Joy of distance learning? How student self-efficacy and emotions relate to social support and school environment

Homeschooling in non-pandemic circumstances

Previous results from research on “homeschooling” under non-pandemic circumstances and homework (Hagenauer and Oberwimmer, 2019; Kohler, 2011; Trautwein et al., 2006) are relevant as the role of the teachers as well as the role of social support (e.g., by family and peers) is increasingly important in this context. In this regard, research shows that especially the organization of education matters. Martin-Chang et al. (2011), for instance, found that students who received structured homeschooling outperformed public school students in all subjects but that students who received unstructured homeschooling showed statistically significant reduced performances compared to public school students. As distance learning under COVID-19 circumstances is much more a consequence of a closed school than a voluntary decision to be educated at home, students in distance learning might be especially disadvantaged (Helm et al., 2021) because many parents have no time to guide their children during their lessons. This is especially relevant when teachers do not pre-structure the working process (Voss and Wittwer, 2020). Another substantial difference between homeschooled children and children in distance learning during COVID-19 is the number of social contacts. While homeschooled children and publicly schooled children do not differ in number of social contacts (Chatham-Carpenter, 1994), and homeschooled children participate in more extracurricular social activities than publicly schooled children (Ray, 2017), during the COVID-19 pandemic school closure period, all extracurricular activities have been cancelled. Thus, the pandemic poses an exceptional challenge to students’ socioemotional wellbeing.

In addition to research in the scope of private homeschooling, a large number of (correlational) studies investigated the relation between social integration/commitment to the school and emotion/motivation in public schools. Support from family and school has been shown to promote persistence and perseverance as well as self-efficacy (Datu, 2017; Eskreis-Winkler et al., 2014; Strayhorn, 2014).

Research on student emotions: The roles of enjoyment, the joy of learning, and anxiety

Emotions are closely connected to the learning process (Linnenbrink-Garcia et al., 2016; Pekrun, 2009; Schutz and Lanehart, 2002; Steinmayr et al., 2020). They influence several information processing mechanisms, including attention and recall but also other variables relevant to learning, such as self-regulation and motivation (Pekrun, 2009; Pekrun et al., 2002). Additionally, emotions influence academic achievement by modulating engagement (Linnenbrink, 2007; Linnenbrink and Pintrich, 2004). Positive emotions are positively correlated to self-regulated learning (Boekaerts et al., 2000; Carver and Scheier, 1990; Pekrun et al., 2002). Anger and anxiety have been found to be related to avoidance (Carver and Harmon-Jone, 2009) or extrinsic motivation, which, in turn, is related to a lack of engagement (Assor et al., 2005). Moreover, it has been proposed that emotions signal the occurrence of an emotionally relevant event and characterize the nature of the resulting motivation (Assor et al., 1986; Buck, 1988; Frijda, 2003). Regarding the present context of research on learning during the COVID-19 pandemic, Huber and Helm (2020a) reported on a correlation of positive emotions with learning outcomes as well as the student–teacher relationship, but the researchers found no correlation between any of the student outcomes with the quality of the digital lessons. Technical equipment was associated with higher positive emotions, investment, and learning outcomes. A negative correlation of negative emotions with learning outcomes was found (Huber and Helm, 2020a; Steinmayr et al., 2020). Indicators of independence (“I considered it especially challenging to plan my learning on my own”) were associated with learning outcomes, investment, and positive emotions and negatively correlated with negative emotions (Huber et al., 2020).

Research on self-efficacy as student motivation, time for learning, and learning outcome

