Self-regulation in School

Research on self-regulated learning has grown over the past three decades from being comparatively neglected to being one of the most intensively studied fields of research in educational psychology (Winne, 2005). The huge increase in interest is in large part attributable to the consensus of policy makers, teachers, educators, parents and researchers concerning the importance of students’ ability to self-regulate themselves and their learning processes (Boekaerts, 1999; Paris & Paris, 2001). The idea of lifelong learning is assuming ever greater importance in our society (Schober et al., 2007). The ever changing positions and responsibilities in the workplace as well as rapidly developing new technologies make it extremely important to be able to acquire knowledge and new skills not only as quickly and comprehensively as possible but also as independently as possible. Thus, one of the most important duties of our schools is to give students the ability to regulate themselves and their own learning. This central interest in self-regulated learning raises many different questions.

For example: Which components are most important for effective self-regulation? How can the learning process be best protected against distracting thoughts and emotions? Which strategies are most often used by students to regulate their learning process? Which strategies are most effective for successful self-regulation? To what extent do dispositional and situational aspects influence learning behavior? How can self-regulated learning best be assessed? Past researchers have employed an immense diversity of definitions, models and studies on self-regulated learning in their attempts to address these problems. This is matched by an immense variety of results.

Current definitions and models of self-regulation and self-regulated learning illustrate the complexity of these concepts (e.g., Boekaerts & Niemivirta, 2005; Borkowski, 1996; Pintrich, 2005; Winne & Hadwin, 1998; Zimmerman, 2005). Effective self-regulation requires not only the ability to regulate the self motivationally and emotionally and to protect the self and the learning process against competing personal needs, but also the ability to reduce situational distractions and use metacognitive and cognitive learning strategies to successfully organize the learning process (Boekaerts, 1999). The studies presented in this dissertation focus on students’ regulation of the self in terms of protecting the learning process against internal and external disruptions (Study I and Study II) and on students’ use of

metacognitive strategies when organizing the learning process for a test (Study III). Thus, the present studies contribute to research on the ways in which students efficiently regulate their learning and to the question of which strategies are not often used, assessing both the dispositional factors underpinning the ability to regulate the self as well as the situational factors influencing the employment of strategies. To this end, traditional questionnaires were used to assess the dispositional nature (Study I and Study II) and the experience sampling method (Csikszentmihalyi & Larson, 1987; Hektner, Schmidt, & Csikszentmihalyi, 2007) was used to assess the situational nature of regulation strategies (Study II and Study III).

1.1.1 The Concept of Self-regulated Learning

Most theorists would agree students who self-regulate their learning display an active and constructive engagement in the process of meaning generation. Skilled self-regulators adapt their emotions, as well as their cognitions and actions as needed in order to optimize their learning and motivation (Boekaerts & Corno, 2005). More specifically, self-regulated learning is often described as the combination of the ability to employ appropriate and powerful strategies to attain learning goals important to the individual, together with adapting the application of those strategies in order to monitor one’s own learning to detect and eliminate possible learning problems (e.g., Nueckles, Huebner, & Renkl, 2009; Paris & Paris, 2001; Schraw, 1998; Zimmerman, 2005). In addition to metacognitive and cognitive strategies (Boekaerts & Corno, 2005), motivational and emotional components of self-regulated learning represent an important part of current self-self-regulated learning models (e.g., Boekaerts & Niemivirta, 2005; Borkowski, 1996; Pintrich, 2005; Winne & Hadwin, 1998; Zimmerman, 2005). Most current models are consistent in the inclusion and description of these essential components of self-regulated learning (Puustinen & Pulkkinen, 2001).

