Abstract
Self-regulated learning (SRL) provides the foundation for building sustainable knowledge and is therefore important for schools, classrooms, and lifelong learning in general. Especially in vocational education and training, the concept of SRL remains fundamental as it relates to preparing future employees. However, further research is needed on how vocational students situationally regulate their learning process and the extent to which this may be related to a dispositional change in their SRL. In this study, we analyzed longitudinal questionnaire data from 159 students who attended either SRL-conducive or regular vocational classes. We refer to Perry and colleagues' (2018) framework of an SRL-conducive learning environment, which focuses on (meta)cognitive, motivational, and emotional aspects of learning. Using multilevel analysis, we found differences in the development of (meta)cognitive components of learning, whereas no clear differences could be identified for motivational and emotional components. The results support the assumption that process analyses can be used to draw a more differentiated picture of SRL in vocational schools. Moreover, indirect approaches to promoting SRL should be designed to include all SRL-relevant aspects.
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Introduction
Self-regulated learning (SRL) can be seen as a complex process including (meta)cognitive, motivational, emotional, and behavioral aspects, which also relates to social processes (Järvelä & Bannert, 2021). As such, SRL is understood as the autonomous, self-directed behavior of individuals who actively monitor and regulate goal-oriented actions to improve their knowledge and skills (Paris & Paris, 2001). SRL is relevant at all levels of education and is a prerequisite for lifelong learning (Alheit & Dausien, 2002; Baumeister, 2005; Bolhuis, 2003). In this context, vocational schools can be seen as important spaces preparing learners for lifelong learning in the workplace (Deissinger & Gonon, 2021). Therefore, the investigation of SRL in vocational schools is highly relevant from both a scientific and a practical perspective.
SRL has already been studied multiple times as an educational concept and has been excellently conceptualized from a theoretical point of view (see Panadero, 2017 for an overview). Empirically, a significant impact of SRL on academic performance (e.g., Cleary et al., 2020; Dignath & Büttner, 2008; Zimmerman & Bandura, 1994), well-being (e.g., Davis & Hadwin, 2021; Park et al., 2012; Zimmerman & Martinez-Pons, 1990), and the development of generic competencies (e.g., Artelt et al., 2001; Weinstein & Hume, 1998; Wolters, 2011) has been demonstrated.
Despite the extant literature, several aspects of SRL remain unexplored. An ongoing major issue is the question of student learning trajectories (Winne, 2019). Promising approaches have been developed to determine what exactly constitutes the learning trajectory (methodologically: e.g., Schmitz et al., 2012; content-related: e.g., Pintrich, 2000; Zimmerman, 2000). However, considering SRL as a process also raises the question of how individuals interact with their learning environment. Existing suggestions have focused on the evolution of learners’ SRL and change over time from a more externally determined to a more self-determined learning environment (for a related discussion, see Hmelo-Silver et al., 2007; Kirschner et al., 2006). Nevertheless, the role of the learning context can be seen as underrepresented in many studies on SRL and, therefore, not sufficiently investigated (Perry & Rahim, 2011). To address this issue, the goal of this paper is twofold: (1) to gain more detailed insight into students’ SRL trajectories in vocational schools and, in this context, (2) to demonstrate the importance of the learning environment for SRL. Of central importance are the constitutive elements for promoting SRL, rather than the outcome of SRL itself. In this paper, the results of an intervention focusing on key classroom features that foster SRL will be presented and discussed. Key classroom features represent a set of characteristics in the classroom context that emphasizes SRL, referring to student activities as much as teacher activities (Perry et al., 2018).
Self-Regulated-Learning in Vocational Schools
Today, the promotion of SRL plays an important role as many companies expect their employees and learners to be self-directed and responsible learners (Dall'Alba, 2009; Ertl & Sloane, 2004; Kirschner & Stoyanov, 2018). For students to acquire these competencies, professional knowledge should also be developed through active and self-directed learning processes (Lang & Pätzold, 2006; Metzger et al., 2005). In this context, work-related SRL is a central factor in the professionalization process of aspiring employees. However, existing research indicates that the task orientation of vocational learners rarely takes place during hands-on simulations. Thus, the use of different (meta)cognitive strategies (e.g., time management, self-monitoring, or goal-setting) is, in many cases, not considered by vocational students (Khaled et al., 2015). Jossberger and colleagues (Jossberger et al., 2020), for their part, were able to show that, despite being able to effectively plan and monitor their self-regulatory activities, students are often unable to carry out their planned activities successfully.
At the same time, the promotion of SRL during vocational school lessons can be said to be very important as well, especially because it can be assumed that the number of learning opportunities in training companies will decrease in the coming years (e.g., due to megatrends) and that schools will play an increasingly important role in providing vocational education (OECD, 2021). Accordingly, a sustainable vocational education does not focus only on the acquisition of occupational skills but also on the development of generic competencies. The importance of vocational schools for the vocational training of learners is particularly evident in dual vocational training systems such as those found in Switzerland, Germany, Denmark, the Netherlands, or Austria. For example, a Swiss apprentice should not only learn to become a skilled worker for the labor market but also be able to continue learning at a higher level of the education system later on (Gonon, 2017). In a recent study, Kirschner and Stoyanov (2018) were able to show, based on a survey of experts, that SRL continues to play an important role for vocational learners in training because it forms the basis for lifelong learning. Correspondingly, the experts who were interviewed considered the promotion of cognitive and metacognitive strategies just as fundamental as learning in authentic learning situations. However, the learning context must be modified and adapted so that learners can develop their SRL competencies appropriately (Kirschner & Stoyanov, 2018). This shows that the development of job-specific competencies continues to play an important role, particularly because these are relevant for the transition from school to work. At the same time, successful SRL is also important for achieving and, more importantly, retaining employment (Forster-Heinzer et al., 2016; Hanushek et al., 2017).
SRL can be considered an essential prerequisite for lifelong learning, and the design of learning environments plays an exceptionally important role in effective SRL. In this context, instructional designs that link to learners’ competence development and shift over time from more externally to more self-directed instructions have been proposed several times (Dubs, 2015). The promotion of the different components of SRL (cognition, metacognition, emotion, and motivation) plays an equally important part. For example, in a series of studies on vocational education in Germany, Sembill and colleagues demonstrated that SRL-oriented instruction leads to higher learning motivation and problem-solving skills among vocational students while they develop the same degree of expert knowledge (Sembill, 1999; Sembill et al., 2001). In addition, SRL-conducive learning environments have been shown to lead to deeper interconnectedness across SRL phases (forethought, performance, and self-reflection), as well as significantly more questions from learners about the learning content and better feedback from teachers (Sembill, 2004). Accordingly, teachers should provide many opportunities for learners to participate in the classroom given that a greater experience of autonomy has a positive impact on vocational students’ motivation to learn and SRL (Sembill et al., 2001; Van Grinsven, 2003). This illustrates clearly for vocational education what is central at other levels of education as well: purposeful feedback and various forms of assessment of learning processes represent a fundamental prerequisite for SRL when they are intensively linked to the learning environment (Butler & Winne, 1995). However, in vocational education, these elements are only partially used (Rozendaal, 2002), resulting in limited recourse to metacognitive regulation strategies (van Velzen, 2004; van Velzen & Tillema, 2004).
Overall, vocational school instruction must be seen as a key component of the quality of training for vocational students, which should be subject to constant further development due to its lasting effects on the learning and action patterns of the students (Höpfer, 2017). This requires, inter alia, instructional measures in vocational schools through which young people can further develop their SRL competencies (Frey & Terhart, 2010; Sachs et al., 2016).
Self-Regulated Learning and Learning Environment
Studies on the promotion of SRL have increasingly focused on the development of individuals based on the design of their learning environment, with fundamental importance given to teachers (e.g., Dignath & Büttner, 2008; Dignath et al., 2008; Kramarski, 2018; Kramarski et al., 2013; Waytens et al., 2002). This development is particularly necessary because it has been pointed out that not enough attention is paid to the interaction between the individual and the learning environment (Martin, 2007; McCaslin & Good, 1996; Perry & Rahim, 2011). In a recent review, various approaches to promote SRL have been provided (Dignath & Veenman, 2020). Based on this overview, direct promotion refers to teachers’ instruction of regulation strategies—further divided into explicit and implicit strategy instruction—, while indirect promotion refers to the design of a learning environment that fosters SRL. Direct strategy instruction is gradual in terms of its explicitness (e.g., Dignath & Büttner, 2008). Brown and colleagues (Brown et al., 1981) differentiate three different levels of direct strategy instruction in this regard. Whereas blind training refers only to the instruction of strategies without further contextual information on how to use them, informed training also provides students with information on the benefit of the given strategy. Self-control training combines strategies with explicit instructions on when, how, and where to use the provided strategies during the learning process (Dignath & Veenman, 2020). At this point, it becomes clear that teacher expertise is central to both direct and indirect promotion of SRL. According to Dignath and Veenman (2020), significant differences exist between teachers in terms of promoting SRL. For example, regulation strategies are prompted differently and often taught implicitly rather than explicitly (i.e., through verbalization). Furthermore, there is a positive relation between teachers' instruction of SRL strategies and students' use of them, while teachers' SRL beliefs are positively correlated to their SRL practice (Dignath & Veenman, 2020). Further research indicates that teacher self-regulation (e.g., Kramarski, 2008; Kramarski & Kohen, 2017), self-efficacy (e.g., De Smul et al., 2018; Dignath, 2016), motivation (e.g., Karlen et al., 2020) and knowledge of SRL (e.g., Spruce & Bol, 2015) are important predictors of learners' successful SRL. In this paper, the focus lies exclusively on the learning environment, that is, the indirect promotion of learning environments.
The relevance of learning environments for learning in general—and SRL in particular—has been widely demonstrated (Biggs, 1989, 1993; Boekaerts, 1992, 1996; De Corte, 1996; Entwistle, 1991; Vermunt, 1995; Vermunt & Donche, 2017; Zimmerman, 1989). In the school context, various instructional approaches have been developed since the 1980s, such as cognitive apprenticeship (Collins et al., 1989), situated learning (Greeno, 2006; Resnick, 1987), and problem-based learning (Barrows & Tamblyn, 1980). To promote SRL in school, some models explicitly include the learning environment. For example, the CLIA model (De Corte et al., 2004) emphasizes that the various components of competencies (Competence), characteristics of effective learning processes (Learning), principles and methods for designing a learning environment (Intervention), and various forms of assessment (Assessment) must be aligned to promote self-directed learning in students (De Corte et al., 2004, p. 368). Effectiveness studies revealed that students in SRL-supportive environments demonstrate more sophisticated mathematical problem-solving skills and have more positive attitudes and beliefs regarding mathematics (De Corte et al., 2004). In addition, more intense co-regulation between individual students (De Corte, 2012), higher achievement, and increased use of metacognitive regulation strategies have been demonstrated (De Corte, 2016; Masui & De Corte, 2005).
