Recognizing socially shared regulation by using the temporal sequences of online chat and logs in CSCL

Learning and Instruction 42 (2016) 1e11

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Recognizing socially shared regulation by using the temporal sequences of online chat and logs in CSCL €rvela €*, Jonna Malmberg, Marika Koivuniemi Sanna Ja University of Oulu, Finland

a r t i c l e i n f o

a b s t r a c t

Article history: Received 8 April 2014 Received in revised form 21 September 2015 Accepted 24 October 2015 Available online xxx

The increasing amount of empirical research shows that the role of regulatory processes is critical in CSCL and collaborative learning settings. However, the current conceptual definitions and specificity of the findings vary. This is most probably because of limitations in the methods investigating regulated learning in a collaborative learning context. This study aimed to provide empirical evidence for how selfand shared regulation activities are used and whether they are useful for collaborative learning outcomes. Eighteen graduate students worked in collaborative groups for seven weeks in a CSCL course and the data of this study focuses on three one week online collaborative learning phases in the course. Temporal and sequential analysis of chat discussions and log file traces were matched to find evidence about whether the students' collaboratively planned regulatory activities became shared in practice. The results show evidence that collaborative planned regulatory activities become shared in practice. The groups that achieved good learning results used multiple regulatory processes to support their learning and also reached shared regulation. The four microlevel examples demonstrate simplified patterns of the activation of self-regulation and shared regulation. In conclusion, individual socially shared regulation plays a critical role in successful collaborative learning. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Self-regulation Socially shared regulation Collaborative learning Log file traces Temporal and sequential analysis CSCL

1. Introduction An increasing amount of empirical research shows that the role of regulatory processes is critical in computer-supported collaborative learning (CSCL) and collaborative learning settings in general (Chan, 2012; Kempler Rogat, & Adams-Wiggins, 2014; Kempler Rogat, & Linnenbrink-Garcia, 2011; Saab, 2012; Volet, Vauras, & Salonen, 2009). The concepts of self-, co-, and socially shared regulation of learning have been used to describe these processes €rvela €, & Miller, 2011). In collaborative learning research, (Hadwin, Ja regulatory processes have typically been considered from a cognitive perspective and, thus, the definition has been linked to cognitive processes in knowledge co-construction (Hmelo-Silver & Barrows, 2008), the socio-cognitive dynamics of knowledge building (Zhan et al., 2007), knowledge convergence (Weinberger, Stegmann, & Fischer, 2007), and socio-cognitive and team-related aspects (Fransen, Weinberger, & Kirschner, 2013). What is

* Corresponding author. Department of Educational Sciences and Teacher Education, Learning and Educational Technology Research Unit (LET), P.O. BOX 2000, FIN-90014, University of Oulu, Finland. E-mail address: sanna.jarvela@oulu.fi (S. J€ arvel€ a). http://dx.doi.org/10.1016/j.learninstruc.2015.10.006 0959-4752/© 2015 Elsevier Ltd. All rights reserved.

important and different in the shared regulation of learning is that self-regulated learning theory extends the concept of learning beyond cognitive processes and outcomes, acknowledging the interactive roles of motivation, emotion, metacognition, and strategic behaviour in successful learning (Zimmerman & Schunk, 2011). Collaborative groups can be considered as social systems comprised of multiple self-regulating individuals who must at the €rvela €, Volet, & same time regulate together as a social entity (Ja €rvenoja, 2010; Volet, Summers, & Thurman, 2009; Volet, Ja Vauras, et al., 2009). Although self-regulation concerns individual adaptation, taking responsibility for one's own learning is an important aspect of collaboration. Co-regulation indirectly supports collaboration because individuals in the group are temporarily supported by one another to take personal responsibility for directing and adapting their behaviour, cognition, and motivation for the collective potential of the group. Although self-regulation and co-regulation can assist group members to engage productively in joint tasks, shared regulation is essential for optimizing collaboration. The socially shared regulation of learning refers to processes by which group members regulate their collective activity. This type of regulation involves interdependent or

