Socially Constructed Self-Regulated Learning and Motivation Regulation in Collaborative Learning Groups
by Sanna Järvelä & Hanna Järvenoja - 2011
Background/Context: Most of the earlier empirical findings deal with motivation regulation in individual learning situations. This study identifies higher education studentsï¿½ socially constructed motivation regulation in collaborative learning and stresses that regulation of motivation is crucial in socially self-regulated learning because motivation is constantly shaped and reshaped as the activity unfolds.
Purpose of Study: The purpose of the study is to identity higher education studentsï¿½ socially constructed motivation regulation in collaborative learning This was studied by collecting data about the studentsï¿½ (N = 16) experiences of situation-specific social challenges in collaborative learning groups and observing what the students do to overcome these challenges.
Research Design: The study is a qualitative, multimethod study. Three methodsï¿½namely, adaptive instrument, video-tapings, and group interviewsï¿½were used to assess the individual- and group-level perspectives on those situations that the students felt were challenging and thus possibly activated joint regulation of motivation.
Conclusions: Motivation regulation can be identified as a socially constructed activity, and the importance of regulation of motivation in socially self-regulated learning is discussed.
The nature and assumptions underlying self-regulation in learning (SRL) have been widely discussed (e.g., Winne, 1995; Zimmerman, 1989) and, more recently, related to motivation and emotion in learning environments (e.g., Boekaerts & Corno, 2005). The theorys main topics concern how learners develop learning skills and how they can use learning skills effectively. Studying effectively by self-regulating learning is itself a skill powered by will, that is to say, directed and regulated by motivation.
Regulation of motivation has become a key feature within several social cognitive models of self-regulated learning (Boekaerts & Cascallar, 2006; Wolters, 2003). Zimmermans (2000), and Boekaertss (1996) models depict students understanding and active management of their own motivational processing as an important part of self-regulated learning. Motivation or motivational processing can be the target of students efforts to plan, monitor, control, and reflect. These efforts are thought to contribute to students increased attention, effort, and persistence, which ultimately lead to improved learning and higher achievement. Self-regulated learning is a social cognitive construct rooted in individual perspectives rather than socially constructivist theories. This study involves adapting the construct to a social constructivist stance. Currently, there is an increasing interest in considering self-regulated learning processes at the social level with reference to concepts such as social regulation, shared regulation, and coregulation (Hadwin & Oshige, 2006; Järvelä, Volet, & Järvenoja, 2010; McCaslin, 2004).
Conceptualizing self-regulation as a dual individual-social phenomenon calls for the integration of self-regulation, as an individual psychological concept, within the social, shared, and interactive processes of learning (Järvenoja et al., 2009). Such an approach is critical for understanding productive engagement and participation in real-life social learning environments. Our proposal is based on the assumption that in collaborative learning, individual group members represent interdependent self-regulating agents (cognitive angle) who at the same time constitute a social entity that creates affordances and constraints for group and individual engagement (situative angle). It is our contention that a situative angle focusing on group processes is necessary to capture the social construction and enactment of motivation, but it needs to be complemented by a cognitive angle, which represents the mediating role of individual members cognitions, metacognitive reflections, and interpretations. This complementary approach views social and psychological processes as occurring concurrently, and in need of joint consideration to further our understanding of motivation in collaborative learning.
Learners adaptation to social learning situations, such as sharing knowledge and maintaining coordinated activity (Roschelle & Teasley, 1995), requires cognitive, motivational, and socioemotional skills that are different from, and often more challenging than, those used in more conventional and well-structured learning situations (Winne & Perry, 2000). Despite the centrality of the social context in models of SRL (Zimmerman & Schunk, 2008), a need has arisen that must be made clearer: explaining precisely the role of social and contextual influences on the variety of phases of SRL, exploring the critical phases of self and the social in the strategic regulation of learning, and developing more precise language to describe what we mean by the social in theory and empirical research about SRL. Taking on these challenges, this article aims to investigate socially constructed self-regulation, focusing especially on the regulation of motivation in collaborative learning.