In line with this model, Perry and colleagues (Perry, 1998, 2013; Perry et al., 2018) developed a framework with different characteristics of the classroom context that emphasizes SRL. They summarize classroom characteristics in four macrocategories (“SRL-Supportive Structures,” “Student Influence and Autonomy,” “Supporting, Scaffolding, Co-Regulation,” and “Functions as a Community”), which, in turn, are subdivided into several microcategories that reflect the types of practices that teachers use. “SRL-Supportive Structures” are defined as (1) assigning meaningful tasks that are linked to clear instructions and expectations as well as (2) providing students with enough opportunities to participate in classroom activities. Particularly well-designed tasks lead to deeper information processing, more efficient use of regulatory strategies, and higher self-efficacy of the students (Perry, 2013). However, complex learning situations also require systematic and targeted support for learners (Reeve & Halusic, 2009). In this respect, Perry and colleagues (Perry et al., 2020a) were able to demonstrate that classrooms in which SRL is highly valued also provide structural support for SRL and the students’ autonomy. The macrocategory “Student Influence and Autonomy” refers to the availability of opportunities to co-design lessons and control one’s own learning, thus promoting student influence and autonomy. The selection, modification, and alteration of tasks, as well as various forms of self-assessment, are crucial to this process. Students who learn in autonomy-enhancing learning environments experience more positive emotions about their learning process, seek more challenging tasks (Su & Reeve, 2011), and show greater engagement, less amotivation (Cheon & Reeve, 2015), and increased autonomous motivation (De Naeghel et al., 2016). “Supporting, Scaffolding, Co-Regulation” and “Creating a Community of Learners,” as the third and fourth macrocategories, relate to the interactions between teachers and students as well as among the students themselves. Powerful learning environments are characterized by model learning, demonstration, metacognitive and motivational dialogue, and mutual and differentiated feedback (Perry et al., 2020b). In this regard, the importance of social interactions for SRL-enabling learning environments, which is discussed extensively in the context of socially shared regulation, is emphasized (Hadwin et al., 2018). The importance of the social context has been demonstrated several times, for example, regarding scaffolding through teachers and peers (Leeuwen & Janssen, 2019; Molenaar et al., 2014; Salonen et al., 2005; Winstone et al., 2016) or collaborative learning (McCaslin & Vriesema, 2018; Panadero et al., 2015; Vriesema & McCaslin, 2020). Thereby, positive effects regarding shared goal orientation (Isohätälä et al., 2017), performance (Janssen et al., 2012), and the quality of regulatory processes in groups (De Backer et al., 2015) can be distinguished. Furthermore, the relevance of group-regulated learning for productive cognitive interaction (Khosa & Volet, 2014), supportive socio-emotional interaction (Rogat & Adams- Wiggins, 2015; Rogat & Linnenbrink- Garcia, 2011), and even their interplay (Barron, 2003; Sinha et al., 2015) was demonstrated. So, when a classroom takes on a positive climate, characterized by shared knowledge, respectful communication, acknowledgment of individuality, and mutual support, the classroom functions as a community. It was found that establishing a community of learners is conducive to SRL because learners seek more help and support from each other more intensively overall (Perry & Drummond, 2002). Together, these categories summarize characteristics of the classroom context that emphasize students’ SRL (Perry et al., 2015) and serve as the theoretical foundation in this study (Perry et al., 2018).
However, it must also be noted that the manifestation of SRL depends not only on the learning context (Winne & Hadwin, 1998) but also on how regulation evolves over time (McCardle & Hadwin, 2015). On the one hand, there is the view that stable personality characteristics are decisive; on the other hand, there is the conviction that, above all, the current situation is crucial to appropriately analyze SRL. Therefore, time is an important component for the understanding of SRL (Patrick & Middleton, 2002).
Self-Regulated Learning as a Temporal Process
Whereas in the early stages of its theoretical conceptualization, SRL was primarily defined as a disposition and empirical studies have measured it as a dispositional trait (Boekaerts & Corno, 2005; McCardle & Hadwin, 2015; Winne, 2019; Winne & Perry, 2000), contemporary views understand SRL as a dynamic and repetitive process in which the (meta)cognitive, emotional, and motivational components of learning (seen as states) unfold over time. Thus, the effective self-regulation of learning is fundamentally dynamic in various phases of learning and can be flexibly modified to suit the learning environment (and associated requirements) (Greene et al., 2021). If regulation is understood as action and/or behavior that develops over time, SRL can be seen as a series of events during a learning task, which should be captured and analyzed in terms of its process (McCardle & Hadwin, 2015; Winne, 2019). Learners who effectively self-regulate their learning set learning goals and continuously adjust their efforts by monitoring the achievement of their learning goal (Bernacki, 2018; Greene & Azevedo, 2010). This illustrates the relevance of metacognitive processes because SRL can be measured via concrete events in class, for instance, when students solve tasks or edit texts (Greene et al., 2021). In this context, different types of self-regulation can be identified and approached as different metacognitive processes, such as task understanding, elaboration, evaluation, and monitoring (McCardle & Hadwin, 2015). In their study, McCardle and Hadwin (2015) demonstrated that metacognitive awareness changes over time and has a significant influence on how learners control and organize their learning process. Nonetheless, other components of SRL also evolve. For example, Moos and Azevedo (2008) showed that not only (meta)cognitive components but also emotional and motivational components fluctuate during learning. The results of their study indicate that learners develop more sophisticated strategies over time to solve tasks, along with an increasing interest in the tasks themselves.
Viewing SRL as a dynamic process rather than a disposition has led to a great deal of discussion in recent years, in which the measurement of SRL processes is still considered a major challenge (Veenman et al., 2006; Winne, 2010). The need to measure SRL as a process, via so-called online measures, has been expressed several times (Molenaar & Järvelä, 2014; Winne & Perry, 2000; Zimmerman, 2008). Some innovative instruments such as think-aloud protocols (Sonnenberg & Bannert, 2019), log files (Bernacki, 2018), data mining (Lajoie et al., 2021), or electrodermal activity (Malmberg et al., 2019) have been developed in recent years. For example, Molenaar et al. (2021) have depicted student learning progress through moment-by-moment learning curves, thus providing deeper insights into when students need additional learning support.
Based on the foregoing, it is clear that SRL can be measured at different levels of granularity. Granularity refers to the level of detail at which self-regulatory processes are assessed (Azevedo, 2009). The decision to measure SRL finely or coarsely depends largely on the research question. Coarse-grained measures aim to capture the global process phases of learning (Rovers et al., 2019). Fine-grained measurement, in contrast, concerns the micro-level of learners’ SRL processes. An example is Schmitz and Wiese’s (2006) study, which examined the development of students’ learning over several days. Learning diaries were used for five weeks to track the development of self-regulatory behavior. Similarly, McCardle and Hadwin (2015) presented different types of self-regulation measured over 11 weeks by combining qualitative and quantitative methods. Several other studies have attempted even more fine-grained analysis of the SRL process. For instance, hypermedia learning sessions have revealed the relationship between cognitive and metacognitive processes during task solving (Azevedo et al., 2010). In a 60-min experiment (carried out in 10-min segments), it was shown that during task solving, learning strategies were used far more often (76.67%) than metacognitive strategies such as planning (4.80%) or monitoring (15.56%; Azevedo et al., 2010, p. 216). In studies on the dynamics of SRL, units of time are conceptualized differently, creating an artificial division. That is, time is segmented in various ways and can refer, for example, to individual lessons or entire teaching units over several weeks. It is therefore fundamental to link the segmentation of defined periods to clear guidelines and justify them theoretically (Molenaar, 2014). Although new and innovative methods have been developed in recent years for the measurement of SRL, there is still a lack of studies examining SRL as a process over time (Järvelä & Bannert, 2021). This can be noted in particular for vocational education and training, as “little is known about vocational students’ learning and their strategy use in real time” (Jossberger et al., 2020, p. 135).
The Present Study
Vocational education provides a notably promising environment for the promotion of the development of SRL (OECD, 2021). Fostering SRL may require reforming the way that learning is organized and implemented in vocational schools. Physical spaces and new or adapted teaching materials are key factors in this process (Musset, 2019). At the same time, there is the question of whether students become more self-directed in dealing with different learning situations as they grow in age and experience (Boekaerts, 1996). Therefore, in this study, we designed an intervention that possesses some essential features of an SRL-conducive learning environment (for details, see “Intervention” section).
The overall aims of the present study were twofold. Our first goal was to investigate the development of SRL components over time. Due to the complexity of SRL, Pintrich’s model (2004) focuses on three areas of metacognition, motivation, and emotions, which remain very broad constructs. As such, the area of (meta)cognitive strategies is specified by the strategies of repetition, organization, elaboration, planning, monitoring, regulation, effort, time management, learning with fellow students, and learning environment. Motivational regulation is assessed by the two poles (intrinsic and external regulation) of Deci and Ryan’s (2002) continuum structure to reflect motivation within SRL. Finally, with regard to emotions, two common emotions (enjoyment and boredom) are examined that have different valences (positive and negative) and activation (activating and deactivating; Pekrun, 2006). All of these components are explicitly reflected in the framework of Perry et al. (2018), which forms the basis for our intervention.
Our second goal was to analyze whether an SRL-promoting learning environment in vocational schools may have an impact on students’ development of these SRL components in comparison with regular instruction. To date, too little is known about the process of SRL (Järvelä & Bannert, 2021; Winne, 2019). To better understand how SRL unfolds over time, it is therefore necessary to relate SRL as static competence (dispositional development) and SRL as strategic adaptation (situational development) to one another (McCardle & Hadwin, 2015). Thus, based on the different levels of granularity of SRL processes (Azevedo, 2009), we addressed this desideratum by analyzing the development of SRL components at two different measurement levels: the macro level to capture potential changes in students’ dispositions over a school year (coarse grained), and the meso level to examine weekly trends in SRL components over a semester (fine grained). The following research questions and hypotheses were investigated regarding the dispositional change and situational development of vocational students’ SRL components. To examine changes in students’ disposition in SRL components, our first research question is as follows:
Do dispositional changes in the use of (meta)cognitive strategies, perceived motivation, and emotions of students in treatment classes differ from those of students in control classes? (RQ1)
Based on the encouraging results of existing research (e.g., Sembill et al., 2007; Van Grinsven & Tillema, 2006), we hypothesized that students in the treatment classes increase their use of cognitive and metacognitive strategies compared to students in the control classes. Previous research demonstrated that the satisfaction of students’ basic psychological needs (need for autonomy, competence, and relatedness) predicts positive emotions and contributes to intrinsic motivation (De Naeghel et al., 2016; Isen & Reeve, 2005; Ryan & Deci, 2020). Thus, because the SRL-setting is also assumed to better fulfill students’ basic psychological needs (Perry et al., 2018), we assumed that students in the treatment classes exhibit an increase in intrinsic motivation and positive emotions. Several studies revealed that basic need satisfaction is associated with higher internalization of externally motivated activities and a decrease in negative emotions (Skinner et al., 2017; Vansteenkiste et al., 2020; Yu et al., 2016). Therefore, we expect a decrease in extrinsic motivation and negative emotions compared to the control classes. For the control classes, we did not expect any changes in the development of the SRL components over a school year. To gain deeper insights into the development of learners’ SRL, our second research question is as follows:
Does the situational development in the students’ use of (meta)cognitive strategies, perceived motivation, and emotions in treatment classes differ from those of students in control classes? (RQ2)
In line with the existing research (Sembill et al., 2008; Wild, 2001; Wild & Krapp, 1996), we hypothesized increasing linear development in the use of (meta)cognitive strategies, intrinsic motivation, and positive emotions in the treatment classes. A linear development of SRL (e.g., Leidinger & Perels, 2012; Schmitz & Wiese, 2006) or positive emotions (Goetz et al., 2013) could be demonstrated over a similar period of time. For motivation and engagement, Martin and colleagues (Martin et al., 2015) were able to identify a linear development between weeks, even if non-linear developments between single days have been detected as well.