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collectively shared regulatory processes, beliefs, and knowledge (e.g., strategies, monitoring, evaluation, goal setting, motivation, and metacognitive decision making) orchestrated in the service of a co-constructed or shared outcome (Hadwin et al., 2011; Hadwin, €rvela €, & Miller, 2015; in preparation). Ja However, the current conceptual definitions and specificity of empirical findings regarding the socially shared regulation of learning vary. This is most probably because data collection and analysis methods typically focus on either individual regulatory activities or social and collaborative interaction processes and are not able to capture the adaptation in between individual and group level processes of SRL. Even though there is a growing body of work focussing on the interaction between individual and social learning regulations (Perry & Winne, 2013; Volet, Summers, et al., 2009; Whitebread & Pino Pasternak, 2013), the analyses of individual perspectives as part of a shared, communal perspective are rare, which we consider a criteria for data collection in investigating the socially shared regulation of learning. This is because regulation in collaboration has been investigated in many forms and concepts and there are differences in analyses in terms of what is regulated. Some of the analyses have a focus on task performance and task engagement (e.g. Volet, Summers, et al., 2009), others focus on group regulation aiming to clarify how group members learn to collaborate (Fransen, Kirschner & Erkens, 2013), or function as a group (Kwon, Liu, & Johnson, 2014), and other focus on how individuals self-regulate in a group context (Kempler Rogat & Linnenbrink-Garcia, 2011). €rvela € (2014) discussed the advances of Recently, Molenaar and Ja temporal and sequential data analysis in self-regulated learning (SRL) research, as a potential way to increase our understanding of how learners use SRL in collaboration. The increasing use of technology to support the learning and interaction processes, as well as data collection, has provided a new source of data to trace learning processes and new data-driven analytical techniques are available (Reimann, Markauskaite, & Bannert, 2014). Researchers have started to view SRL as a series of events, which can be perceived as a process that unfolds over time in a certain order (Azevedo, Johnson, Chauncey, & Burket, 2010; Azevedo, Moos, Johnsson, & Chauncey, 2010; Schoor & Bannert, 2012). Still, Molenaar and J€ arvel€ a (2014) conclude that there is relatively little research about how groups and individuals in groups engage, sustain, support, and productively regulate collaborative social processes. Researchers are struggling to find the right level of granularity for one to understand the role, emergence, and development of a particular cognitive and metacognitive process during task performance, or a learning task while interacting alone, with a computer-based learning environment, or in a group of students (Azevedo, 2014). The aim of this study was to investigate the temporal sequences of self- and socially shared regulation of learning in CSCL, in order to provide empirical evidence about how self- and shared regulation activities are used and whether they are useful for collaborative learning outcomes. When considering what is regulation in learning, at least three criteria can be acknowledged which have methodological consequences when investigating the shared regulation of learning. First, the regulation of learning, whether self-regulation or socially shared regulation, has a cyclical nature as it interacts with context (Kitsantas & Zimmerman, 2002). Empirical research should target not only the occurrence of regulatory activities, but also how the regulation of learning changes over time (Zimmerman, 2008) and how it is affected by conditions that change over time (Winne, 2006; Winne, Zhou, & Egan, 2011). Second, regulation is not a spontaneous activity, and a need for regulation is required. Hadwin et al. (2011) argued that learning activities need to be authentic as well as optimally challenging in order to invite the regulation of