Following the general expectancy–value model of achievement choices (Eccles, 2005), motivation as an essential precursor of effective learning (including a high time investment) is not only influenced by anticipated joy, task difficulty, and achievement motives but depends on a complex interplay of intrapersonal and environmental factors, including ability, siblings, interpretations of former experiences, gender roles, cultural stereotypes, the attitudes of the social agents, goals, and expectations. One often-investigated construct of expectation is self-efficacy (Bandura, 1977). Self-efficacy refers to the belief in one’s own capability to solve problems or master challenges within a certain area of competence. Therefore, self-efficacy is future-oriented and task-specific (Bong and Skaalvik, 2003). It is acquired through repeated experiences of successful behavior but also the subjectively experienced belief of social agents in one’s own abilities. Self-efficacy fosters the motivation to learn and to develop new skills (Schunk, 1985). In previous studies, self-efficacy was found to be an influential factor for self-regulation and academic achievement (Bandura, 1977; Pekrun and Perry, 2014; Ryan and Deci, 2000) as well as a predictor of mathematics performance (Pajares and Graham, 1999). It has been shown to be influenced by the learning environment through certain positive experiences, reinforcing feedback, verbal encouragement, social persuasion, and adequate task difficulties (Harter, 1978; Usher and Pajares, 2008; Vallerand and Reid, 1988). Self-efficacy is especially important for children at risk to develop a sense of agency. Therefore, teachers should aim at directing students’ attention to their psychological capital (Seligman, 2002), thus supporting the development of self-efficacy, confidence, hope, and resiliency. This belief is the motivational basis of effort (such as the amount of time spent on learning or perseverance) and learning outcomes (such as knowledge or skills), which are typically measured by self-reporting, teacher questionnaires, standardized assessments, and/or school grades.

Quality of (virtual) exchange during digital lessons

Research has shown that an effective student–teacher relationship is correlated with better academic achievement (Graziano et al., 2007) as well as more frequent use of self-regulated learning strategies (Patrick and Ryan, 2005), learning motivation, and interest (Wentzel, 1998). Therefore, positive student–teacher relationships might prevent students from failing (Pianta et al., 1995). A relationship, that fosters educational outcomes, is characterized by students feeling that their teacher sees them and cares about their concerns, that they can entrust their problems to their teacher, and that their teacher appreciates them. This impression must be built up by positive teacher–student interaction and student–student interaction (Allen et al., 2011; Brophy and Good, 1974; Goodwin et al., 2021). Therefore, it is questionable whether a high-quality student–teacher relationship can be preserved during distance learning. The lack of face-to-face personal contact limits the whole range of information that is typically transmitted during the interaction and impedes many informal occasions to talk about personal emotions, experiences, and worries as well as to ask questions (Resnick et al., 2015). In addition, during structured online lessons, it is challenging to introduce a productive culture of questioning and discussing (Kiemer et al., 2015; Walshaw and Anthony, 2008). While the teacher needs to eliminate disturbances by students during virtual meetings, he or she has only a few opportunities to monitor and regulate attention processes or cheating attempts (Rovai, 2007).

Beyond the absence of direct student–teacher interactions, virtual exchange also reduces contact with peers. A large amount of learning in different respects happens in interaction with peers at school. Cartland et al. (2003) found that school life, with its social networks, peer interaction, and opportunities to receive informal help and support with emotional issues is an important resource for adolescents. Additionally, the sense of community or relatedness with the school has been found to influence not only psychosocial wellbeing but also motivation and attitudes (Bateman, 2002; Pretty et al., 1994). Different viewpoints foster lively discussions and widen the scope of one’s own rational horizon. Peers can help each other to understand tasks and problems and share strategies or serve as models for observational learning (Webb et al., 2002). Dropping out of school has been found to be better predicted by social factors than by parental factors (Vitaro et al., 2001). This might be due to the mediating role of social interactions with teachers (Perry et al., 2002) and peers (Webb and Mastergeorge, 2003) for self-regulated learning (Zimmerman, 1989).

Furthermore, in the direct effects of contact with peers on learning, which is frequently investigated in group-work settings (Webb and Palincsar, 1996), socioemotional components play a central role (Bateman, 2002; Battistich and Hom, 1997; Pretty et al., 1994; Rubin et al., 2006; Zins et al., 2004). Eisenberg et al. (1998), for instance, showed that expressive environments are positively correlated with the capacity to understand emotional expressions and to regulate emotions (Eisenberg et al., 1998; Fried, 2010). In line with this finding, it has been proposed that emotion-related classroom activities might foster competent emotion regulation, which, in turn, is associated with student engagement, motivation, and commitment and, consequently, learning outcomes (Fried, 2011). It has been shown that individual and social elements contribute to intrinsic group dynamics in collaborative learning situations. Collaborative work at school offers numerous opportunities for experiencing social challenges and developing strategies for individual self-regulation as well as co-regulation and shared regulation (Järvenoja and Järvela, 2009). Schunk (1999) argued that attempts to influence others in co-regulation serve the aim of co-constructing a learning environment that is cognitively, motivationally, and emotionally satisfying for the individual. Thus, social processes at school are an important component of learning motivation and emotional wellbeing (Thompson and Fine, 1999). Therefore, the absence of group-work settings and other opportunities for social and socioemotional learning might lead to worse understanding, less depth of processing, a reduction in or loss of motivation, and problems in emotion regulation.