At the same time, we expected a decrease in extrinsic motivation and negative emotions in the treatment classes. For the control classes, we did not expect any changes in the development of the SRL components over a semester.
Method
Participants and Data Collection
The purpose of the present study is to evaluate an SRL-supportive instructional setting within a quasi-experimental study. The quantitative sample consisted of 159 commercial apprentices in seven classes, with a mean age of 16.64 years (SD = 2.23 years) at the beginning of their first school year at vocational school “Wirtschafts- und Kaderschule Bern” in “ Bern, Switzerland.” Learners were assigned to the treatment classes on a voluntary basis, i.e. the vocational learners were informed by the school about the content of the SRL setting before they could decide for themselves whether they wanted to participate.
Students in a vocational school in “ Switzerland” attend part-time classes two days a week and, on the other three days, are with their apprenticeship companies and attend no classes. Our intervention study only addresses learning in school and there is no transfer to the apprenticeship companies. Of these 159 students, 76 were male (47.8%) and 83 were female (52.2%). Three of the seven classes were intervention classes (n = 68; 42.8%), and four were control classes (n = 91; 57.2%). At the beginning (August) and end (June) of the school year, the students completed an extended online self-report questionnaire (Fig. 1). Between these long questionnaires, the students could participate in weekly short questionnaires during the school year. A total of 119 students (n = 46; 38.7% intervention vs. n = 73; 61.3% control) downloaded an application developed for this study onto their smartphones and participated in the weekly short questionnaire via this app. It took the students approximately one to two minutes to complete the short questionnaire. Once a week, the students received a push notification on their smartphones informing them that a new questionnaire was available. If they did not complete the questionnaire, the students received five more push notifications over the following days. To ensure that the results were not biased by a specific subject or day, a semi-randomized time interval was assigned for the data collection (Himmelstein et al., 2019). Data collection was tested with pilot studies. Before data collection, parental and student permission was obtained and the Ethics Committee of the Faculty of Human Science of the “University of Bern ” classified the study as safe/uncritical.
Intervention
Our intervention approach refers to the framework model developed by Nancy Perry and colleagues, “Classroom Practices that Support Self-Regulated Learning” (Perry et al., 2018, 2020a). Based on this framework, we created a learning environment with different instructional elements to emphasize students’ SRL in a participatory approach with a vocational school (Perry et al., 2015). Table 1 illustrates which elements of the “Classroom Practices that Support Self-Regulated Learning” framework were included in our intervention.
In our intervention, a classroom structure was provided that allowed for autonomous learning (“SRL-Supportive Structures”). Most of the time, the students worked independently on individual assignments. For this purpose, they received a so-called “learning job” every four weeks. This document included all tasks to be completed, exam dates, and optional self-tests. At the same time, the learners received four to five 20-min input sessions during school hours, in which the technical content of the subjects Business and Society, Information, Communication, and Administration, German, English, and French were taught. During the school days, two teachers of different subjects were always available to answer students’ questions. In addition to the teachers’ input, exam times were also scheduled. Each week, an exam in one subject was held on the second day of school from 09:00 to 10:00. Students also had access to all documents and materials at any time via an online platform.
Regarding “Student Influence and Autonomy,” students in our intervention had the opportunity to design their learning process largely on their own. Based on their learning jobs, they created individual weekly plans and independently decided how much time they would spend on each subject at school and at home. The weekly plans served to control and assess SRL strategies and, thus, represented the central document to record the development of the vocational learners’ learning competencies. The main task of the students was to plan the processing of the learning assignments (in terms of time and content). With regard to self-assessment, they had the opportunity to check their learning progress via self-tests. The self-tests were formative and were coordinated with the teachers’ input.
Finally, “Supporting, Scaffolding, Co-Regulation” and “Creating a Community of Learners” relate to the interactions between teachers and students as well as among the students. In our intervention, these aspects were, inter alia, influenced by the coaching sessions. Each student was supported by a personal coach. At these coaching meetings, held every four weeks, in addition to discussing the self-tests and individual planning, various aspects of SRL such as applied learning strategies and time management were discussed, and individual goals were set. In addition, all tasks in the learning job could be completed in a chosen social form (e.g., partner or group work) to enable as much mutual support and co-regulation as possible.
Overall, the implementation of the intervention supported several macrocategories of Perry’s heuristic on “Classroom Practices that Support Self-Regulated Learning” and, thus, based on theoretical considerations, aimed to promote SRL among vocational students. The following approaches were used to evaluate implementation. Students’ weekly plans were evaluated by coaches and provided to the research team as a manipulation check. Additionally, the records of the coaching sessions were submitted to verify implementation. Finally, implementation of the inputs, learning jobs, weekly plans, self-tests, coaching sessions, and flexibility regarding social forms were verified through the interviews.
Besides the intervention setting, students in the control group attended regular classes with lessons in all subjects for 45 min each on both school days. After each lesson, they changed classrooms and teachers. Thus, the instructional design was primarily the responsibility of teachers and varied between subjects, and all instructional elements proposed to the students in the intervention setting (weekly plans, learning jobs, self tests) were not carried out in the control classes. In addition, there was no coaching or systematic individual mentoring in the regular classes.
Measurement
As mentioned above, two types of measures were used for this study: a) long questionnaires at the beginning and end of the school year, and b) weekly short questionnaires. The long questionnaire consisted of 14 scales addressing the different components of SRL (Table 2). (Meta)cognitive strategies and resource management were measured using ten subscales: repetition, organization, elaboration, planning, monitoring, regulation, effort, time management, learning with fellow students, and learning environment of the “Inventory for the Measurement of Learning Strategies in Academic Studies” (LIST; Wild & Schiefele, 1994). Motivational components were measured using the intrinsic and extrinsic motivation components of the “German Self-Regulation Questionnaire” (Müller et al., 2007). Finally, for the emotional components, the two scales of enjoyment and boredom based on the German version of the “Achievement Emotion Questionnaire” (AEQ; Pekrun et al., 2005) were used.
The weekly short questionnaire consisted of one item of the scales of repetition, organization, elaboration, planning, monitoring, regulation, effort, time management, learning with fellow students, learning environment, intrinsic motivation, extrinsic motivation, enjoyment, as well as boredom, and was used over 14 weeks (one semester). Single-item measures have been reported to have adequate psychometric properties and represent a suitable alternative for long scales when those are not applicable (e.g., for frequent measures; Gogol et al., 2014). All single items were adopted from the long questionnaire (Table 2). For this purpose, we selected the items that best represented each corresponding scale (Goetz et al., 2013, p.387; Schmitz & Wiese, 2006). In the short questionnaire, all items were rated on a 4-point Likert scale ranging from 1 (not true) to 4 (true).
Data Analysis
First, we wanted to investigate dispositional differences in the change of SRL components between students in the treatment and control classes. Linear mixed models have the advantage of allowing for the estimation of interindividual variability in intraindividual patterns of change over time (Raudenbush & Bryk, 2002). This allows estimation of a mean trajectory for the two groups, as well as a subject-specific difference for each individual (McNeish & Matta, 2018). We ran linear mixed-effect models based on data from the long questionnaires (start and end of the school year). This trait data corresponds to a nested data structure in which measures (Level 1; N = 318) are nested within persons (Level 2; N = 159). The number of missing values on the dependent variables ranged between 17% and 42.8%. This occurred as a result of the voluntary nature of participation in the study, absences due to illness during the survey period, and transfers from/to another school or profile within the school during the school year. Missing data in the long questionnaire were assessed with multiple imputations by a chained equation – package mice (van Buuren & Groothuis-Oudshoorn, 2011, version 3.13.0, number of imputed datasets m = 25 and iteration maxit = 25). Linear mixed models were run using the lme4 package (version 1.1.27; Bates et al., 2015).
Second, to investigate the situational differences in the development of SRL components, we used data from the weekly short questionnaires (14 measurement points). This state data represents a nested data structure in which measures (Level 1; N = 1666) are nested within persons (Level 2; N = 119). The Table 6 in Appendix A shows variance components for all 14 variables. Due to the low group variances, a longitudinal two-level model was retained in our analyses (Level 1time and Level 2person). Because of our intervention, we assumed a continuous development across time and, therefore, linear mixed-effect models were run using the nlme package (version 3.1.152; Pinheiro et al., 2021). Missing data were estimated using maximum likelihood estimates. All analyses were conducted in R (R Core Team, 2019).
Results
Descriptive statistics and correlations for all the variables of the long questionnaire are presented in Table 3. In the first measurement, the two groups only differed in planning (p = 0.05). All other variables demonstrated no significant difference at t0. As expected, (meta)cognitive strategies and resource management were significantly correlated within measurement points. In addition, they usually correlated significantly with intrinsic motivation and enjoyment within measurement points. Interestingly, extrinsic motivation at the first measurement point was only significantly correlated with enjoyment, while significant correlations were found with (meta)cognitive strategies and resource management at the second measurement point. Boredom was significantly correlated with cognitive strategies, effort, learning environment, and enjoyment within the measurement points. All significant correlations showed the expected direction.
Dispositional Change
To examine differences in the dispositional change of SRL components among students in treatment and control classes, separate linear mixed models were run from the long questionnaires to determine whether there was an interaction between the treatment and time (one school year).
The results revealed a significant interaction effect of time and the treatment in elaboration (b = -0.34, t(415.557) = -1.96, p = 0.05), planning (b = -0.48, t(182.251) = -2.37, p = 0.02), and learning with fellow students (b = -0.35, t(519.623) = -1.63, p = 0.10). Separate multilevel models revealed that time significantly predicted elaboration in the control group (b = -0.25, t(296.201) = -2.08, p = 0.04), whereas it did not in the treatment group (b = 0.10, t(432.930) = 0.74, p = 0.46). The interaction effect reflects the difference in slopes for time as a predictor of elaboration, meaning that elaboration in the students’ learning process decreased between the two measurement points in the control group, while it increased in the treatment group, although not significantly. In addition, separate multilevel models revealed that time significantly predicted planning in the treatment group (b = 0.34, t(453.040) = 2.53, p = 0.01) but not in the control group (b = -0.13, t(111.667) = -0.92, p = 0.36). The interaction effect, therefore, reflects the difference in slopes for time as a predictor of planning, such that the treatment group increased planning in their learning process between the two measurement points, while the control group lowered it, although not significantly. Separate multilevel models revealed that time did not significantly predict learning with fellow students in either the treatment (b = 0.18, t(157.227) = 1.01, p = 0.31) or the control group (b = -0.17, t(178.868) = -1.06, p = 0.29). Therefore, the interaction merely reflected the significant trend of the two groups as a whole. All other variables showed no significant interaction effects of time and the treatment (Table 4).
To examine within-group differences over time, separate multilevel models were run. They revealed a significant main effect of time on regulation in the control group (b = -0.49, t(101.878) = -3.13, p = 0.002), whereas in the treatment group, time did not significantly predict regulation (b = -0.13, t(138.968) = -0.83, p = 0.41). Regarding time management, separate multilevel models revealed a significant main effect of time in the treatment group (b = 0.34, t(214.284) = 1.98, p = 0.05, as well as in the control group (b = 0.51, t(183.413) = 2.59, p = 0.01). Therefore, both groups significantly increased their time management between the two measurement points. All other variables showed no significant effect of time in either the treatment or the control group.