learning. That is, challenges noted by learners create opportunities for the regulation of learning. Third, in the shared regulation of €rvela € learning, individual and group-level processes are parallel (Ja et al., 2010; Volet, Vauras, et al., 2009). This enables the examination of congruence in the individual representations of potentially shared regulatory processes, as well as the examination of calibration amongst individual representations and shared regulatory processes (Hurme, J€ arvel€ a, & Merenluoto, 2014). In this study, we applied microanalytical protocols (Cleary, Callan, & Zimmerman, 2012) to identify traces of regulation (Perry & Winne, 2013) across different events involving online collaboration. Generally, microanalytical protocol refers to a highly specific or fine-grained form of measurement that targets behaviours or processes as they occur in real time across authentic contexts (Cleary et al., 2012). In the majority of existing studies, researchers use microanalysis to directly observe overt microlevel behaviours during authentic interactions. In studying the shared regulation of learning, we had two criteria: a) to understand the sequential aspects of regulated learning in different collaborative tasks, and b) to focus on the temporal sequences of individual and shared regulatory activities in collaborative interactions. To better capture the individual and shared regulatory activities, we utilized nStudy technology, with which we implemented learning activities and tools that represent critical aspects of self- and shared regulation of learning. We used trace methods that can inform how and when students are engaging in self- and shared regulation of learning in CSCL. 2. Shared regulation of learning in collaborative tasks When individuals work collaboratively, at least three types of regulated learning come into play (J€ arvel€ a & Hadwin, 2013; Winne, Hadwin, & Perry, 2013): (1) each group member takes responsibility for regulating his or her learning (self-regulated learning), (2) each group member supports peers in regulating their learning (co-regulated learning), and (3) the group comes together to collectively regulate learning processes in a synchronized manner (shared regulation of learning). In all, shared regulation refers to group members' deliberate and strategic adaptation during phases of collaborative planning, task enactment and reflection. It involves multiple individual perspectives fine-tuning of cognitive, motivational and emotional conditions as needed. Importantly, in collaborative learning individual self-regulation in the service of the group task is necessary for productive collaboration to occur. Shared regulation does not imply dissolving or devaluing of individual regulation in collaboration nor does it equate with collective sameness (Hadwin et al., 2015; in preparation). For example, group member's strategic adaptation to the collaborative task presumes socially shared task understanding, so that groups negotiate shared perceptions or interpretations of the collaborative task. They also draw on their collective awareness of task conditions, contexts, and target outcomes to set shared goals, standards and plans, which can be called socially shared planning for strate€rvela €, gically approach the task (Miller, Malmberg, Hadwin & Ja 2015). Socially shared strategy use refers to choosing ways on how to enact the plans in action and using strategies such as €rvenoja, & reviewing or summarizing (Malmberg, J€ arvel€ a, Ja Panadero, 2015) and socially shared motivation targets to restore group's motivation or emotional balance or reinforce the group beliefs of competence within a situation, such as social reinforcing, €rvenoja & Ja €rvela €, efficacy management, interest enhancement (Ja 2013). €rvela € (2014) conducted a qualitative Recently, Panadero and Ja review to analyse empirical evidence that supports the socially shared regulation of learning. After analysing a large amount of

€rvela € et al. / Learning and Instruction 42 (2016) 1e11 S. Ja

literature, which was found by searching the PsycINFO, ERIC, and Thomson Reuters Web of Knowledge databases, only 24 studies were found that included empirical research on socially shared €rvela € concluded that there is enough regulation. Panadero and Ja empirical evidence to support the existence of the phenomenon known as the socially shared regulation of learning. Their review showed that two types of collaborative regulation can be identified. First, a less optimal and unbalanced regulation of learning, usually characterized by one student taking the lead role, is known as coregulation (Volet, Summers, et al., 2009). Second, a more optimal approach to collaborative learning involves a process in which group members organize the regulation of activities jointly (Grau & Whitebread, 2012). There is some evidence that the socially shared regulation of learning produces a higher performance than coregulation or self-regulation (author et al., 2013; Volet, Vauras, et al., 2009), but more research is needed that confirms these findings. For example, Schoor and Bannert (2012) found no difference between high-achieving and low-achieving dyads in the frequencies of regulatory activities, which contrasts with the results of Winters and Alexander (2011), who showed a positive relationship between regulatory activities and performance. In all, the current conceptual definitions and specificity of the findings vary, and a detailed picture of the dynamics of the individual and social processes of socially shared regulation is not yet clear. The contextual conditions that are required to promote the socially shared regulation of learning in CSCL are also not clear. 3. Sequential and temporal analysis of the data Earlier attempts to study the social processes of self-regulation in collaborative tasks have sought to understand the process€rvela €, Ja €rvenoja, & oriented nature of self-regulated learning (Ja €ykki, 2013). These studies endeavoured to: a) use context- or Na task-specific questions to measure students' thoughts and beliefs as €rvenoja, Volet, & Ja €rvela €, 2012), b) discern they occur in real-time (Ja how combining data at the individual and group levels can provide €rvela € et al., 2013), c) insights into the dynamics of the group (Ja examine the extent of congruence and/or dissimilarity in each individual's data in relation to others' data during the same event €rvenoja, & Ja €rvela €, 2010). Each of these approaches (Malmberg, Ja has produced useful information about the regulatory processes of learning, but they have not fully explored the socially shared regulation of learning. Despite the fact that self-regulated learning has been studied for over four decades, only a few studies have focused on examining the temporal aspects of regulated learning (Cleary et al., 2012). As a typical example, self-regulated learning has often been studied by counting the frequencies of learning activities. However, the sequential aspects (actions that typically follow each other) and the temporality of those actions (when actions take place and how they influence each other) are often ignored (Winne, 2014). Reimann (2009) claimed that the methods employed in standard research practices neglect to make full use of information relating to time and order. This is especially problematic when social and collaborative processes are studied in groups that work together for a longer time, which is the case in CSCL. Sequential characteristics describe the learning activities a student typically uses and they inform the actual process of selfregulated learning (Johnson, Azevedo, & D'Mello, 2011). Cleary et al. (2012) used the term “sequential phases of regulation” to describe the cyclical nature of regulated learning. The selfregulated learning process has two primary parts, sequential characteristics that entail transitions from one state to another (e.g., the change from a planning activity to making a summary), and temporal characteristics that entail occurrence and changes of