STRATEGIC REGULATION OF MOTIVATION IN SELF-REGULATED LEARNING
In spite of increasing interest in explaining the role of social and contextual influences of self-regulation in learning (Corno & Mandinach, 2004), there is great diversity as to where the social is positioned in SRL, varying from individual constructivist to more social constructivist perspectives on learning (Meyer & Turner, 2002).
A sociocognitive perspective to SRL focuses on an individual as a regulator of a behavior. SRL refers to the process of becoming a strategic learner by regulating an individuals cognition, motivation, and behavior to optimize learning (Schunk & Zimmerman, 1994). Social context affords opportunities for modeling, guided practice, and instrumental feedback, each of which plays an essential role in the self-regulated learning process (Zimmerman, 2000). In the sociocognitive perspective on SRL, group processes are not the focus of the analysis, but the unit of analysis is usually the individual. Data are often collected about aspects of self-regulated learning, such as individual performance, strategies, efficacy, behaviors, goal setting, and self-evaluation.
Conceptualizing SRL learning as a coregulation has been influenced by sociocultural theory, which emphasizes gradual appropriation of sharing common problems and tasks through interpersonal interactions (Hadwin, Wozney, & Pontin, 2005; Karasavvidis, Pieters, & Plomp, 2000; McCaslin & Hickey, 2001). The sociocognitive models emphasize that even if SRL can be assisted by external modeling and feedback, it develops within the individual. In contrast to this perspective of SRL, coregulation emphasizes a shifting in who actually does the regulation for the student. In this perspective, the appropriation of self-regulated learning means examining changes in interaction and exchange. Because coregulation relies on scaffolding and intersubjectivity, it involves sharing facts, ideas, and explanations of plans, goals, and activities around the joint task. Building on Winne and Hadwins (1998) sociocognitive models of self-regulated learning, Hadwin et al. (2005) have reframed a sociocultural model of self-regulation that emphasizes the complex interplay between the learner and the social context that supports and frames that task. Hadwin et al. (2005) referred to self-regulatory ownership and its transition from teacher to student during naturalistic instructional situations. As students are enculturated in the task and task context, teacher regulation should give way to shared responsibility for regulating learning; this is what is referred to as coregulation.
A third perspective to look at is how the regulation process can be framed in terms of shared cognition and recent research on collaborative learning, which is, in essence, the co-construction of shared understanding (Roschelle & Teasley, 1995). This is collective regulation in which groups develop shared awareness of goals, progress, and task toward co-constructed regulatory processes, thereby sharing regulation processes together as a collective processes. In this respect, an important conceptual and empirical focus of research has been the question pertaining to how learners build a shared understanding of a task or a learning environment (Resnick, Levine, & Teasley, 1991). According to Schwartz (1995), it is the effort toward shared understanding that constitutes the real motor of collaborative learning: the intrinsic effort of an individual to understand what the other means drives cognitive and dialogic activities that in turn enable cognitive changes in this individual. Reaching a shared understanding among the participants is one of the central elements in successful collaboration (Teasley & Roschelle, 1993). Also in social psychology, Thompson and Fine (1999) presented an approach to socially shared learning with key processes, such as immediate interaction, reciprocal influence of processes between individuals and groups, and goal-directed behavior. It is obvious that perspective taking, mutuality, and sharing understanding are coprocesses of motivation regulation in socially shared learning. As Gehlbach (2004) argued, perspective taking is more than a cognitive ability, it also includes a motivational component. Being able to understand the perspective of another individual and being motivated to engage the ability are critical for navigating most social situations. Motivation and emotion regulation are commitment resources for successful perspective taking.
Empirical studies on socially shared regulation have examined individual regulatory processes as part of socially constructed learning. The individual is present in the analysis as part of a collective entity because motivation is conceptualized as a process of engagement and participation in a social activity, which is an interaction of a mental state within a situation (Järvenoja et al., 2009). Thus, socially shared regulation is examined in a group of shared regulators, and individual regulation is always studied in relation to others and to the group regulation. Most of these studies dealing with self-regulation (Hadwin, Oshige, Cress, & Winne, in press), metacognition (Hurme & Järvelä, 2005; Iiskala, Vauras, & Lehtinen, 2004), or motivation and emotion regulation (Järvelä, 1995; Järvenoja & Järvelä, 2005; Salovaara & Järvelä, 2003) are conducted in technology-based learning environments where social exchange and co-construction can be more easily traced with the help of technology. The research focuses on collective interactions and collaboration rather than individual cognition or transfer of knowledge through interpersonal interactions.