Research on supportive aspects and school-related factors

Although the amount of influence of structural conditions, such as family background, is under debate (Kundu, 2017), research has shown that social aspects and a student’s individual pre-conditions play a key role in educational outcomes (Schubert and Becker, 2010; Vieluf et al., 2020). Sociodemographic characteristics, such as status, residential area, and family income, have been shown to be relevant predictors of student learning outcomes, for example, for college readiness and academic development (Duncan and Murnane, 2014; Fruchter et al., 2012; Sharkey, 2013; Schubert and Becker, 2010; Taylor et al., 2004). For instance, parents’ social heritage and educational aspiration are central for children’s educational aspiration and success (Buchmann and Dalton, 2002; Dupriez et al., 2012). Especially after-school learning and homework are influenced by parents’ social background (Hagenauer and Oberwimmer, 2019). Trautwein et al. (2006) considered parent characteristics to be a relevant component of homework quality. In addition, significant others, such as teachers or peers at school, or in other contexts, are taken into account. However, these trajectories are not inevitable. Studies have found that school and high-quality teaching are likely to initiate a contextual change from a disadvantageous social background to an education-oriented expedient one (Aikens and Barbarin, 2008; Downey et al., 2004; Temple et al., 2000).

Presence at school might be one means of reducing structural inequality and buffering the effects of social background (Downey et al., 2004). Downey et al. (2004) considered schools as equalizers that may correct for social disadvantages. Apart from the social isolation and lack of peer learning, distance learning is also challenging due to organizational and contextual caveats. Many families’ houses and apartments are not large enough to offer a quiet space for students to learn, and not every family has enough computers or fast-enough Internet access (Fairlie, 2012: 1–2).

Control–value theory of achievement emotions

Summarizing the results above, Pekrun and Stephens (2009) developed a model that explains the relations of learning and achievement with contextual and individual characteristics (see Figure 1). The model proposes that learning and achievement, which massively depend on motivation, problem-solving, and cognitive resources, are related to emotions, especially activating joy and inhibiting anxiety, which in turn are related to certain appraisals such as self-efficacy beliefs. These appraisals are the result of an interaction of environmental factors, including instruction, autonomy support, feedback and sanctions, and achievement expectations. The environmental factors operate within the social contexts of children, that is, mainly at home and at school. This model illustrates the complex preconditions of successful and healthy learning and at the same time demonstrates the challenge of COVID-19 school closures, especially for those children with a low-income social background.

In the context of COVID-19, the influence of the school as a learning environment is assumed to be reduced. Furthermore, Huber and Helm (2020b: 249) related the amount of learning, especially of less-motivated students, partly to control by the teacher. Thus, support from teachers and other caregivers might be a means of regulating learning behavior in less conscientious students. During school closures, the role of the family becomes pronounced in that students are surrounded by the values and attitudes, cultural peculiarities, and specific role images of their families around the clock. Additionally, they depend on the knowledge and skills of their parents and siblings in terms of technical issues and school content and on the time capacities of the respective family members. These resources are explored as “support by the family” in the present study.

Overall, the school closures associated with the COVID-19 pandemic are likely to pose a severe rupture in students’ school life, including: a massive reduction in social contact; limited access to help; few opportunities to learn in group-work settings; likely reduced student–teacher interaction; and a strong correlation of possibilities in academic development with the family’s social, ethnic, and educational background (Steinmayr et al., 2021). These constraints and inequalities might result in students’ poor emotional and motivational constitution.