Situational Development
To investigate whether the situational development of students’ SRL components in treatment classes differed from that of control classes over 14 measurement points, separate linear mixed models were run to determine whether there was an interaction between treatment and time. Significant effects are described below, and —non-significant effects are reported in Table 5.
The results revealed a significant interaction effect of time and treatment in repetition (b = -0.03, t(407) = -1.74, p = 0.08), planning (b = -0.04, t(406) = -2.28, p = 0.05), monitoring (b = -0.03, t(408) = -1.67, p = 0.08), regulation (b = -0.03, t(408) = -1.93, p = 0.02), and structuring an appropriate learning environment (b = -0.04, t(409) = -2.73, p = 0.007). Separate multilevel models showed that time significantly positively predicted repetition in the treatment group (b = 0.03, t(206) = 2.51, p = 0.02), while no significant effect was found in the control group (b = -0.001, t(201) = -0.09, p = 0.92). Therefore, the interaction effect reflected the difference in slopes for time as a predictor of repetition. Repetition increased over the 14 weeks in the treatment group while remaining stable in the control group. Separate multilevel models indicated that planning was significantly negatively predicted by time in the control group (b = -0.02, t(200) = 1.98, p = 0.05). At the same time, it was not significantly predicted by time in the treatment group (b = 0.02, t(206) = -1.27, p = 0.20). The interaction effect reflected the difference in slopes for time as a predictor of planning, whereby planning decreased over the 14 weeks in the control group. Planning increased slightly in the treatment group, albeit not significantly (Fig. 2). In terms of monitoring, a separate multilevel model revealed that time was not a significant predictor for students in the treatment (b = 0.01, t(206) = 1.18, p = 0.24) or in the control group (b = -0.02, t(201) = -1.23, p = 0.22). Therefore, the interaction merely reflected the significant trend of the two groups as a whole. Regarding regulation, separate multilevel models revealed that time significantly positively predicted regulation in the treatment group (b = 0.02, t(207) = 1.98, p = 0.05), while no significant effect was found in the control group (b = -0.01, t(201) = -0.69, p = 0.49). This indicates that students in the treatment group significantly increased their regulation over time, whereas regulation remained stable in the control group. Finally, separate multilevel models showed that structuring an appropriate learning environment was significantly negatively predicted by time in the control group (b = -0.04, t(201) = -2.86, p = 0.005) but not in the treatment group (b = 0.01, t(208) = 0.52, p = 0.60). This indicates that the appropriate structuring of the learning environment by the learners in the control group decreased significantly over the 14 weeks but remained stable in the treatment group. All other variables showed no significant interaction effects of time and the treatment.
In addition, separate multilevel models brought to light a significant main effect of time on time management, learning with fellow students, and enjoyment. In terms of time management, the main effects of time were found for the treatment group (b = 0.02, t(208) = 1.70, p = 0.09), whereas in the control group, time did not significantly predict time management (b = -0.006, t(201) = -0.49, p = 0.62). A main effect of time on students’ learning with fellow students was found for those in the treatment group (b = 0.02, t(208) = 1.70, p = 0.09). For students in the control group, learning with others was not significantly predicted by time (b = -0.006, t(201) = -0.49, p = 0.76). Finally, a significant main effect of time was identified for enjoyment (Fig. 2): in the treatment group, enjoyment was significantly positively predicted by time (b = 0.02, t(205) = 1.87, p = 0.06), while no significant effect was found in the control group (b = 0.003, t(201) = 0.30, p = 0.76). These main effects indicate that students in the treatment group showed a significant increase in time management, learning with fellow students, and enjoyment. However, the effect cannot be clearly attributed to the intervention.
Discussion
Today, understanding SRL as a process in its context is one of the key challenges in SRL research (Järvelä & Bannert, 2021; Winne, 2019). In this study, we analyzed questionnaire data from vocational students during their first year of study to investigate the development of the (meta)cognitive, emotional, and motivational components of learning over time. Moreover, we were interested in whether an intervention setting that aimed at fostering SRL in vocational education may change these developmental trends. To gain deeper insights, we investigated the dispositional change (RQ1) as well as the situational development (RQ2) of SRL components and looked for differences between the treatment and control groups.
SRL as a Temporal Process
We found differences in dispositional changes in (meta)cognitive strategies like planning and elaboration within and between the two studied groups. There was also evidence of a situational development of (meta)cognitive variables like repetition, planning, and monitoring. As suggested, in the treatment group, the students’ disposition regarding planning increased significantly (Perry, 2013). In terms of elaboration and regulation, the results show that the intervention did not foster these strategies; instead, it was suggested that the intervention could protect students in the treatment group from a decrease. Therefore, based on the negative development of the control group, we noted the maintenance of elaboration and regulation as a positive result. For example, the significant negative effect of the trait measure over one year in the control group suggested that students spending more time in the normal school setting were less likely to use the cognitive strategy of elaboration, while this strategy remained constant in the treatment group. This is consistent with the findings of Bannert and colleagues (Bannert et al., 2014), who demonstrated that successful students use a cyclical sequence of SRL strategies that are repeated over time. Interestingly, in our study, both groups reported an increase in time management. Thus, this increase cannot be attributed to the intervention. Rather, we assume that the change in the school setting led to this increase. In compulsory education, students attend school from Monday to Friday, whereas in vocational school, they only spend two days in the school setting and the remaining three days in their training company. Accordingly, all students must adapt their time management to the new circumstances during their first year of vocational education (Wolters & Brady, 2020).
Our study showed that exploring the dispositional and situational attributes of SRL gives a better understanding of how vocational students learn in school (Sembill et al., 2008; Wild & Krapp, 1996). SRL is thereby seen as a process that becomes apparent through a series of events or actions in a certain temporal order (Molenaar & Järvelä, 2014). Against this backdrop, the weekly measurements helped to better understand the students’ general engagement in SRL. For instance, significant differences in the development of planning were observed between the two groups in both the trait and state measurements. However, separate models for each group revealed a significant decrease of the state measurement over time in the control group, while the trait measurement showed a significant increase in the treatment group. In addition, in the trait measurement, students in the treatment group reported lower levels of planning at the beginning of vocational education. This might indicate that students in the treatment group rated themselves lower in planning than the students in the control group because of the more complex setting. However, this increased complexity regarding students’ planning of their own learning could have had a long-term effect on students in the treatment group, resulting in a positive effect in the trait measurement. In contrast, students in the control group exhibited a decrease in planning in the state measurement because they became used to the new setting of only two school days and may have assumed that they must plan less than in lower secondary education (Xu et al., 2014). Thus, the decrease in planning in the control group during the first semester may be a transition effect that fades out with time. Equally important is the significant effect of regulation in the state measurement of the treatment group, reflecting an increase during the first semester, whereas in the trait measurement, no significant effect was found. This change could be explained by the strong situational variation of the variable, indicating that regulation varied strongly situationally but remained stable dispositionally (McCardle & Hadwin, 2015; Pintrich, 2004).
These results support the call for combined analyses of varying granularity (Rovers et al., 2019) and illustrate that intervention-based changes in SRL can be captured more sensitively by combining state and trait measures, while confirming findings from previous studies (e.g., Schmitz & Wiese, 2006). Moreover, the findings align with previous research in the area of SRL in the workplace (for an overview, see Cuyvers et al., 2020). For example, our results provide a complementary addition to the findings of Jossberger and colleagues (Jossberger et al., 2020), illustrating the contribution that vocational schools can make in promoting SRL and the extent to which this can be helpful in the workplace.
SRL and the Learning Context
Concerning the striking development of the variables elaboration, structuring an appropriate learning environment, planning, repetition, and regulation in our study, our results are consistent with previous SRL research emphasizing metacognition for SRL (Bernacki, 2018; Greene et al., 2021). The particular relevance of metacognition for vocational learning has also been demonstrated (Kirschner & Stoyanov, 2018; Rozendaal, 2002; van Velzen, 2004). For instance, teachers who ask vocational students reflective questions influence their self-reflective thinking. Therefore, vocational students’ perceptions of teachers are particularly relevant, highlighting the importance of the relationship between teachers and learners (van Velzen & Tillema, 2004). However, in contrast to other (intervention) studies on SRL in vocational schools (e.g., Sembill et al., 2001), our intervention setting did not lead to increased positive emotions and intrinsic motivation. Although we assumed an indirect effect promoting SRL through our learning setting (Perry, 1998, 2013; Perry et al., 2018, 2020a), we could not identify any effects on the motivation variables, while only weak effects on enjoyment could be demonstrated. One possible explanation is that although a learning environment conducive to SRL was created in our study, no direct regulation strategies were taught; thus, the effects of combined strategy training are not attested, and the intended effect is missing (Paris & Paris, 2001). Finally, vocational students have been part of the school system for several years. Therefore, a stabilization of motivation and emotions over time (e.g., during adolescence; Gillet et al., 2012; Gläser-Zikuda et al., 2005) would also be possible, whereby motivation and emotions would have to be regarded more as a habitual pattern. Consequently, given the negative trajectories of motivation and emotion during primary and secondary education (Meyer & Schlesier, 2021; Raccanello et al., 2019; Scherrer & Preckel, 2019), a targeted promotion of these components would be important. To foster these aspects, the use of socio-psychological elements that explicitly focus on the meanings and inferences that students draw about themselves or situations has produced promising results (Walton & Wilson, 2018).
Overall, teacher competence on SRL could also be a decisive factor (Dignath & Veenman, 2020; Karlen et al., 2020). In our study, teachers' skills and attitudes towards SRL have not been included, but this could be an important explanation for the differential development in SRL. In addition, we assumed a moderation via basic psychological needs in our hypothesis, but this was not explicitly tested. It is possible that the SRL-conducive learning environment had no, little, or inconsistent influence on students’ perceptions of basic need satisfaction. At the same time, we treat the SRL of vocational students in this study independently of the subject content or tasks that learners were required to solve (Zimmerman, 2000). There is still little empirical research that intentionally explores this distinction (Alexander et al., 2011). However, researchers who distinguish between domain-specific (subject-related) and domain-nonspecific (subject-independent) SRL have also come to different conclusions in this respect. For example, Veenman & Spaans (2005) conclude that SRL changes gradually over time, with younger learners more likely to use domain-specific regulatory strategies and older learners more likely to demonstrate general SRL skills. In a recent study, metacognitive self-regulation strategies in digital learning environments were shown to be partly generic and partly domain-specific (Greene et al., 2015). Moreover, the role of the coaching sessions needs to be critically reflected: Although students should be holistically supported in their SRL through the coaching sessions, the coachings have mainly focused on (meta)cognitive aspects, such as planning or organization. However, this creates the risk that self-efficacy (Bandura, 1999; Hattie et al., 1996), for example, is not promoted as an important motivational component of SRL in a sustainable way.
Limitations
Despite the advantages of the present study, notably its longitudinal approach over a school year and in-depth weekly measurements over a semester, some limitations must be taken into account. First, during the second semester, the COVID-19 pandemic affected our study. Because of the school’s closure, the students attended school from home for 12 weeks. This affected the treatment and control groups equally and may have impacted the results of the long questionnaire at the end of the school year, although the students were back at school at that time. In this context, the lockdown forced us to reduce our process analysis to 14 weeks (first semester), even though the measurement was originally planned for the entire school year. Thus, we cannot exclude the possibility of bias in the main and short questionnaires, and the results must be interpreted tentatively.