STRATEGIES STUDENTS ACTUALLY EMPLOY TO REGULATE THE MOTIVATIONAL STATE
Wolters (2003) defined regulation of motivation generally as the process through which individuals purposefully manage either their level of motivation or the underlying processes through which their motivation is determined. Motivational regulation includes thoughts and behaviors through which students act to initiate, maintain, or supplement their willingness to start or to make an effort toward completing academic activities. According to Kuhl (1985), theories of motivation emphasize the subjective control that various beliefs and attitudes have on student choice, effort, and persistence, whereas the regulation of motivation concerns students active control of the processes that influence these outcomes.
Regulation of motivation is conceptually distinct from motivation even though it may be difficult to differentiate empirically between these two phenomena, especially given that the relation between students motivation and motivation regulation is mutual (Järvenoja & Järvelä, 2005; Wolters & Rosenthal, 2000). Wolters (1998, 2003) has reviewed several strategies for regulation of motivation and identified key activities that can be considered strategies for regulating motivation, and has evaluated the evidence linking these strategies to students motivation, cognitive engagement, and achievement. However, Wolterss motivation regulation strategies focus on individual motivation regulation and, based on these concepts, Järvelä, Järvenoja, and Veermans (2008) modified the framework to adapt to a socially shared learning situation and characterized socially shared strategies such as socially shared, goal-oriented talk, efficacy management, handicapping of group functioning, task structuring, social reinforcing, and interest enhancement. Järvenoja et al. (2009) argued that in a social learning context, motivation is an outcome but is also generated as a coregulated occurrence. In the other words, the social context in which engagement and participation take place coregulates each participants engagement and participation in the activity. Regulation of motivation is crucial in socially self-regulated learning because motivation is constantly shaped and reshaped as the activity unfolds.
Although prior work has identified a number of motivation regulation strategies that students use to control their motivation, Winne and Hadwin (2008) argued that we still know very little about how students become strategic learners who can adapt tactics and strategies to a range of learning situations. They pointed out a need to explore the differences between analysis of a set of studying episodes and actual online decisions students make while trying to construct efficient strategies for academic achievement. Current research on motivation regulation gives reports on students perceptions of motivated states and goals for future behavior (Pintrich, 2003), but few explain whether and how these intentions are realized and how students implement the strategies in practice. There are research findings explaining what students might use to regulate motivation and emotion (e.g., Wolters & Rosenthal, 2000), but what is still missing is how students actual use of specific strategies for regulating motivation calibrate to their use of strategies (cf. Winne & Jamieson-Noel, 2002).
The aim of this study is to identify students socially constructed motivation regulation in collaborative learning when the students experience situation-specific social challenges. This study investigates these questions: (1) What kinds of challenges do the students have in collaborative learning? (2) What do the students do to overcome these challenges in practice?
METHOD AND ANALYSIS
Because the essence of the process of self-regulation is a change that focuses on activity, it is important that the unit of analysis help to uncover the dynamic, nonlinear nature of student self-regulation. Turner (2006) suggested measuring self-regulation as a dynamic process rather than a linear one and including measures of the context such as task, interpersonal contacts, and community norms. In this study, the focus of the analysis is on a collaborative activity shifting from what the person is thinking to what the person is doing. The data analysis has three perspectives: identified challenges (what a person is thinking), activated motivation regulation strategies in collaboration (what a group is doing), and explanations of how self-regulation in learning was socially constructed (what a group is thinking).
In this study, four groups of first-year graduate students worked with three different collaborative learning tasks in an educational psychology class. The group work was part of a mandatory course, which was assessed as either passed or failed. The students were informed about the study at the beginning of the group work and were informed that participation in the study was voluntary. The students (N = 16) were randomly assigned to groups of four, so that each group had both female and male students. The structure of the three different tasks varied in terms of the form and level of instructions and hence in terms of how much responsibility the groups themselves had to take for the organization of their group work. During all three tasks, the students were presumed to collaborate in order to reach a shared goal. The tasks were performed during two to four collaborative group sessions, each lasting about 1.52.5 hours.