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sequential states over time (e.g. the student's progress from the beginning of the lesson to the end of the lesson). Earlier research has investigated the regulation of learning using self-reporting instruments, and frequently without contextualizing the regulation processes, despite the fact that regulation processes are affected by conditions that change over time (Winne, 2006). This is why the time at which data is gathered matters; the state of the learner's engagement and task accomplishment changes over time (Greene & Azevedo, 2010; Winne et al., 2011). Winne (1982) introduced the operational definition of “trace” to describe data that reflects learners' level of engagement in learning tasks over time. Traces capture students' immediate activities without interrupting the learning process, thus making it possible to follow their use of different studying techniques in the learning context (Hadwin, Nesbit, Jamieson-Noel, Code, & Winne, 2007). In essence, traces can serve as the indicators of self-regulated learning that students leave behind while studying (Boekaerts & Corno, 2005). The trace method is a subcategory of microanalytic protocols (Cleary & Zimmerman, 2012) that cover any event-based measures of regulated learning. Thus, microanalytic methods target specific aspects of regulated learning and focus on specific moments in time that have a beginning and an end. Capturing those specific moments in time can serve as a clear indicator of the self-regulated learning process while it is being generated (Cleary, 2011). In summary, understanding the socially shared regulation of learning often requires an understanding of the learning context and the evolution of social and regulatory processes over timedthis is why it is necessary to implement sequential and temporal aspects in the data analysis. During data collection, social interactions are contextualized in larger episodes to better capture the regulatory processes as they unfold over time to address a shared goal. 4. Aims The aim of this study was to investigate the temporal sequences of the self- and socially shared regulation of learning in CSCL, in order to provide empirical evidence about how self- and shared regulation activities are used and whether they are useful for collaborative learning outcomes. The research questions are as follows: RQ1) How are self- and shared regulation of learning activated by students during collaborative learning? RQ2) How are self- and shared regulatory activities connected with learning outcomes? RQ3) What characterizes the temporal sequences of self- and shared regulation activities in high and low learning outcome tasks?

4.1. Participants The participants included 18 adult graduate students in the Learning and Educational Technology Master's Program (mean age ¼ 44.5, SD ¼ 7.7, 10 women and eight men), where they studied to elevate their Bachelor degree to a Master's. The participants were assigned to groups of three in order to have an equal balance in terms of collaborative learning experiences. Group assignments were based on students' background information, knowledge, and their group work skills, as evaluated by their teacher. The teacher who made these selections had been working with the students for three semesters and knew them very well. This arrangement resulted in six total groups of participants. However, since one student did not grant permission for the research, five student