Second, because of the difference in granularity between the state and trait measurements, time is segmented in different ways (Azevedo, 2009), which might affect comparability between the two measurements and with other studies (e.g., Azevedo et al., 2010). In this study, fine- and coarse-grained measurements were related, and their temporal units were based on weekly measurements (situational development) and annual measurements (dispositional change). Thus, the determination of time units has a significant impact on how the results are interpreted (Molenaar, 2014). Third, the present study is exclusively based on quantitative self-reported data. The enrichment with qualitative data (e.g., think-aloud protocols; Sonnenberg & Bannert, 2019) and objective data (e.g., classroom observation, Dignath & Veenman, 2020; electrodermal activities, Malmberg et al., 2019) could provide deeper insight into the development of the SRL components of vocational students. Fourth, due to the large time span of the study and the number of weekly measurements, the number of missing values is high. Although modern techniques such as multiple-imputation and maximum likelihood estimations are appropriate to handle missing data (Buhi et al., 2008; Schlomer et al., 2010), the possibility of bias cannot be excluded.
Implications and Future Research
The results of the present study regarding the effects of a structural SRL intervention in a vocational school provide important information for future research and practice. It can be assumed that the intervention positively affects students’ (meta)cognitive strategies. The effects on different levels of granularity, therefore, provide additional information on the overall impact of such an environment on students’ situational and lifelong learning (Kirschner & Stoyanov, 2018). For practice, the state measurement brought to light interesting individual trajectories of SRL components over time. Based on this data, researchers and/or teachers could react situationally to the developments of individual students and provide individual support (Molenaar et al., 2021; Reeve & Halusic, 2009). Given the increasing heterogeneity of students in classrooms, it is important in modern and future-oriented schools to focus not just on collective but also on individual learning paths.
In future studies, the structural intervention in vocational schools could also be aligned with an SRL intervention in training companies to achieve comprehensive promotion. In particular, this raises the question of how learners can be supported in their SRL by teachers (Karlen et al., 2020; Kramarski, 2018; Kramarski et al., 2013; Spruce & Bol, 2015). In doing so, it is important not only to create the appropriate environment for SRL but also to provide specific support for students in applying different strategies. Following the principle of scaffolding (Hmelo-Silver et al., 2007), teachers could, for example, use different forms of strategy instruction to support vocational learners individually, according to their stage of development (Dignath & Veenman, 2020). Thus, linking support via direct and indirect strategies would be an important direction for future intervention research on SRL (Paris & Paris, 2001).
Another important aspect relates to the implementation of the intervention: In our study, it became clear that, teachers implemented the coaching sessions differently despite being instructed to use a standardized manual. This in turn might have affected the heterogeneous results in the quantitative analysis. At this point, it becomes clear that the sustainable promotion of SRL among learners also always presupposes a structured learning environment and is not to be confused with minimal guidance (van Hout et al., 2000). Thus, effective implementation of instructional interventions is always a matter of instructional quality (Holtsch et al., 2019).
Finally, the possibility of aptitude-treatment interactions must always be considered when evaluating intervention research. The assumption is that the outcome of an intervention depends on the match between the students’ aptitudes and the treatment (Cronbach & Snow, 1977; Yeh, 2012). Thus, students differ in their readiness to profit from an intervention based on their individual aptitudes (Snow, 1992). Consequently, the intervention may not be equally effective for all students. Further research is needed to provide additional insight into whether there are systematic differences between students based on their aptitudes with respect to the effectiveness of the intervention.
Data Availability
Data are available upon justified request after publication.
Code Availability
Our R codes are available on request.
References
Alexander, P. A., Dinsmore, D. L., Parkinson, M. M., & Winters, F. I. (2011). Self-regulated learning in academic domains. In B. J. Zimmerman & D. H. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 393–407). Routledge/Taylor & Francis Group.
Alheit, P., & Dausien, B. (2002). The ‘double face’ of lifelong learning: Two analytical perspectives on a ‘silent revolution.’ Studies in the Education of Adults, 34(1), 3–22.
Artelt, C., Stanat, P., Schneider, W., & Schiefele, U. (2001). Lesekompetenz: Testkonzeption und Ergebnisse [Reading literacy: test design and results.]. In J. Baumert, E. Klieme, M. Neubrand, M. Prenzel, U. Schiefele, W. Schneider, P. Stanat, K-J. Tillmann, & M. Weiss (Eds.), PISA 2000. Basiskompetenzen von Schülerinnen und Schüler im internationalen Vergleich [PISA 2000. Basic competencies of students in international comparison] (pp. 69–137). Leske + Budrich.
Azevedo, R. (2009). Theoretical, conceptual, methodological, and instructional issues in research on metacognition and self-regulated learning: A discussion. Metacognition and Learning, 4(1), 87–95. https://doi.org/10.1007/s11409-009-9035-7
Azevedo, R., Johnson, A., Chauncey, A., & Burkett, C. (2010). Selfregulated learning with MetaTutor: Advancing the science of learning with MetaCognitive tools. In M. Khine & I. Saleh (Eds.), New science of learning: Computers, cognition, and collaboration in education (pp. 225–247). Springer.
Bandura, A. (1999). Self-efficacy in changing societies. Universal Press.
Bannert, M., Reimann, P., & Sonnenberg, C. (2014). Process mining techniques for analysing patterns and strategies in students’ self-regulated learning. Metacognition and Learning, 9(2), 161–185. https://doi.org/10.1007/s11409-013-9107-6
Barron, B. (2003). When smart groups fail. Journal of the Learning Sciences, 12(3), 307–359. https://doi.org/10.1207/S15327809JLS1203_1
Barrows, H. S., & Tamblyn, R. M. (1980). Problem-based learning: An approach to medical education. Springer.
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01
Baumeister, R. F. (2005). Self-concept, self-esteem, and identity. In V. Derlega, B. Winstead, & W. Jones (Eds.), Personality: Contemporary theory and research (3rd ed., pp. 246–280). Wadsworth.
Bernacki, M. L. (2018). Examining the cyclical, loosely sequenced, and contingent features of self-regulated learning: Trace data and their analysis. In D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (pp. 370–387). Routledge/Taylor & Francis Group.
Biggs, J. B. (1989). Approaches to the enhancement of tertiary teaching. Higher Education Research & Development, 8, 7–25. https://doi.org/10.1080/0729436890080102
Biggs, J. B. (1993). From theory to practice: A cognitive systems approach. Higher Education Research & Development, 12, 73–85. https://doi.org/10.1080/0729436930120107
Boekaerts, M. (1992). The adaptable learning process: Initiating and maintaining behavioural change. Applied Psychology, 41, 377–397. https://doi.org/10.1111/j.1464-0597.1992.tb00713.x
Boekaerts, M. (1996). Personality and the psychology of learning. European Journal of Personality, 10(5), 377–404. https://doi.org/10.1002/(SICI)1099-0984(199612)10:5%3c377::AID-PER261%3e3.0.CO;2-N
Boekaerts, M., & Corno, L. (2005). Self-Regulation in the classroom: A perspective on assessment and intervention. Applied Psychology: An International Review, 54(2), 199–231. https://doi.org/10.1111/j.1464-0597.2005.00205.x
Bolhuis, S. (2003). Towards process-oriented teaching for self-directed lifelong learning: A multidimensional perspective. Learning and Instruction, 13(3), 327–347. https://doi.org/10.1016/S0959-4752(02)00008-7
Brown, A. L., Campione, J. C., & Day, J. D. (1981). Learning to learn: On training students to learn from texts. Educational Researcher, 10(2), 14–21.
Buhi, E. R., Goodson, P., & Neilands, T. B. (2008). Out of sight, not out of mind: Strategies for handling missing data. American Journal of Health Behavior, 32(1), 83–92. https://doi.org/10.5555/ajhb.2008.32.1.83
Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65(3), 245–281. https://doi.org/10.2307/1170684
Cheon, S. H., & Reeve, J. (2015). A classroom-based intervention to help teachers decrease students’ amotivation. Contemporary Educational Psychology, 40, 99–111. https://doi.org/10.1016/j.cedpsych.2014.06.004
Cleary, T. J., Slemp, J., & Pawlo, E. R. (2020). Linking student self-regulated learning profiles to achievement and engagement in mathematics. Psychology in the Schools, 58(3), 443–457. https://doi.org/10.1002/pits.22456
Collins, A., Brown, J. S., & Newman, S. E. (1989). Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics. In L. B. Resnick (Ed.), Knowing, learning, and instruction: Essays in honorof Robert Glaser (pp. 453–494). Lawrence Erlbaum Associates, Inc.
Cronbach, L. J., & Snow, R. E. (1977). Aptitudes and instructional methods: A handbook for research on interactions. Irvington.
Cuyvers, K., Van den Bossche, P., & Donche, V. (2020). Self-regulation of professional learning in the workplace: A state of the art and future perspectives. Vocations and Learning, 13, 281–312. https://doi.org/10.1007/s12186-019-09236-x
Dall’Alba, G. (2009). Learning professional ways of being: Ambiguities of becoming. Educational Philosophy and Theory, 41(1), 34–45. https://doi.org/10.1111/j.1469-5812.2008.00475.x
Davis, S. K., & Hadwin, A. F. (2021). Exploring differences in psychological well-being and self-regulated learning in university student success. Frontline Learning Research, 9(1), 30–43. https://doi.org/10.14786/flr.v9i1.581
De Corte, E. (1996). Instructional psychology: Overview. In E. De Corte & F. E. Weinert (Eds.), International encyclopedia of developmental and instructional psychology (pp. 33–43). Elsevier Science.
De Corte, E. (2012). Constructive, self-regulated, situated, and collaborative learning: An approach for the acquisition of adaptive competence. Journal of Education, 192(2–3), 33–47. https://doi.org/10.1177/0022057412192002-307
De Corte, E. (2016). Improving higher education students’ learning proficiency by fostering their self-regulation skills. European Review, 24(2), 264–276. https://doi.org/10.1017/S1062798715000617
De Backer, L., Van Keer, H., & Valcke, M. (2015). Socially shared metacognitive regulation during reciprocal peer tutoring: Identifying its relationship with students’ content processing and transactive discussions. Instructional Science, 43(3), 323–344. https://doi.org/10.1007/s11251-014-9335-4
De Corte, E., Verschaffel, L., & Masui, C. (2004). The CLIA-model: A framework for designing powerful learning environments for thinking and problem solving. European Journal of Psychology of Education, 19, 365–384.
De Naeghel, J., Van Keer, H., Vansteenkiste, M., Haerens, L., & Aelterman, N. (2016). Promoting elementary school students’ autonomous reading motivation: Effects of a teacher professional development workshop. The Journal of Educational Research, 109(3), 232–252. https://doi.org/10.1080/00220671.2014.942032
De Smul, M., Heirweg, S., Van Keer, H., Devos, G., & Vandevelde, S. (2018). How competent do teachers feel instructing self-regulated learning strategies? Development and validation of the teacher self-efficacy scale to implement self-regulated learning. Teaching and Teacher Education, 71, 214–225. https://doi.org/10.1016/j.tate.2018.01.001
Deci, E. L., & Ryan, R. M. (2002). Overview of self-determination theory: An organismic dialectical perspective. In E. L. Deci & R. M. Ryan (Eds.), Handbook of self-determination research (pp. 3–33). The University of Rochester Press.