The first task was designed to create cognitive conflicts between the group members and to support the shared reconstruction process in a group. The aim of this task was to stimulate student argumentation and negotiation in order to reach a shared understanding in cognitively and emotionally conflicting situations (Dillenbourg & Traum, 2006). The students were first asked to read articles dealing with the same theme but with a different view on it (e.g., what is learning from the point of view of problem-based learning/cognitive theory and educational practice?). Grounded in these articles, the students filled out a structured form in which they elaborated their conceptions of the theme. The forms were used to stimulate a group discussion aimed at a unified conception of the theme. After the discussion, the group members completed the same form once again. The final form represented their unified conception of the theme and was the eventual outcome of the task.
The aim of the second task was to encourage the students to discuss and share their authentic real-life experiences and expertise gained from different theoretical articles (The Cognition and Technology Group at Vanderbilt, 1990). This was expected to generate reciprocity between the students and regulation of their own motivation and emotions. The task was not as structured as the first task with respect to the support for cognitive processes. However, the progression of the group work was preplanned. It structured the different phases of the task and helped the students to organize their group work. Again the students were first asked to read articles dealing with the theoretical basis of the phenomenon. The perspectives of the different articles varied, and each group member read a different piece. The reading preceded construction of a real-life case example of the phenomenon that the group members were studying. For example, one of the groups dealt with the question, What motivational factors may affect the learning and commitment of underachievers in primary school? Once an agreement was reached regarding the case, the group members analyzed the case. Each member was expected to contribute to the analysis based on his or her unique expertise gained from the different articles (Brown & Campione, 1994). After the joint analysis was completed, the case was commented on by another group. Based on these comments, the group finalized the case analysis and created suggestions for how this case could be approached and solved in practice.
The third task was the most open and least structured of all the tasks. Only general themes and some basic materials were provided to the students. The groups were advised to study the theme (e.g., conceptual change or metacognition) so that they were able to create a scientific poster as a result. The group members had to first agree on the theme they wanted to study and then plan how to organize their collaborative work. The students were responsible for the process, but they were provided support with content-related issues if needed.
After each of three collaborative learning tasks, each of the 16 students filled out a task-specific Adaptive Instrument for Regulation of Emotions and motivation (AIRE), which resulted altogether in 48 responses (16 students x 3 tasks). The AIRE was designed to assess each group members specifically situated experiences of socioemotional challenges and the use of self- and socially shared regulation strategies within a group, alongside generic dimensions related to the motivational regulation of both individuals and groups (Järvenoja et al., 2009). The content of the instrument was the same for every evaluation point, and it aimed to identify challenges that the students experienced during the collaborative tasks (what a person is thinking). With AIRE, the challenges were presented in the form of 14 scenarios. These scenarios include a general description of the challenge and a few concrete examples of the possible situations.
First, the students were instructed to indicate, from all 14 scenarios, whether they had experienced the same type of situations as described in scenarios during the group work using a 5-point Likert scale ranging from 0 (did not happen at all) to 4 (it was a big challenge). The students were then asked to indicate which scenario they considered to be the biggest socioemotional challenge to their group during the collaborative task. This resulted altogether in 45 responses of the most challenging situations among three collaborative learning tasks (16 students × 1 challenge × 3 tasks, less 3 missing).
The analysis proceeded by first indicating how the students reported experiencing each of the 14 challenges through calculating the mode and range of reports from each scenario. Next, because in this study, the aim was to specify the type of socioemotional challenges the students actually had confronted during collaborative learning, the analysis focused on the 45 reports in which the students prioritized the biggest challenge. For this analysis, the 14 challenge scenarios were divided into five different thematic categories, and a general frequency distribution (including all three tasks) of the students responses between these categories was decoded. The thematic categories were challenges in personal priorities, work and communication, teamwork, collaboration, and external constraints. The categories were constructed based on a review of research literature that has documented different kinds of social challenges in collaborative learning ranging from perceived incompatibility in group members personal characteristics and priorities (Webb & Palincsar, 1996) to challenges that emerge from social relationships and activities required in intense collaboration, such as the need to create a common ground in shared problem solving (Mäkitalo, Häkkinen, Järvelä, & Leinonen, 2002; for more details of the instrument, see Järvenoja et al., 2009). The descriptions of these common characteristics and an example of one scenario in each category are presented in Table 1.