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groups of three were selected for further analysis. 4.2. Research design and tasks The students participated in a course entitled Cognitive, Motivational, and Emotional Bases in Learning for Understanding. The purpose of this course was to develop theoretical and practical understanding about topics in self-regulated learning, and to prompt the students to reflect on their solo and collaborative regulation processes and strategies. The data was collected during a seven weeks course. The course design included three learning phases each consisting of: (a) a one day face-to-face meeting, (b) a one week online solo learning phase, and (c) a one week online collaborative learning phase. This study focuses on three one week online collaborative learning phases in the course. Each learning phase had a similar structure, but the topics were different (see Fig. 1 for the task topics). The learning task in each topic was to create a real life case example of different learning problems that were related to the themes of each phase. The students were instructed to: a) create a case scenario, b) analyse the case scenario using the concepts presented at the literature and lecture, and c) engage in deeper processing through critical analysis, evaluation and application and reflection to the theory. The course materials and workspaces for the solo and collaborative work were accessed through nStudy (Winne, Hadwin, & Beaudoin, 2010). nStudy is a web-based application designed based on the model of self-regulated learning (Winne & Hadwin, 1998). nStudy works like a web browser, but it includes learning objects that support students in conducting various learning tasks individually or collaboratively. For example, students can read content from the web and link that content into their own notes, tags, or folders, or share it with their group members. It is possible to modify and structure nStudy learning objects, such as notes and tags, to promote the self- and shared regulation of learning. The students used nStudy in each learning task. The nStudy solo workspaces contained each student's work and were accessed by individual students, whereas the collaborative workspaces contained all of the collaborative work and were accessed by each of the group members. During their solo work, the students were assigned to read at least two scientific articles and then construct six notes from their course readings. Each collaborative task assignment included three phases: (a) a planning phase, in which the students responded collaboratively to questions regarding their task understanding, goals, and plans for the task assignment, (b) the task enactment phase, in which the groups executed the task together, and (c) the reflection phase, in which the groups collaboratively reflected on the learning process with respect to their realized task understanding, goals, and plans for the task assignment (see Fig. 1). 4.3. Procedures The students were trained for two hours on the first day regarding how to use the nStudy learning environment. First,

students were taught to annotate learning material in their solo learning spaces by highlighting and assigning labels, completing pre-stocked notes, and defining terms. Second, they were taught how to search and organize the learning objects in their workspaces and how to switch between the solo and collaborative learning spaces. In the collaborative workspaces, they were taught how to share and modify learning objects among their small group members and how to use chat to communicate with their small group members. The students were asked to fill out the collaborative planning notes (Hadwin, Miller, & Webster, 2012) in nStudy at the beginning of each collaborative learning phase and a collaborative reflection note at the end of each collaborative learning phase. Thus, students were asked to work as a group to discuss and negotiate what and how they would respond to questions presented in the collaborative planning and reflection notes. Questions in the collaborative planning notes dealt with shared task understanding (e.g., Describe your group learning task. What is the purpose of this task?), and shared goals (e.g., Set one goal for your task). Questions in the collaborative reflection notes dealt with: a) Shared task understanding (e.g., How well did your performed task match the instructions? Explain why?) b) Goal attainment (How well did you achieve your goal?) c) Challenges (Describe one challenge your group encountered) d) Cognitive regulation (What did you do to overcome that challenge?) e) Planning (What could you do differently the next time you confront the same challenge?) The collaborative planning and reflection notes were identical in each of the three learning tasks and they were used to stimulate and provide specific targets for the socially shared regulation of learning. 4.4. Data collection The data included students' collaborative online chat discussions in nStudy from the three structured collaborative learning phases. This resulted in a total of 15 log files including traces from five student groups' collaborative activities recorded by nStudy and the 15 collaborative learning task outcomes. The students' collaborative online chat discussions were recorded in nStudy. The chat discussions from the five groups over the course of three collaborative learning phases included 6188 lines and 46,136 words of text. These chat discussions were used to identify the target of socially shared regulation and the time when the student groups shared their regulation of learning. Log file traces of the individual student's activities during the three collaborative learning phases were also recorded in nStudy. Log file traces consist of time-stamped information about the students' activities performed in nStudy, such as what activities are performed before task execution, during task execution, and after task execution. The features of the collaborative learning space

Fig. 1. The course design.

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(nStudy) included six activities that were essential for each student to perform: 1) view the task instructions, 2) view the collaborative planning note, 3) edit the collaborative planning note, 4) view the edited planning note, 5) edit the reflection note, and 6) view the edited reflection note. The log file traces were used to identify these six types of activities during each collaborative learning phase with respect to how they occurred before, during, and after task execution. Collaborative learning outcomes (f ¼ 15) from each of the three tasks were assessed through the nStudy collaborative learning space.