Deissinger, T., & Gonon, P. (2021). The development and cultural foundations of dual apprenticeships – a comparison of Germany and Switzerland. Journal of Vocational Education & Training, 73(2), 197–216. https://doi.org/10.1080/13636820.2020.1863451
Dignath, C. (2016). What determines whether teachers enhance self-regulated learning? Predicting teachers’ reported promotion of self-regulated learning by teacher beliefs, knowledge, and self-efficacy. Frontline Learning Research, 4(5), 83–105. https://doi.org/10.14786/flr.v4i5.247
Dignath, C., & Büttner, G. (2008). Components of fostering self-regulated learning among students. A meta-analysis on intervention studies at primary and secondary school level. Metacognition and Learning, 3(3), 231–264. https://doi.org/10.1007/s11409-008-9029-x
Dignath, C., & Veenman, M. V. J. (2020). The role of direct strategy instruction and indirect activation of self-regulated learning—Evidence from classroom observation studies. Educational Psychology Review, 33(2), 1–45. https://doi.org/10.1007/s10648-020-09534-0
Dignath, C., Büttner, G., & Langfeldt, H. (2008). How can primary school students learn self-regulated learning strategies most effectively? A meta-analysis on self-regulation training programmes. Educational Research Review, 3, 101–129. https://doi.org/10.1016/j.edurev.2008.02.003
Dubs, R. (2015). Gedanken zu Kompetenzen und zum selbstgesteuerten Lernen am Beispiel der Kompetenz „Argumentieren“ [Thoughts on competencies and self-directed learning using the example of the competency "argumentation".]. In A. Rausch, J. Warwas, J. Seifried, & E. Wuttke (Eds.), Konzepte und Ergebnisse ausgewählter Forschungsfelder der beruflichen Bildung [Concepts and results of selected fields of research in vocational education and training] (pp. 21–35). Schneider Hohengehren.
Entwistle, N. (1991). Approaches to learning and perceptions of the learning environment. Higher Education, 22(3), 201–204. https://doi.org/10.1007/BF00132287
Ertl, H., & Sloane, P. F. E. (2004). The German training system and the world of work: The transfer potential of the Lernfeldkonzept. Berufs- Und Wirtschaftspädagogik, 7, 1–15.
Forster-Heinzer, S., Holtsch, D., Rohr-Mentele, S., & Eberle, F. (2016). Do they intend to stay? An empirical study of commercial apprentices’ motivation, satisfaction and intention to remain within the learned occupation. Empirical Research on Vocational Education and Training, 8(16), 1–27. https://doi.org/10.1186/s40461-016-0041-0
Frey, A., & Terhart, P. (2010). Wie man Ausbildungsabbrüche vermeiden kann [How to avoid training dropouts]. Blätter der Wohlfartspflege (BdW), 157(3), 109–113. https://doi.org/10.5771/0340-8574-2010-3-109
Gillet, N., Vallerand, R. J., & Lafrenière, M.-A.K. (2012). Intrinsic and extrinsic school motivation as a function of age: The mediating role of autonomy support. Social Psychology of Education, 15(1), 77–95. https://doi.org/10.1007/s11218-011-9170-2
Gläser-Zikuda, M., Fuss, S., Laukenmann, M., Metz, K., & Randler, C. (2005). Promoting students’ emotions and achievement–Instructional design and evaluation of the ECOLE-approach. Learning and Instruction, 15(5), 481–495. https://doi.org/10.1016/j.learninstruc.2005.07.013
Goetz, T., Luedtke, O., Nett, U. E., Keller, M., & Lipnevich, A. A. (2013). Characteristics of teaching and students’ emotions in the classroom: Investigating differences across domains. Contemporary Educational Psychology, 38, 383–394. https://doi.org/10.1016/j.cedpsych.2013.08.001
Gogol, K., Brunner, M., Goetz, T., Martin, R., Ugen, S., Keller, U., Fischbach, A., & Preckel, F. (2014). “My questionnaire is too long!” The assessments of motivational-affective constructs with three- item and single- item measures. Contemporary Educational Psychology, 39(3), 188–205. https://doi.org/10.1016/j.cedpsych.2014.04.002
Gonon, P. (2017). Renaissance der dualen Berufsbildung durch Modernisierung [Renaissance of dual vocational training through modernization]. In P. Schlögl, M. Stock, D. Moser, K. Schmid, & F. Gramlinger (Eds.), Berufsbildung, eine Renaissance [Vocational training, a renaissance]? (pp. 44–60). Bertelsmann.
Greene, J., & Azevedo, R. (2010). The measurement of learners’ self-regulated cognitive and metacognitive processes while using computer-based learning environments. Educational Psychologist, 45(4), 203–209. https://doi.org/10.1080/00461520.2010.515935
Greene, J. A., Bolick, C. M., Jackson, W. P., Caprino, A. M., Oswald, C., & McVea, M. (2015). Domain-specificity of self-regulated learning processing in science and history. Contemporary Educational Psychology, 42, 111–128.
Greene, J., Plumley, R. D., Urban, C. J., Bernacki, M., Gates, K. M., Hogan, K. A., Demetriou, C., & Panter, A. T. (2021). Modeling temporal self-regulatory processing in a higher education biology course. Learning and Instruction, 72, 1–8. https://doi.org/10.1016/j.learninstruc.2019.04.002
Greeno, J. G. (2006). Learning in activity. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 79–96). Cambridge University Press.
Hadwin, A. F., Järvelä, S., & Miller, M. (2018). Self-regulation, co-regulation, and shared regulation in collaborative learning environments. In D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (pp. 83–106). Routledge/Taylor & Francis Group.
Hanushek, E. A., Schwerdt, G., Woessmann, L., & Zhang, L. (2017). General education, vocational education, and labor-market outcomes over the lifecycle. Journal of Human Resources, 52(1), 48–87. https://doi.org/10.3368/jhr.52.1.0415-7074R
Hattie, J., Biggs, J., & Purdie, N. (1996). Effects of learning skills interventions on student learning: A meta analysis. Review of Educational Research, 66, 99–136. https://doi.org/10.2307/1170605
Himmelstein, P. H., Woods, W. C., & Wright, A. G. (2019). A comparison of signal-and event-contingent ambulatory assessment of interpersonal behavior and affect in social situations. Psychological Assessment, 31(7), 952–960. https://doi.org/10.1037/pas0000718
Hmelo-Silver, C. E., Duncan, R. G., & Chinn, C. A. (2007). Scaffolding and achievement in problem-based and inquiry learning: A response to Kirschner, Sweller, and Clark (2006). Educational Psychologist, 42(2), 99–107. https://doi.org/10.1080/00461520701263368
Holtsch, D., Hartig, J., & Shavelson, R. (2019). Do Practical and Academic Preparation Paths Lead to Differential Commercial Teacher “Quality”? Vocations and Learning, 12(1), 23–46. https://doi.org/10.1007/s12186-018-9208-0
Höpfer, (2017). Eigenaktivität als Lernchance: lernförderliches Potenzial und adaptive Unterstützung eigeninitiierter verbaler Handlungen von angehenden Kaufleuten [Self-activity as a learning opportunity: learning-promoting potential and adaptive support of self-initiated verbal actions of prospective merchants] (Dissertation). Universität Zürich. https://www.zora.uzh.ch/id/eprint/147332/1/20173081.pdf
Isen, A. M., & Reeve, J. (2005). The influence of positive affect on intrinsic and extrinsic motivation: Facilitating enjoyment of play, responsible work behavior, and self-control. Motivation and Emotion, 29(4), 295–323. https://doi.org/10.1007/s11031-006-9019-8
Isohätälä, J., Järvenoja, H., & Järvelä, S. (2017). Socially shared regulation of learning and participation in social interaction in collaborative learning. International Journal of Educational Research, 81, 11–24. https://doi.org/10.1016/j.ijer.2016.10.006
Janssen, J., Erkens, G., Kirschner, P. A., & Kanselaar, G. (2012). Task-related and social regulation during online collaborative learning. Metacognition and Learning, 7, 25–43.
Järvelä, S., & Bannert, M. (2021). Editorial. Temporal and adaptive process of regulated learning - what can multimodal data tell? Learning and Instruction, 72, https://doi.org/10.1016/j.learninstruc.2019.101268
Jossberger, H., Brand-Gruwel, S., van de Wiel, M. W. J., & Boshuizen, H. P. A. (2020). Exploring students’ self-regulated learning in vocational education and training. Vocations and Learning, 13, 131–158.
Karlen, Y., Hertel, S., & Hirt, C. N. (2020). Teachers’ professional competences in self-regulated learning: An approach to integrate teachers’ competences as self-regulated learners and as agents of self-regulated learning in a holistic manner. Frontiers in Education, 5, 159. https://doi.org/10.3389/feduc.2020.00159
Khaled, A., Gulikers, J., Biemans, H., & Mulder, M. (2015). Occurrences and quality of teacher and student strategies for self-regulated learning in hands-on simulations. Studies in Continuing Education, 38, 101–121. https://doi.org/10.1080/0158037X.2015.1040751
Khosa, D. K., & Volet, S. E. (2014). Productive group engagement in cognitive activity and metacognitive regulation during collaborative learning: Can it explain differences in students’ conceptual understanding? Metacognition and Learning, 9, 287–307. https://doi.org/10.1007/s11409-014-9117-z
Kirschner, P., & Stoyanov, S. (2018). Educating youth for nonexistent/not yet existing professions. Educational Policy, 34(3), 477–517.
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41, 75–86.
Kramarski, B. (2008). Promoting teachers’ algebraic reasoning and self-regulation with metacognitive guidance. Metacognition and Learning, 3(2), 83–99. https://doi.org/10.1007/s11409-008-9020-6
Kramarski, B. (2018). Teachers as agents in promoting students’ SRL and performance: Applications for teachers’ dual-role training program. In D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (pp. 223–239). Routledge/Taylor & Francis Group.
Kramarski, B., & Kohen, Z. (2017). Promoting preservice teachers’ dual self-regulation roles as learners and as teachers: Effects of generic vs. specific prompts. Metacognition and Learning, 12(2), 157–191. https://doi.org/10.1007/s11409-016-9164-8
Kramarski, B., Desoete, A., Bannert, M., Narciss, S., & Perry, N. (2013). New perspectives on integrating self-regulated learning at school. Education Research International, 1, 1–4. https://doi.org/10.1155/2013/498214
Lajoie, S. P., Zheng, J., Li, S., Jarrell, A., & Gube, M. (2021). Examining the temporal nature of affect and self regulation in the context of clinical reasoning. Learning and Instruction, 72, 1–14. https://doi.org/10.1016/j.learninstruc.2019.101219
Lang, M., & Pätzold, G. (2006). Selbstgesteuertes Lernen - theoretische Perspektiven und didaktische Zugänge [Ways to promote self-directed learning in vocational training]. In D. Euler, M. Lang, & G. Pätzold (Eds.), Selbstgesteuertes Lernen in der beruflichen Bildung [Self-directed learning in vocational training] (pp. 9–35). Franz Steiner Verlag.
Leidinger, M., & Perels, F. (2012). Training self-regulated learning in the classroom: Development and evaluation of learning materials to train self-regulated learning during regular mathematics lessons at primary school. Education Research International, 2012, 1–14. https://doi.org/10.1155/2012/735790
Malmberg, J., Järvelä, S., Holappa, J., Haataja, E., Huang, X., & Siipo, A. (2019). Going beyond what is visible: What multichannel data can reveal about interaction in the context of collaborative learning? Computers in Human Behavior, 96, 235–245. https://doi.org/10.1016/j.chb.2018.06.030
Martin, J. (2007). The selves of educational psychology: Conceptions, contexts, and critical considerations. Educational Psychologist, 42, 79–89.