Table 1. The Thematic Categorization of 14 Challenge Scenarios From the Adaptive Instrument and Descriptions of the Common Characteristics of Each Category
Another method for data collection was video recording students group work. The video data were aimed at identifying the students actual group-level regulation strategies in those situations in which these strategies were employed (what a group is doing). Altogether, there were about 28 hours of video recordings from the four groups (Group A = 8 h 18 min; Group B = 7 h 40 min; Group C = 5 h 38 min; and Group D = 6 h 5 min). The analysis proceeded by first sequencing the whole set of data into episodes that reflected one phase or activity in group work and describing especially the motivational aspects. Thus, a single episode can be described as a meaningful motivational contribution and is presented in short descriptive narrative. The time frame with these episodes was, on average, about 5 minutes but varied from about 2 minutes up to almost 30 minutes. This was because the focus in sequencing and describing the data was on one motivational indicator/aspect at a time, in addition to the structure of the activity. This resulted, on the one hand, in long periods of time when students engaged in a certain type of activity, and on the other hand, in sudden shifts in motivational indicators when the nature of the activity was more dynamic.
From these descriptive narratives, the group members regulation of motivation was identified and coded into eight categories, which were originally based on Wolterss (1998, 2003) motivation regulation strategies but were modified to adapt to situations of socially shared learning (Järvelä, Järvenoja, & Veermans, 2008). The modification (resulting in the categories shown in the appendix) was done during the first analyzing phase. Two researchers read the transcriptions and combined the theory-based ideas with the data and vice versa. In the second phase, two independent codings were conducted. During the third phase, the two codings were compared, and contradictory codings were searched. Because the amount of contradictory codings was less than 15%, those codings were taken under joint analysis, and codings were negotiated until a unified solution of the codings was reached.
Semistructured group interviews were conducted right after the collaboration sessions, and each group was asked to discuss their positive and negative experiences in their process of collaboration (what a group is thinking). They were also asked to explain their reasons for their emerged thoughts and actions in practice. For example, the group members were asked to elaborate on the kind of challenges their group had faced and to describe the social dynamics related to challenging situations within the group. The interview protocol included four main questions: (1) What do you think was the main objective/goal during the group work? (2) What was the main/biggest challenge for your group? Why? (3) What did you personally do to overcome challenges? (4) What did you do as a group to overcome challenges? Each question also included several probes that could be used to elaborate the topic in more detail. Students in each of the four groups were interviewed three times, to total 12 interviews altogether. The interviews were tape-recorded, and each interview lasted about 3040 minutes.
Finally, the analyses of three different data sets (AIRE, video observations, and interviews) were drawn together in a cross-data summary. The aim was to explore how different methods provide a complementary view of socially constructed self-regulated learning in collaborative learning groups. The summary does not aim to be conclusive, but to illustrate the interconnectedness of different data sources in socially constructed self-regulated learning.
For the purpose of the cross-data summary, the different data were examined from a perspective of three different tasks. In this way, it was possible to link the students self-reported challenges (from the AIRE) to actual collaborative tasks and to the identified strategies from video data, and to interview data examples. First, the frequency distribution of the reported challenges was defined for each task. Second, the group-level regulation strategies (identified from the video data) were also matched to the respective task. In this way, the challenges that the students experienced during the task and strategies the students employed for the task could be linked. Finally, the interview data were used to illustrate how the students explained the situation in which they activated motivation regulation strategies. To combine the three different data sources together, a table was created in which each of the three tasks was presented in its own column, and selected results from each data source were presented in one row. First, the most frequent challenges were gathered to this cross-data summary table. The challenge type was included in the summary if the frequency of the coding entries for one collaborative learning task exceeded 4. This cut-off was based on the fact that when the frequency of the certain type of challenge was 5 or 6, there had to be reports from more than one group (each group was composed of 4 students). Also, when frequency exceeded 4, it included over one third of all 16 students indications of the biggest challenge for one task (two responses were missing from the second task and one response from the third task). A decision to include the regulation strategy in the summary table was made if the frequency of the coding entries exceeded 12, which was half of the largest frequency in one regulation strategy class within a task.