4.5. Data analysis The collaborative learning outcomes from each of the three learning tasks among the five groups were coded and categorized according to a ordinal scale varying from one to four, which was loosely based on Biggs' SOLO taxonomy (Biggs, 1984) (see Table 1). This resulted in four categories, where a score of four was the highest possible outcome and a score of one was the lowest possible outcome. Three learning tasks achieved the highest possible score (4) and five learning tasks achieved the lowest possible score (1) (see Table 2). The learning outcomes were analysed by two independent researchers who were trained to apply Biggs' (1984) SOLO taxonomy. The Kendall's tb correlation was used to analyse the extent to which two sets of ranks between the raters differ (or agree). The agreement of between the two ratings was .770 (p < .01). Finally, for contradictory ratings, a third researcher's opinion was taken into account until a consensus was reached. The socially shared regulation of learning episodes were coded from the collaborative chat discussions through qualitative content analysis (Miles & Huberman, 1994). Our understanding is that socially shared regulation refers to the processes by which group members regulate their collective activity (Hadwin et al., 2012). This action is located at the articulation of individual and social €rvela € et al., 2010). Following Greeno's (2006) situative processes (Ja learning framework, which integrates the individual and social perspective in “learning in activity”, we targeted the meaningful interactive chat episodes. Each chat discussions involved many interactive episodes by the students. First, we identified an initiative act of one student that was used to start the collective chat discussion. The criterion for coding was that it invites the other group members to discuss (e.g., “Now we should start thinking about our case example, let's start!” or “What do you think would be good criteria for the representative example?”). Second, we identified how group members responded to that initiative act, and whether a consensual shared regulation of learning emerged or not. The criterion for shared regulation was that two or more students reciprocally participated. Third, only those instances of discussion that reflected: a) task understanding, b) planning, c) strategy use, or d) motivation were coded. According to Greeno (2006, p. 85), “Data are records of interaction, rather than verbal reports”, therefore, an exact beginning or end for the coding was not necessary (in terms

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Table 2 Coding of the learning outcomes among the fifteen learning tasks. Learning task

Learning outcome

Task 1 Task 2 Task 3

4 2 2

3 1 3

Mdn 2 1 1

1 3 3

1 4 4

2 2 3

of words). Rather, the most important criteria for recognizing socially shared regulation of learning was the content of an initiative act. The coding was applied by three independent researchers to the complete chat data, and a consensus was reached first from 65% of the coded instances. At this point of the analysis, each initiative act was coded and contradictory findings were negotiated, until a consensus was reached and definitions for the coding principles were specified. Next, the coded instances that reflected only socially shared regulation of learning were categorized based on the data and theory (Winne & Hadwin, 1998). Again, contradictory findings were negotiated and the coding principles specified until a consensus was reached. Each phase of the analysis from the beginning until the end included multiple negotiations between the researchers, starting with how to identify the socially shared regulation episodes, which resulted in four categories that represent socially shared regulation in collaborative learning groups: a) socially shared task understanding (f ¼ 3), b) socially shared planning (f ¼ 17), c) socially shared strategy use (f ¼ 7), and d) socially shared motivation (f ¼ 11). See Appendix A for examples of the coding used in each category. Cohen's Kappa was used to determine inter-rater reliability and there was a substantial agreement for the main categories: the kappa was k ¼ .63. Finally, contradictory findings were negotiated and the coding principles specified until 100% consensus was reached. The self-regulated learning activities during the three collaborative learning phases were analysed from the five student groups' log file traces. These individual regulatory activities informed each student's efforts during their collaboration, particularly if and how those individual efforts were transformed into the shared regulation of learning. These activities were: viewing the task instructions, opening the planning notes to view discussion topics for the collaborative chat, editing the collaborative planning notes, viewing the edited planning notes, editing the reflection notes, and viewing the edited reflection notes (see frequencies in Table 3). These six activities comprised the four dimensions of self-regulated learning, which are task understanding, planning, monitoring, and evaluating. The temporal sequences of the data were derived from the integration of the coded online chat discussions (f ¼ 38) with the coded log file traces (f ¼ 321). All of the learning activities among the five groups were organized in a temporal order. The coded online chat discussions and the coded log file traces were divided

Table 3 Activities from log file traces in respect of dimensions of self-regulated learning. Table 1 Characteristics of learning outcome. Score Characteristics of the outcome 4 3 2

1

Relevant concepts were explained and used to critically analyse the case Relevant concepts were explained and related to the cases. Examples were provided but overall critique or analysis was avoided. Relevant concepts were included, however they were listed rather than explained or used for analysis (e.g., retell or define the concepts without explaining their relevance for this case) Case analysis is off track. The examples were loose are not tied into relevant course concepts.