Martin, A. J., Papworth, B., Ginns, P., Malmberg, L.-E., Collie, R. J., & Calvo, R. A. (2015). Real-time motivation and engagement during a month at school: Every moment of every day for every student matters. Learning and Individual Differences, 38, 26–35. https://doi.org/10.1016/j.lindif.2015.01.014
Masui, C., & De Corte, E. (2005). Learning to reflect and to attribute constructively as basic components of self-regulated learning. British Journal of Educational Psychology, 75, 351–372. https://doi.org/10.1348/000709905X25030
McCardle, L., & Hadwin, A. F. (2015). Using multiple, contextualized data sources to measure learners’ perceptions of their self-regulated learning. Metacognition and Learning, 10, 43–75. https://doi.org/10.1007/s11409-014-9132-0
McCaslin, M., & Good, T. L. (1996). The informal curriculum. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of educational psychology (pp. 622–670). Macmillan.
McCaslin, M., & Vriesema, C. C. (2018). Co-regulation: A model for classroom research in a Vygotskian perspective. In G. A. D. Liem & D. M. McInerey (Eds.), Big theories revised 2: Research on sociocultural influences on motivation and learning (pp. 319–352). Information Age Publishing.
McNeish, D., & Matta, T. (2018). Differentiating between mixed-effects and latent-curve approaches to growth modeling. Behavior Research Methods, 50(4), 1398–1414. https://doi.org/10.3758/s13428-017-0976-5
Metzger, C., Nüesch, C., Zeder, A., & Jabornegg, D. (2005). Förderung und Prüfung von Lern- kompetenzen in der kaufmännischen Grundbildung [Promotion and testing of learning competencies in basic commercial education]. https://edudoc.ch/record/3844/files/Bericht%20Vorprojekt_definitiv_16.pdf?version=1
Meyer, S., & Schlesier, J. (2021). The development of students’ achievement emotions after transition to secondary school: A multilevel growth curve modelling approach. European Journal of Psychology of Education, 1–21,. https://doi.org/10.1007/s10212-021-00533-5
Molenaar, I. (2014). Advances in temporal analysis in learning and instruction. Frontline Learning Research, 2(4), 15–24. https://doi.org/10.14786/flr.v2i4.118
Molenaar, I., Sleegers, P., & van Boxtel, C. (2014). Metacognitive scaffolding during collaborative learning: A promising combination. Metacognition Learning, 9, 309–332. https://doi.org/10.1007/s11409-014-9118-y
Molenaar, I., Horvers, A., & Baker, R. (2021). What can moment-by-moment learning curves tell about students’ self-regulated learning? Learning and Instruction, 72, 1–14. https://doi.org/10.1016/j.learninstruc.2019.05.003
Molenaar, I., & Järvelä, S. (2014). Sequential and temporal characteristics of self and socially regulated learning. Metacognition and Learning, 9, 75–85. Springer.
Moos, D. C., & Azevedo, R. (2008). Self-regulated learning with hypermedia: The role of prior domain knowledge. Contemporary Educational Psychology, 33(2), 270–298. https://doi.org/10.1016/j.cedpsych.2007.03.001
Müller, F. H., Hanfstingl, B., & Andreitz, I. (2007). Skalen zur motivationalen Regulation beim Lernen von Schülerinnen und Schülern: Adaptierte und ergänzte Version des Academic Self-Regulation Questionnaire (SRQ-A) nach Ryan & Connell [Scales of motivational regulation in student learning: Adapted and augmented version of the Ryan & Connell academic self-regulation questionnaire (SRQ-A).]. Alpen-Adria-Universität.
Musset, P. (2019). Improving work-based learning in schools. OECD social, employment and migration working papers, No. 233. OECD Publishing. https://doi.org/10.1787/918caba5-en
OECD (2021). Teaching and learning in VET: Providing effective practical training in school-based settings. OECD Policy Responses to Coronavirus (COVID-19), OECD Publishing. https://doi.org/10.1787/64f5f843-en
Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8, 1–28. https://doi.org/10.3389/fpsyg.2017.00422
Panadero, E., Kirschner, P., Järvelä, S., & Malmberg, J. (2015). How individual self-regulation affects group regulation and performance: A shared regulation intervention. Small Group Research, 46(4), 431–454.
Paris, S. G., & Paris, A. H. (2001). Classroom applications of research on self-regulated learning. Educational Psychologist, 36(2), 89–101. https://doi.org/10.1207/S15326985EP3602_4
Park, S., Holloway, S., Arendtsz, A., Bempechat, J., & Li, J. (2012). What makes students engaged in learning? A time-use study of within- and between-individual predictors of emotional engagement in low-performing high schools. Journal of Youth and Adolescence, 41, 390–401. https://doi.org/10.1007/s10964-011-9738-3
Patrick, H., & Middleton, M. J. (2002). Turning the kaleidoscope: What we see when self-regulated learning is viewed with a qualitative lens. Educational Psychologist, 37(1), 27–39. https://doi.org/10.1207/00461520252828537
Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341.
Pekrun, R., Goetz, T., & Perry, R. P. (2005). Achievement emotions questionnaire (AEQ) - User’s manual. Ludwig Maximilian Universität.
Perry, N. E. (1998). Young children’s self-regulated learning and contexts that support it. Journal of Educational Psychology, 90(4), 715–729. https://doi.org/10.1037/0022-0663.90.4.715
Perry, N. E. (2013). Understanding classroom processes that support children’s self-regulation of learning. In N. E. Perry (Ed.), Self-regulation and dialogue in primary classrooms (pp. 45–68). The British Psychological Society.
Perry, N. E., & Drummond, L. (2002). Helping young students become self-regulated researchers and writers. The Reading Teacher, 56(3), 298–310.
Perry, N. E., & Rahim, A. (2011). Studying self-regulated learning in classrooms. In B. J. Zimmerman & D. H. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 122–136). Routledge/Taylor & Francis Group.
Perry, N. E., Brenner, C. A., & MacPherson, N. (2015). Using teacher learning teams as framework for bridging theory and practice in self-regulated learning. In T. J. Cleary (Ed.), Self-regulated learning interventions with at-risk youth: Enhancing adaptability, performance, and well-being (pp. 229–250). American Psychological Association. https://doi.org/10.1037/14641-011
Perry, N. E., Mazabel, S., Dantzer, B., & Winne, P. (2018). Supporting self-regulation and self-determination in the context of music education. In G. A. D. Liem & D. M. McInerney (Eds.), Big theories revisited 2: A volume of research on sociocultural influences on motivation and learning (pp. 295–318). Information Age Press.
Perry, N., Lisaingo, S., Yee, N., Parent, N., Wan, X., & Muis, K. (2020a). Collaborating with teachers to design and implement assessments for self-regulated learning in the context of authentic classroom writing tasks. Assessment in Education: Principles, Policy & Practice, 27(4), 416–443. https://doi.org/10.1080/0969594X.2020.1801576
Perry, N., Mazabel, S., & Yee, N. (2020b). Using self-regulated learning to support students with learning disabilities in classrooms. In A. J. Martin, R. A. Sperling, & K. J. Newton (Eds.), Handbook of educational psychology and students with special needs (pp. 292–314). Routledge.
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., R Core Team (2021). nlme: Linear and nonlinear mixed effects models. R package version 3.1–153. https://cran.r-project.org/web/packages/nlme/index.html
Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 452–502). Academic Press. https://doi.org/10.1016/B978-012109890-2/50043-3
Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16, 385–407. https://doi.org/10.1007/s10648-004-0006-x
R Core Team (2019). R: A language and environment for statistical computing. R foundation for statistical computing. https://www.R-project.org/index.html
Raccanello, D., Brondino, M., Moè, A., Stupnisky, R., & Lichtenfeld, S. (2019). Enjoyment, boredom, anxiety in elementary schools in two domains: Relations with achievement. The Journal of Experimental Education, 87(3), 449–469. https://doi.org/10.1080/00220973.2018.1448747
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. Sage.
Reeve, J., & Halusic, M. (2009). How K-12 teachers can put self- determination theory principles into practice. Theory and Research in Education, 7(2), 145–154. https://doi.org/10.1177/1477878509104319
Resnick, L. B. (1987). Education and learning to think. National Academy Press.
Rogat, T. K., & Adams- Wiggins, K. R. (2015). Interrelation between regulatory and socioemotional processes within collaborative groups characterized by facilitative and directive other-regulation. Computers in Human Behavior, 52, 589–600. https://doi.org/10.1016/j.chb.2015.01.026
Rogat, T. K., & Linnenbrink- Garcia, L. (2011). Socially shared regulation in collaborative groups: An analysis of the interplay between quality of social regulation and group processes. Cognition and Instruction, 29(4), 375–415. https://doi.org/10.1080/07370008.2011.607930
Rovers, S., Clarebout, G., Savelberg, H., de Bruin, A., & Merriënboer, J. (2019). Granularity matters: Comparing different ways of measuring self-regulated learning. Metacognition and Learning, 14(3), 1–19. https://doi.org/10.1007/s11409-019-09188-6
Rozendaal, J. S. (2002). Motivation and information processing in innovative secondary vocational education. Leiden University.
Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61(101860), 1–11. https://doi.org/10.1016/j.cedpsych.2020.101860
Sachs, M. E., Ellis, R. J., Schlaug, G., & Loui, P. (2016). Brain connectivity reflects human aesthetic responses to music. Social Cognitive and Affective Neuroscience, 11(6), 884–891. https://doi.org/10.1093/scan/nsw009
Salonen, P., Vauras, M., & Efklides, A. (2005). Social interaction- What can it tell us about metacognition and coregulation in learning? European Psychologist, 10(3), 199–208. https://doi.org/10.1027/1016-9040.10.3.199
Scherrer, V., & Preckel, F. (2019). Development of motivational variables and self-esteem during the school career: A meta-analysis of longitudinal studies. Review of Educational Research, 89(2), 211–258. https://doi.org/10.3102/0034654318819127
Schlomer, G. L., Bauman, S., & Card, N. A. (2010). Best practices for missing data management in counseling psychology. Journal of Counseling Psychology, 57(1), 1–10. https://doi.org/10.1037/a0018082
Schmitz, B., & Wiese, B. S. (2006). New perspectives for the evaluation of training sessions in self-regulated learning: Time-series analyses of diary data. Contemporary Educational Psychology, 31(1), 64–96. https://doi.org/10.1016/j.cedpsych.2005.02.002
Schmitz, B., Klug, J., & Hertel, S. (2012). Collecting and analyzing longitudinal diary data. In B. Laursen, T. D. Little, & N. A. Card (Eds.), Handbook of developmental research methods (pp. 181–195). Guilford Press.
Sembill, D., Schumacher, L., Wolf, K. D., Wuttke, E., & Santjer-Schnabel, I. (2001). Förderung der Problemlösefähigkeit und der Motivation durch Selbstorganisiertes Lernen [Promote problem-solving skills and motivation through self-organized learning]. In K. Beck & V. Krumm (Eds.), Lehren und Lernen in der beruflichen Erstausbildung [Teaching and learning in initial vocational training] (pp. 257–281). Leske + Budrich.