Third, illustrative interview examples were chosen to back up the video data and get students explanations of how self-regulation was socially constructed. Group interviews were chosen because the interest was not in individual regulation strategies, but socially constructed strategies. The interview data were transcribed and read through many times by two researchers. Especially those episodes were searched that describe the group members joint discussions and emergent thought dealing with specific situations in a collaborative learning task.
IDENTIFIED CHALLENGES IN COLLABORATIVE LEARNING
After every three collaborative group tasks, the students filled out the AIRE and identified the experienced socioemotional challenges from 14 scenarios. In 13 out of 14 scenarios, at least some students reported that they had experienced a similar situation and that these situations had been socioemotionally challenging at some level. The range in the students reports varied between 0 and 4 depending on the scenario. However, the mode of the reports for each challenge type was 0 or 1, indicating that in most of the cases, the challenge was not experienced at all or was only a small challenge. Because the aim in this study was to specify the type of socioemotional challenges the students actually had confronted during collaborative learning, the analysis focused next on the 45 scenarios that the students prioritized as the most challenging. This analysis shows that the students indications of the most challenging experiences were spread between 13 scenarios (18 indications per scenario), and hence, the biggest challenges reported varied also among all five of the challenge types. Figure 1 illustrates how the different challenges were reported by the students throughout the three tasks. Teamwork was the most often reported challenge type (17 indications). The next were collaboration, work and communication, and personal priorities (9, 8, and 7 indications, respectively). External constraints was indicated four times.
Figure 1. Identified challenges by the individual students (N = 16) throughout the three collaborative learning tasks
THE STUDENTS ACTIVATED SOCIALLY SHARED MOTIVATION REGULATION STRATEGIES IN COLLABORATION
The focus of the video data analysis was to identify different socially shared regulation strategies that the students actually employ when they face challenging situations. The video data indicate that the students were able to activate a variety of motivation regulation strategies. During the three collaborative learning tasks among the four groups, task structuring (49 coding entries) and social reinforcing (40 coding entries) were the strategies which most often emerged. Self-efficacy management, interest enhancement, and socially shared goal-oriented talk were observed 28, 27, and 22 times, respectively. Use of the handicapping of group functioning strategy was observed for seven episodes. In Table 2, frequencies of each strategy and examples of each socially shared regulation strategies are presented.
Table 2. The Students Activated Socially Shared Motivation Regulation Strategies in Collaborative Learning
The aim of the examples is to illustrate how the characteristics of a specific situation determine whether the group members activate regulation of motivation. In other words, both the reason to use certain motivation regulation strategies and the actual observed motivation regulation actions define whether the actions can be considered regulation of motivation. When regulation of motivation is studied in real-life situations, the purpose of using the strategy is an inseparable part of the regulation process. For example, task coordination is a cognitive strategy and part of successful collaboration (Dillenbourg, 1999), but it also contributes to the students task engagement in those situations in which they, as a group, try to reinforce task engagement and activate their collaboration. For instance, an example of the task-structuring strategy in Table 2 illustrates how the students use this type of strategy as a motivation regulation strategy to overcome a motivationally challenging situation (e.g., passivity leading to giving up the task).
The results also show that in a socially challenging learning situation, an individual group member can play a leading role in regulation of the groups motivation, as the example of the social reinforcing strategy shows (see Table 2). This type of regulation was observed when the strategy aimed at shaping the groups motivation and joint behavior. Efficacy management strategy was identified in those situations in which the students monitored or controlled their competence and strove for the joint task. One example, which is presented, of this strategy is an illustration of how the students purposefully control and reshape their group focus and behavior toward the joint task.
The motivation regulation strategies, which were included in the interest enhancement category, reflected how the students increase their intrinsic motivation or situational interest while completing the task. The example in Table 2 shows how the students intentionally create a situation in which they share their individual experiences in order to enhance their shared interest toward the task. Socially shared goal-oriented talk category codings involve episodes in which the students articulate their reasons to persist in the task or complete the task. In the Table 2 example is an illustration of a situation in which the group is engaged in the task but unable to go ahead until one of the group members explicitly directs the group members focus to the joint goal by starting a goal-oriented dialogue. The distinction between interest enhancement and socially shared goal-oriented talk can be found from the contextual features of the situation. The first aims to engage the group members in on-task activities, while the latter aims to shape the on-task activities.