Boekaerts (1999) provides a detailed description of the components that are essential for effective self-regulated learning in her three-layered model of self-regulated learning (Figure 1.1). Going from the outside to the inside, the first layer describes the regulation of the self with special concern for motivational and emotional aspects. These are essential for the initiation and maintenance of learning activities with respect to the choice of goals and the administration of resources employed to meet these specific goals. The second layer represents the regulation of the learning process and includes the use of metacognitive knowledge and skills in order to control the learning process and make it as efficient as

monitoring and evaluation of one’s own learning behavior (Schraw, 1998). The third layer, describing the regulation of processing modes includes cognitive learning strategies that include domain-specific knowledge about the proper application and effectiveness of cognitive learning strategies for certain tasks in specific situations (Boekaerts, 1999).

Figure 1.1: The Three-layered Model of Self-regulated Learning by Boekaerts (1999).

These three fundamental components of self-regulated learning can be found in most of the current models, although slightly differently conceptualized and defined in each model.

The first two components, ability to regulate the self and the ability to employ metacognitive knowledge, are thought to be general skills applicable to all domains. The third layer, that is, the use of cognitive strategies, is very domain specific in nature. In this dissertation, we focus on students’ strengths in the area of general abilities, namely on the regulation of the self (Study I and Study II) and on the use of metacognitive strategies (Study III).

1.1.1.1 Regulation of the self

The initiation of a learning process and the maintenance of this learning process require general skills such as the ability to regulate one’s own goals, motivation and emotions (Boekaerts, 1999). This includes a variety of strategies needed to regulate specific aspects of the self, namely choosing one’s goals, regulating motivation, and fostering specific positive emotions or coping with specific negative emotions. The relation between students’ goal

orientation and motivation towards their learning behavior and success has already been especially well researched (Boekaerts, 1999; Boekaerts & Niemivirta, 2005), additionally the relation between academic emotions and learning behavior has been the focus of recent research (Pekrun, Goetz, Titz, & Perry, 2002).

Study I and Study II presented in this dissertation focus on one specific aspect of the regulation of the self, namely on the regulation of the self in terms of coping with a specific negative emotion, boredom. Although there is empirical proof that boredom is one of the most often experienced negative emotions in the classroom (Goetz, Frenzel, Pekrun, & Hall, 2006;

Larson & Richards, 1991), there is remarkably little theoretical or empirical attention on how students’ cope with this negative emotion (Vodanovich, 2003b). However, boredom in the classroom as well as boredom at the workplace has been connected to many negative correlates, such as drop-out rates (Bearden, Spencer, & Moracco, 1989; Farrell, Peguero, Lindsey, & White, 1988; Tidwell, 1988; Wegner, Flisher, Chikobvu, Lombard, & King, 2008), truancy (Sommer, 1985), and deviant behavior (Wasson, 1981) and job dissatisfaction, absenteeism, and lack of loyalty to the organization (e.g., Kass, Vodanovich, &

Callender, 2001) in classrooms and the workplace, respectively. Although it might be seen as a teacher’s job to provide non-boredom inducing learning environments and instead design interesting and activating classroom settings, it is probably impossible for any teacher to prevent all students from being bored at all times. Further considering that boring settings likely happen at some point in most jobs, it seems to be a very important part of regulating the self to learn to cope with such an omnipresent emotion as boredom efficiently.

1.1.1.2 Regulation of the learning process

Motivational and emotional strategies are compulsory to define learning goals and protect the learning process against personal and situational disruptions. However, arguably more essential for the learning process itself is the attainment of metacognitive knowledge in the service of organizing one’s learning in such a way that domain-specific knowledge and skills can be acquired (Boekaerts, 1999). Current models of self-regulated learning (e.g., Boekaerts & Niemivirta, 2005; Borkowski, 1996; Pintrich, 2005; Winne & Hadwin, 1998; Zimmerman, 2005) agree on the central role metacognitive strategies play, and accentuate their relationship with respect to self-regulated learning (Puustinen & Pulkkinen, 2001). In Study III, we focus on students’ use of metacognitive strategies in terms of

A large number of metacognitive regulatory skills have been specified in the literature (Schraw, 1998), yet researchers generally highlight only three essential metacognitive strategies as important for regulating any learning process, namely planning, monitoring and evaluation (Boekaerts, 1999; Schraw, 1998; Spoerer & Brunstein, 2006).