Sembill, D., Wuttke, E., Seifried, J., Egloffstein, M., & Rausch, A. (2007). Selbstorganisiertes Lernen in der beruflichen Bildung. Abgrenzungen, Befunde und Konsequenzen [Self-organized learning in vocational education. Delimitations, findings and consequences]. Berufs- Und Wirtschaftspädagogik Online, 13, 1–33.
Sembill, D., Seifried, J., & Dreyer, K. (2008). PDAs als Erhebungsinstrument in der beruflichen Lernforschung - Ein neues Wundermittel oder bewährter Standard [PDAs as a survey instrument in professional learning research - A new silver bullet or proven standard]? Empirische Pädagogik, 22(1), 64–77.
Sembill, D. (1999). Selbstorganisation als Modellierungs-, Gestaltungs- und Erforschungsidee beruflichen Lernens [Self-organization as a modeling, design, and research idea of professional learning.]. In T. Tramm, D. Sembill, F. Klauser, & E. G. John (Eds.), Professionalisierung kaufmännischer Berufsbildung: Beiträge zur Öffnung der Wirtschaftspädagogik für die Anforderungen des 21. Jahrhunderts. Festschrift zum 60. Geburtstag von Frank Achtenhagen [Professionalization of commercial vocational education: Contributions to the opening of business education for the demands of the 21st century. Festschrift on the occasion of Frank Achtenhagen's 60th birthday] (pp. 146–174). Peter Lang.
Sembill, (2004). Abschlussbericht zu „Prozessanalysen Selbstorganisierten Lernens“ im Rahmen des DFG-Schwerpunktprogramms „Lehr-Lern-Prozesse in der kaufmännischen Erstausbildung“ [Final report on “Process Analyses of Self-Organized Learning” within the framework of the DFG priority program “Teaching-Learning Processes in Initial Commercial Training”]. https://www.uni-bamberg.de/fileadmin/uni/fakultaeten/sowi_lehrstuehle/wirtschaftspaedagogik/Dateien/Forschung/Forschungsprojekte/Prozessanalysen/DFG-Abschlussbericht_sole.pdf
Sinha, S., Rogat, T. K., Adams-Wiggins, K. R., & Hmelo-Silver, C. E. (2015). Collaborative group engagement in a computer-supported inquiry learning environment. International Journal of Computer-Supported Collaborative Learning, 10(3), 273–307. https://doi.org/10.1007/s11412-015-9218-y
Skinner, E., Saxton, E., Currie, C., & Shusterman, G. (2017). A motivational account of the undergraduate experience in science: Brief measures of students’ self-system appraisals, engagement in coursework, and identity as a scientist. International Journal of Science Education, 39(17), 2433–2459. https://doi.org/10.1080/09500693.2017.1387946
Snow, R. E. (1992). Aptitude theory: Yesterday, today, and tomorrow. Educational Psychologist, 27(1), 5–32. https://doi.org/10.1207/s15326985ep2701_3
Sonnenberg, C., & Bannert, M. (2019). Using process mining to examine the sustainability of instructional support: How stable are the effects of metacognitive prompting on self-regulatory behavior? Computers in Human Behavior., 96, 259–272. https://doi.org/10.1016/j.chb.2018.06.003
Spruce, R., & Bol, L. (2015). Teacher beliefs, knowledge, and practice of self-regulated learning. Metacognition and Learning, 10(2), 245–277. https://doi.org/10.1007/s11409-014-9124-0
Su, Y.-L., & Reeve, J. (2011). A meta-analysis of the effectiveness of intervention programs designed to support autonomy. Educational Psychology Review, 23(1), 159–188. https://doi.org/10.1007/s10648-010-9142-7
van Velzen, J. H. (2004). Assessing students’ self-reflective thinking in the classroom: The self-reflective thinking questionnaire. Psychological Reports, 95, 1175–1186. https://doi.org/10.2466/pr0.95.3f.1175-1186
van Buuren, S., & Groothuis-Oudshoorn, K. (2011). Mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67. https://doi.org/10.18637/jss.v045.i03
van Grinsven, L., & Tillema, H. (2006). Learning opportunities to support student self-regulation: Comparing different instructional formats. Educational Research, 48(1), 77–92. https://doi.org/10.1080/00131880500498495
van Hout-Wolters, B., Simons, R.-J., & Volet, S. (2000). Active Learning: Self-directed Learning and Independent Work. In R.-J. Simons, J. van der Linden, & T. Duffy (Eds.), New Learning (pp. 21–36). Springer. https://doi.org/10.1007/0-306-47614-2_2
van Leeuwen, A., & Janssen, J. (2019). A systematic review of teacher guidance during collaborative learning un primary and secondary education. Educational Research, 27, 71–89. https://doi.org/10.1016/j.edurev.2019.02.001
van Velzen, J. H., & Tillema, H. H. (2004). Students’ use of self-reflective thinking: When teaching becomes coaching. Psychological Reports, 95, 1229–1238. https://doi.org/10.2466/pr0.95.3f.1229-1238
Van Grinsven, L. (2003). Krachtige leeromgevingen in het Middelbaar Beroepsonderwijs: Effect op motivatie en strategiegebruik bij zelfregulered leren? [Powerful learning environments in secondary vocational education?] (Dissertation). Universiteit Leiden.
Vansteenkiste, M., Ryan, R. M., & Soenens, B. (2020). Basic psychological need theory: Advancements, critical themes, and future directions. Motivation and Emotion, 44, 1–31.
Veenman, M. V. J., & Spaans, M. A. (2005). Relation between intellectual and metacognitive skills: Age and task differences. Learning and Individual Differences, 15(2), 159–176. https://doi.org/10.1016/j.lindif.2004.12.001
Veenman, M. V. J., Wolters, V., & Afflerbach, P. (2006). Metacognition and learning: Conceptual and methodological considerations. Metacognition and Learning, 1(1), 3–14. https://doi.org/10.1007/s11409-006-6893-0
Vermunt, J. D. (1995). Process-oriented instruction in learning and thinking strategies. European Journal of Psychology Education, 10(4), 325–349. https://doi.org/10.1007/bf03172925
Vermunt, J. D., & Donche, V. (2017). A learning patterns perspective on student learning in higher education: State of the art and moving forward. Educational Psychology Review, 29, 269–299. https://doi.org/10.1007/s10648-017-9414-6
Vriesema, C. C., & McCaslin, M. (2020). Experience and meaning in small-group contexts: Fusing observational and self and other dynamics. Frontline Learning Research, 8(3), 126–139. https://doi.org/10.14786/flr.v8i3.493
Walton, G. M., & Wilson, T. D. (2018). Wise interventions: Psychological remedies for social and personal problems. Psychological Review, 125(5), 617–655. https://doi.org/10.1037/rev0000115
Waytens, K., Lens, W., & Vandenberghe, R. (2002). ‘Learning to learn’: Teachers’ conceptions of their supporting role. Learning and Instruction, 12, 305–322.
Weinstein, C. E., & Hume, L. M. (1998). Study strategies for lifelong learning. American Psychological Association. https://doi.org/10.1037/10296-000
Wild, K.-P., & Krapp, A. (1996). Die Qualität subjektiven Erlebens in schulischen und betrieblichen Lernumwelten: Untersuchungen mit der Erlebnis-Stichproben-Methode [The quality of subjective experience in school and workplace learning environments: Studies using the experience sampling method]. Unterrichtswissenschaft, 24(3), 195–216. https://doi.org/10.25656/01:7935
Wild, K.-P., & Schiefele, U. (1994). Lernstrategien im Studium: Ergebnisse zur Faktorenstruktur und Reliabilität eines neuen Fragebogens [Learning strategies in college: Results on the factor structure and reliability of a new questionnaire]. Zeitschrift Für Differentielle Und Diagnostische Psychologie, 15, 185–200.
Wild, K.-P. (2001). Die Optimierung von Videoanalysen durch zeitsynchrone Befragungsdaten aus dem Experience Sampling [Optimizing video analytics with time-synchronized survey data from experience sampling]. In S. v. Aufschnaiter & M. Welzel (Eds.), Nutzung von Videodaten zur Untersuchung von Lehr-Lernprozessen: Aktuelle Methoden empirischer pädagogischer Forschung [Using video data to study teaching-learning processes: Current methods of empirical educational research] (pp. 61–74). Waxmann.
Winne, P. H. (2010). Improving measurements of self-regulated learning. Educational Psychologist, 45(4), 267–276. https://doi.org/10.1080/00461520.2010.517150
Winne, P. H. (2019). Paradigmatic dimensions of instrumentation and analytic methods in research on self-regulated learning. Computers in Human Behavior, 96, 285–290. https://doi.org/10.1016/j.chb.2019.03.026
Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277–304). Lawrence Erlbaum Associates Publishers.
Winne, P. H., & Perry, N. E. (2000). Measuring self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 531–566). Academic Press. https://doi.org/10.1016/B978-012109890-2/50045-7
Winstone, N. E., Nash, R., Rowntree, J., & Menezes, R. (2016). What do students want most from written feedback information? Distinguishing necessities from luxuries using a budgeting methodology. Assessment & Evaluation in Higher Education, 41(8), 1237–1253. https://doi.org/10.1080/02602938.2015.1075956
Wolters, C. A. (2011). Regulation of motivation: Contextual and social aspects. Teachers College Record, 113(2), 265–283.
Wolters, C. A., & Brady, A. C. (2020). College students’ time management: A self-regulated learning perspective. Educational Psychology Review. Advanced online publication. https://doi.org/10.1007/s10648-020-09519-z
Xu, J., Yuan, R., Xu, B., & Xu, M. (2014). Modeling students’ time management in math homework. Learning and Individual Differences, 34, 33–42. https://doi.org/10.1016/j.lindif.2014.05.011
Yeh, Y.-C. (2012). Aptitude-treatment interaction. In N. M. Seel (Ed.), Encyclopedia of the sciences of learning (pp. 295–298). Springer. https://doi.org/10.1007/978-1-4419-1428-6_582
Yu, C., Li, X., Wang, S., & Zhang, W. (2016). Teacher autonomy support reduces adolescent anxiety and depression: An 18-month longitudinal study. Journal of Adolescence, 49, 115–123. https://doi.org/10.1016/j.adolescence.2016.03.001
Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329–339. https://doi.org/10.1037/0022-0663.81.3.329
Zimmerman, B. J. (2000). Attaining self-regulation: a social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–40). Academic Press. https://doi.org/10.1016/b978-012109890-2/50031-7
Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166–183. https://doi.org/10.3102/0002831207312909
Zimmerman, B. J., & Bandura, A. (1994). Impact of self-regulatory influences on writing course attainment. American Educational Research Journal, 31(4), 845–862. https://doi.org/10.2307/1163397
Zimmerman, B. J., & Martinez-Pons, M. (1990). Student differences in self-regulated learning: Relating grade, sex, and giftedness to self-efficacy and strategy use. Journal of Educational Psychology, 82(1), 51–59. https://doi.org/10.1037/0022-0663.82.1.51
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Mejeh, M., Held, T. Understanding the Development of Self-Regulated Learning: An Intervention Study to Promote Self-Regulated Learning in Vocational Schools. Vocations and Learning 15, 531–568 (2022). https://doi.org/10.1007/s12186-022-09298-4
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DOI: https://doi.org/10.1007/s12186-022-09298-4