Trace data activity

Conceptual definition

Frequency 101 60 56 31 248

Internal actions View task instructions View planning note View edited planning note View edited reflection note

(TI) (VP) (VEP) (VER)

Task understanding Task understanding Monitoring Monitoring Total

Interactive actions Edit reflection note Edit planning note

(ER) (EP)

Evaluating Planning Total

45 28 73

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further into the temporal sequences of regulated learning: before the learning task (forethought phase), during the task execution (performance phase), and after the task execution (reflection and evaluation phase) (Cleary et al., 2012). The microlevel examples illustrate how these self- and shared regulatory activities emerged during the collaborative learning process when the outcome from a collaborative learning task was either high or low (see Table 2). To better illustrate which activity was used, and how the self- and shared regulatory activities were applied, they were divided into two categories: interactive actions (f ¼ 73), and internal actions (f ¼ 248), based on the characteristics of those actions (see Table 3). Interactive actions were those that became explicit for the other group members (e.g., editing the planning and reflection notes), whereas internal actions were those that remained salient for the other group members (e.g., viewing the task instructions, planning notes, or reflection notes). For example, when a student viewed the task instructions or a planning note, it generated information about the student's attempt to understand what the task was about. Thus, when a student viewed the edited planning or reflection notes, it generated information about the student's monitoring activities. However, these are internal activities, since these types of activities stay at an individual student level and are not shared with the other group members during collaborative learning. The other type of activity is interactive, since those activities have the potential to become shared with the other group members. When a student creates or edits the collaborative planning notes, it changes the current state of learning, and if that plan is changed further, it informs regulation, and thus has the potential to become shared with other group members. Similarly, the coded episodes from chat discussions (f ¼ 38) are interactive, since they represent interaction that involved sharing the regulation of learning. Table 4 provides an example of how microlevel temporal sequences of the self- and shared regulation of learning are generated when combining the chat and trace data. It is important to note that the microlevel descriptions are simplified examples of shared regulation. Each sequence starts with an internal activity and ends with an interactive activity (Table 4, row 1). In essence, each internal activity can include several activities (Table 4, row 3). For example, the empirical string “VP þ TI þ TI þ TI / SSTR” represents a theory-driven sequence “Task Understanding (TU) / Socially Shared Strategy (SSTR)” showing how a set of regulation activities guides students to socially shared regulation. By using this principle, four microlevel examples of temporal sequences of self- and shared regulation were generated. 5. Results and discussion 5.1. How are self- and shared regulation of learning activated by the students during collaborative learning (RQ1)? The students used self-regulatory activities, such as task understanding (f ¼ 84, 77%), planning the collaborative task (f ¼ 8, 16%), and monitoring (f ¼ 17, 7%), before the learning task. Our finding points to the major role of task understanding and is consistent with previous research, which indicates task perceptions play a key role in regulating learning (Winne & Hadwin, 2008).