Planning refers to the setting of goals followed by the selection of adequate cognitive strategies and appropriate allocation of resources, such as time needed, to achieve these goals.

Monitoring refers to being aware of one’s level of comprehension and performance on the task. As one monitors results, they often simultaneously use evaluation to correct learning problems and adjust planning as needed (Boekaerts, 1999; Schraw, 1998). While all three strategies are important components of self-regulated learning, the metacognitive strategy of monitoring seems to be of special interest. Winne and Hadwin (1998) include an omnipresent metacognitive monitoring process in their model of self-regulated learning. Within this model monitoring can provide feedback during any phase of the self-regulated learning process, thus it is accountable for subsequent regulation. Although dispositional and situational factors influence their application, such that students are unable to use them in the same way and with the same efficiency in all domains (Boekaerts, 1999), metacognitive strategies and skills are assumed to be domain general in nature.

1.1.2 Traits and States in Self-regulated Learning

Intra-individual self-regulation can differ according to the goal being pursued, as some goals activate self-regulated learning, whereas others retard it (e.g., Boekaerts, 1999;

Boekaerts & Niemivirta, 2005; Pintrich, 2005; Zimmerman, 2005), the domain specificity of the assessment of self-regulated learning gains in importance here. Although the capacity for self-regulated learning might be dispositional in nature, competing goals might lead to the use of self-regulated learning skills within one domain, while neglecting to employ the same skills in another domain. Thus, Boekaerts and Niemivirta (2005) distinguish between optimal and non-optimal conditions for self-regulated learning. Optimal conditions refer to situations in which an opportunity for learning and the felt necessity for learning come together.

According to Winne and Hadwin (1998), properties of both an aptitude and an event are combined in self-regulated learning. Students’ ability to self-regulate their own learning differs in these two aspects. First, they can differ in ‘aptitude’, relatively stable personality features that have an impact on self-regulated learning (Winne & Perry, 2000), such as students’ volition and ability. But these dispositions are not the only factors that are of

consequence for regulation; the environment can also facilitate or disrupt effective self-regulated learning (De Corte, Verschaffel, & Masui, 2004). Situational factors, or ‘events’, contribute to regulation in that situational components affect the occurrence of self-regulated learning.

Consistent with the concepts of aptitudes and events are the more common methodological concepts of traits and states. These concepts were established to explain two different potential sources of variability in psychological attributes, namely inter-individual differences due to dispositional factors and intra-individual differences caused by situational factors (Steyer, Ferring, & Schmitt, 1992). With respect to students’ classroom experiences and behavior, in order to more accurately assess students’ self-regulation of learning it is essential to regard traits as well as states as possible antecedents. Spielberger (1972) identifies personality traits as relatively stable inter-individual differences pertaining to one’s tendency to perceive certain situations in a specific manner or to behave in these situations in a predictable way (see also Cattell & Scheier, 1961). Conversely, the concept of personality

‘states’ is relatively unstable in nature as it depicts intra-individual differences in reaction to specific stimuli (Steyer et al., 1992). Traditional approaches to conceptualizing and measuring regulation behaviors have most often regarded regulation strategy use as a notably stable and dispositional trait (e.g., Pintrich, Smith, Garcia, & McKeachie, 1993). Nevertheless, as a complement to this trait approach, which is well-suited for evaluating the overall conceptual structure and personality correlates of regulation strategies (see Study I), more recent evaluation methods attempt to additionally incorporate situational aspects of coping behavior (e.g., the experience sampling method, Csikszentmihalyi & Larson, 1987; Hektner et al., 2007; Study II and Study III). This approach meets the demand for more naturalistic and empirically valid methods that result in a more dynamic and diversified picture of the nature of self-regulated learning (Puustinen & Pulkkinen, 2001).