The present study

In sum, previous research on the relation between different sources of support, as well as other contextual factors, and emotional and motivational consequences has suggested that the school closures during the COVID-19 pandemic pose a risk for the emotional wellbeing and educational success of students in general and students from less-supportive families in particular (Helm et al., 2021). Although the studies by Huber and Helm (2020a, 2020b), Huber et al. (2020), and Helm et al. (2021), provide an overview of a wide range of school-relevant variables from several perspectives, a thorough understanding of emotional and motivational processes, as well as “buffering mechanisms,” requires more faceted information on students’ perception of the crisis and their emotions, motivations, and contextual help structures. Another limitation of Huber and Helm’s (2020a, 2020b) study is that they used only single items to assess students’ perceptions.

Therefore, we build on that knowledge and aim at providing a more insightful perspective on students’ emotional and motivational perceptions regarding distance learning during the COVID-19 pandemic by systematically analyzing the relations of the various preconditions of learning that were suggested by Pekrun and Stephens (2009). We aim at exploring the consequences of the COVID-19 school closures from a student-centered perspective and at revealing possible strategies for buffering the negative consequences of the crisis. Because the transition to adolescence is an especially demanding period, when isolation from peers can be particularly negative for students’ emotional wellbeing and motivational processes, we focus on secondary education, that is, Grades 5–12 with students aged 10–19 years. The self-report questionnaire covered the students’ emotional and motivational consequences as well as contextual conditions, working styles, and different school-based activities, with most concepts measured as scales of multiple items to cover all possible facets.

The following research questions are addressed:

• RQ1: How do secondary school students perceive distance learning regarding emotions and self-efficacy?

• RQ2: How closely does the relation of self-efficacy and emotion in COVID-19 distance learning with contextual circumstances follow the principles of the control–value theory of achievement emotions?

• RQ3: Which environmental factors are relevant in the context of learning during the COVID-19 school closures?

The control–value theory of achievement emotions (Pekrun and Stephens, 2009) served as a framework for the selection of relevant variables in four central areas (see Table 1).

Table 1. Scales of the present study.

Limitations and future research

There are some limitations that need to be taken into account when interpreting the present results and addressing future research. At first, the design of the study would suggest considering a multilevel data analysis approach. However, the ICCs calculated for each variable of interest (Table 1) showed that the variance between subjects within schools was not considerably smaller than the variance across schools (ICC < 0.06), and therefore, a multilevel approach could be neglected in this study. However, the findings should be carefully interpreted, taking into account the sample size and number of classes involved in this study. Since the student perspective on motivational and emotional consequences of COVID-19 is still understudied, explicit hypotheses concerning these questions were not formulated. Thus, while the present study was rather exploratory, it offers a good basis for future studies. Although the sample size was small, the correlational inferences are valid within the given range of variable values. In this regard, beyond the considered classroom realities (such as perceived quality of virtual exchange during digital lessons), future studies should include the organization of distance learning in more detail (e.g., the portion of self-paced learning, students’ daily schedule, the amounts of individual and group work). Additionally, the differential moderating effects of gender, age, and socioeconomic status should be considered. Concerning digital classes, future studies should investigate what methods and strategies lead to teaching quality and are successfully transferred into the digital classroom, and which formats are newly developed in response to the different requirements of distance learning and the emotional, motivational, and outcome-related consequences (Voss and Wittwer, 2020). Furthermore, it would be valuable to explore other achievement-related emotional states (such as resignation and depression, boredom, frustration, hope, or pride). Additionally, beyond the student perspective, it is relevant to include teachers’ perspectives, which would allow for a better understanding of differences in academic outcomes, emotions, and motivations.

As the present data are cross-sectional, and the results are mainly based on correlational analyses that considered only one point in time, future research should include follow-up measurement points, for example, by comparing control conditions (classes at home due to quarantine conditions) to classes with in-person schooling. This methodological setting would allow access to data in a quasi-experimental setting (DeMathews et al., 2020) with a control group and a repeated measurement in order to make valid inferences on causal relations. This also allows controlling for the general effects of the crisis, that is, fear of infection or stress due to parents’ economic problems. Thus, further research is needed (DeMathews et al., 2020) to better understand educational processes in a pandemic, covering learning outcomes, motivations, and emotions. At the moment, the authors are conducting another study that allows the sample to be extended to n = 500+ students, which permits (under control of whether the lockdown was in 2020 or 2021) the application of structural equation modeling. Additionally, a teacher questionnaire will offer further insights into student emotions, motivation, and learning outcomes in combination with teaching practice.