitation refers to the male/female imbalance in our sample. Prior research has shown the importance of the female gender in learning approaches [95]. Emerging adult women also show greater interindividual variability than men in N and C, in their trajectories between the ages of 16 and 20 years [96]. Consequently, these aspects should be analyzed in future research.
Finally, one important limitation in the present research has to do with the absence of context variables in the analysis. It must not be forgotten that learning approaches are also mediated by factors from the teaching context, and may also mediate the relationships presented here [97][98][99][100]. Future research must integrate analysis of the role of context variables in learning approaches.

Practical Implications: Psychoeducational Intervention
One important practical implication for Educational Psychologists is the need to become aware of students' individual differences, to detect personal characteristics that may be predictive of inappropriate learning approaches. This is especially important in reference to emotional experiences [101] (Li, 2020). University Guidance and Counseling services have a very important role in this preventive evaluation. Assessment of university students' personality characteristics and achievement emotions, in the three situations analyzed, can be a first-order preventive strategy. This information would allow us to detect adaptive or maladaptive emotional states in students, so that we can adjust our intervention to each situation. For example, intervention for the emotion of boredom in class is different from intervention to improve enjoyment while learning or test anxiety during exams.
Another implication for students is the need to assess the student variable of approach to learning, and so be able to promote any help they may need, and to identify students who will most likely need psychoeducational counseling during the course of their university studies. It would be interesting to implement a comprehensive program for managing emotions and coping with stress at university, given the effect that negative emotions have on students and how they may trigger academic burnout [42]. Students with surface approaches to learning are more likely to experience negative emotionality and, consequently, to end in an emotional state of burnout; a deep approach can also lead a high level of perfectionism [71]. Intervention for improving and adjusting coping strategies during study, personalized for each student, might be of considerable help.
Finally, it is essential that teaching faculties become familiar with this evidence and are aware of the relationships between these variables in their students. Effective or regulatory teaching helps to minimize harmful factors, and is largely dependent on the university teacher [17]. Teachers should know their students' approach to learning in order to adjust the teaching process and help students improve their learning process, whether in class, study time, or an exam situation [101,102].

Conflicts of Interest:
The authors declare no conflict of interest.