The handicapping of group functioning strategies have been reported in many self-regulation researches focusing on individuals motivation and learning. With these groups of students, the findings dealing with this strategy were minor but identifiable also from a socially shared and group perspective. Although employment of these strategies was rare, their meaning was considerable because it substantially affected the groups learning and collaboration. The example presented in Table 2 points out that when the group members employed this type of strategy, it led them to give up the group activity instead of striving to restore the goal-oriented motivation. However, it is important to state that with these students, the use of handicapping of group functioning motivation regulation strategies was situation specific, and each group was able to reestablish its motivation and group work later.
STUDENTS EXPLANATIONS OF HOW SELF-REGULATION WAS SOCIALLY CONSTRUCTED
From the interview data, 39 episodes (12 interviews altogether, two to five episodes found per interview) were identified in which the students discussed those situations in which they activated motivation regulation strategies. The criteria for searching the episodes were such that the group discussion clearly indicates that they shared an awareness of a problem in their engagement and as a group activated and shared regulation processes to control the situation. For example,
Kalle: It is actually so, that when we were moving to the side-track . . . it was always someone who said that, hey, should we go on for our task and we all sharpened our mind. . . and then we were back in work again.
Marko: It didnt matter who said it, it worked anyway.
Other students: Yeah, thats right.
It is not possible to categorize the episodes; rather, the data are used to back up the video data analysis of identified motivation regulation strategies (see Table 3). The video data show that the students as a group activated especially motivation regulation in terms of social reinforcing, task structuring, socially shared goal-oriented talk, and efficacy management. The interview data illustrate that the students as a group explain how they socially share regulation during a collaborative task, and those situations characterize reciprocal and mutual interactions as well as joint understanding of the situation.
Drawing from the three data analyses reported previously, a cross-data summary was conducted to combine the results from different data sources. Table 3 collects all three data sourcesadaptive instrument responses, video data, and interview datarespectively. The purpose is to show which challenges the students experienced most and what they did to overcome those challenges, as well as show how they explained their actions in those situations.
First, the experienced challenges that the students identified in the adaptive instrument were situated to the specific collaborative task. After the first task, teamwork (six indications) and personal priorities (five indications) were reported the most of all reported challenges. After the second task, teamwork (six indications) was reported the most often. Finally, after the third task, challenges in collaboration were reported the most often, in addition to challenges in teamwork, which was again reported the most often (five indications both). These three challenge types were gathered in the cross-data summary in Table 3 because the frequencies of these challenges reached five or six indications.
Table 3. The Cross-Data Summary of Three Data Sources
From the cross-data analysis, it can be seen that the students experienced more challenges because of different personal priorities in the beginning of the collaborative work (first task), and, respectively, the indications of collaboration challenges increased toward the end (third task). Teamwork, as a reason for experienced challenges, was reported often throughout all three tasks. There also was no considerable change in how the students experienced the work and communication or external constraints, but these types of challenges were less frequent than teamwork.
The frequencies of motivation regulation strategies from the video data analysis were calculated for each of the three collaborative tasks. The coding frequencies varied from 1 to 24 entries to one regulation strategy within a task. The certain strategy was included in the summary table if there were more than 12 coding entries. Altogether, only two strategies exceeded this criterion. In the first task, social reinforcing was coded to 15 episodes. In the second task, social reinforcing was coded for 15 times, and task structuring was coded in 14 episodes. In the third task, task structuring was coded in 24 episodes.
Social reinforcing motivation regulation strategy appeared the most in the first two tasks. This was also the case when challenges in personal priorities and teamwork were indicated as the source for the experienced challenges. It is possible that these students aimed to solve the challenging situation caused by individual differences or lack of commitment by using strategies that strengthen their feeling of togetherness and joint engagement. In the example from task 1 (in Table 3), the students explain how they had created an atmosphere in which all students felt safe to express their ideas, in other words, their successful results of the efforts to reinforce a feeling of togetherness.
Toward the end of the collaborative work (second and third task), the observations of task-structuring strategies aimed at structuring the task and environmental conditions increased. With these students, this increased frequency is in line with their indications of challenges dealing with different aspects of teamwork and collaboration. In the interview examples from tasks 2 and 3 (in Table 3), the students describe their regulation processes that aim to maintain on-task behavior when the groups focus is distracted. This finding may make clear that when the challenges derive from collaboration instead of personal priorities, the students can focus on on-task activities instead of confirming and shaping their shared motivation.
The aim of this study was to identify higher education students socially constructed motivation regulation in collaborative learning. This was studied by collecting data of the students experienced situation-specific social challenges in collaborative learning groups and observing what the students do to overcome these challenges. Three methodsnamely, the AIRE, video-tapings, and group interviewswere used for reaching the individual- and group-level perspective on those situations that the students felt were challenging and thus possibly activated shared regulation of motivation. In this study, it was asked what kind of challenges the students have in collaborative learning. The cross-data summary sums up the different data and shows that the students experienced more challenges because of different personal priorities in the beginning of the collaborative work, whereas collaboration challenges increased toward the end. Challenges in teamwork were experienced throughout all three tasks. However, it should be noticed that this study did not explicate how the change in the experienced challenges was related to the differences between the differently structured tasks in addition to the experience to work together, which increased from the beginning of the first collaborative task until to the end of the last task.
It was also asked what the students do to overcome these challenges. The results show that socially constructed self-regulation emerged when students worked in collaborative learning groups and made consistent efforts to regulate their learning and engagement. Also, Kempler and Linnenbrink-Garcia (2007) found evidence of group self-regulation when sixth-grade students worked on group activities. In this study, it was seen that the students activated a variety of socially shared motivation regulation strategies, of which social reinforcement and task structuring were the most common. It was also found that the students shaped their use of motivation regulation strategies to fit the specific situated challenges. The findings support the application of motivation regulation strategies to collaborative learning but suggest that socially shared self-regulation has some specific features, such as that the groups motivation may have to be observed through the reciprocal interactions of the group members. This study shows that motivation regulation can be identified as a socially constructed activity. However, in an attempt to evolve our thinking of how motivation is socially constructed, methodological solutions play a major role. There is significant research literature (e.g., Boekaerts, 1996) indicating that cognitive and motivation strategies are intertwined aspects of self-regulated learning. When regulation of motivation is studied in real-life situations, each persons purpose for using the strategy is an inseparable part of the regulation process, and thus, data of each persons situation-specific cognitive actions may give more accurate information about the students motivation regulation. There is, though, a danger that conceptual accuracy of motivation and cognition will be blurred.
Many studies have reported higher education students regulation of effort and persistence in academic tasks (Zimmerman & Martinez-Pons, 1990), interest regulation (Sansone, Weir, Harpster, & Morgan, 1992), or regulation of goals (Wolters & Rosenthal, 2000). However, most of the earlier empirical findings deal with motivation regulation in individual learning situations. Currently, the discussion about the social in motivation and self-regulated learning is becoming active among researchers (e.g., Hadwin & Oshige, 2006). Järvelä et al. (2010) argued that in social learning, contextual motivation is an outcome but also generated as a coregulated feature. Regulation of motivation is crucial in socially self-regulated learning because motivation is constantly shaped and reshaped as the activity unfolds. This phenomenon, though, is not easy to provide evidence for empirically. The earlier findings dealing with motivation and self-regulation have mainly been received from quantitative self-reports, which may limit the ways in which students are able to focus on the dynamic processes of motivation regulation in a learning context (Murphy & Alexander, 2000). The students have been asked to think about challenging situations while they study and rate their possible motivation regulation strategy, but their actual challenge and their activated strategy have not been matched. Because students self-report about their behavior can be inaccurate, data are required about actual behavior to confirm what students self-report (Pintrich, 2003).
This research was supported by the Finnish Science Academy research Grant No. 110734. We thank the two anonymous reviewers for their valuable critiques of earlier versions of this manuscript.
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Motivation Regulation Strategies in a Socially Shared Learning Situation