During the collaborative task, the students' self-regulatory activities focused on planning (f ¼ 18, 16%), task understanding (f ¼ 61, 54%), monitoring (f ¼ 31, 27%), and reflecting (f ¼ 4, 4%). Similarly, Johnson et al. (2011) found that, as the students' progress at the task, they gain more information about the topic and adjust task perceptions and plans. After the learning task, the most common self-regulatory activity was monitoring (f ¼ 39, 51%). Reflecting (f ¼ 20, 26%) and task understanding (f ¼ 16, 21%) were used almost equally, and planning (f ¼ 2, 3%) was the least used self-regulatory activity (see Fig. 2). Among the students' socially shared regulation activities, socially shared planning (f ¼ 10, 61%), socially shared strategy use (f ¼ 3, 17%), and socially shared motivation (f ¼ 3, 17%) were the most common regulatory activities before the learning task. Socially shared task understanding was the least used activity in this phase (f ¼ 1, 6%). In collaborative tasks, the beginning of work seems to be important. For example, Kempler Rogat and Linnenbrink-Garcia (2011) found that identifying a shared plan enables groups to begin work on the task. During the task execution, socially shared planning (f ¼ 6, 39%) and socially shared motivation (f ¼ 6, 33%) were the most used activities, whereas socially shared strategy use (f ¼ 3, 17%) and task understanding (f ¼ 2, 11%) were the least. After the learning task, three regulatory activities emerged, namely, socially shared motivation (f ¼ 2, 50%), socially shared strategy use (f ¼ 1, 25%), and socially shared planning (f ¼ 1, 25%) (see Fig. 3). Despite the fact that socially shared regulation consists of distributing, contributing to, and coconstructing each other's ideas, it still requires multiple individuals to self-regulate and monitor the actual target of socially shared regulation, such as making shared plans (Hadwin et al., 2011), pointing out to the role of self-regulation in socially shared €rvela € & Hadwin, 2013). regulation of learning (Ja In summary, when considering socially shared regulation activities, socially shared planning was the most used activity before the collaborative learning task. During the task execution phase, the need for socially shared planning decreased, whereas the need for socially shared motivation increased. Thus, socially shared motivation regulation was the most used activity after the collaborative task, and it also increased when the students proceeded with the collaborative task. This is line with recent discussions of collaborative group engagement (Sinha, Kempler Rogat, AdamsWiggins, & Hmelo-Silver, 2015), pointing out that cognitive, motivational and socioemotional factors all contribute to the productive collaboration. When considering the temporal aspects of self-regulation and the socially shared regulation of learning, the results illustrate that students used different types of self- and shared regulatory activities as their work proceeded. For example, before the learning task, the most used self-regulatory activities were task understanding and monitoring, whereas the most used socially shared regulatory activities were planning, strategy use, and motivation regulation. €rvenoja, Malmberg, Isoh€ €la € and Sobocinski Similarly, J€ arvel€ a, Ja ata (2015) found that, depending on the phase of the SRL cycle, different types of interaction occur in a collaborative group. Specifically, at the forethought phase, both cognitive and socioemotional interaction contributed to successful collaboration. During the task execution, self-regulatory activities such as

Table 4 Example of coded microlevel sequence of chat and log data of shared regulation during the task execution. 1 2 3

Internal activity Task Understanding (TU) VP TI

TI

VP ¼ view planning note, TI ¼ view task instruction.

TI

Interactive activity Socially shared strategy (SSTR) SSTR

Microlevel sequence of shared regulation TU / SSTR VP þ TI þ TI þ TI / SSTR

€rvela € et al. / Learning and Instruction 42 (2016) 1e11 S. Ja

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Fig. 5. Example of group D self- and shared regulation in a high learning outcome task.

Fig. 2. Self-regulated learning activities before, after and during the collaborative learning task.

planning, monitoring and evaluating) were calculated. The correlation analyses revealed significant positive relationships between the learning outcome and use of specific SSRL processes. There were significant positive correlations between learning outcome and socially shared planning (rs ¼ .39, p < .05), and between learning outcome and socially shared motivation (rs ¼ .41, p < .05). 5.3. What characterizes the temporal sequences of self- and shared regulation activities in high and low learning outcome tasks (RQ3)?

Fig. 3. Socially shared regulation activities before, during and after the collaborative learning task.

monitoring were emphasized along with socially shared planning. Kempler Rogat and Linnenbrink-Garcia (2011) argued, that this specific feedback loop enables collaborating groups to refer back to the shared plans as they monitor progress on the task. After the learning task, the most common self-regulatory learning activities were monitoring and reflecting, whereas the most common socially shared regulation activities were motivation regulation and strategy use.

5.2. How are self- and shared regulatory activities connected with learning outcomes (RQ2)? The second research question addressed the relationship between self- and socially shared regulation of learning and collaborative learning outcomes. To determine this relationship, correlations between learning outcome and frequencies of SSRL processes (socially shared task understanding, planning, strategy use and motivation) and SRL processes (task understanding,

Four contrasting examples that illustrate the differences in the sequential and temporal aspects of self- and socially shared regulation in collaborative learning tasks were chosen for more detailed analysis. Figs. 4 and 5 show two examples of microlevel sequences of self- and socially shared regulatory activities that occurred during the high learning outcome collaborative learning tasks. The data is presented in a temporal sequence with respect to how these processes (e.g., planning and monitoring) occurred before, during, and after the collaborative learning task, this is to say, the data informs which regulatory actions typically follow each other and when those actions are taken. In both examples (group A and D), the self-regulatory processes occurred in connection with task understanding before the learning task, whereas socially shared learning processes occurred in connection with planning. In both of these examples, socially shared planning preceded the actual learning and task execution phase. During the task execution phase, self-regulatory processes occurred in connection with monitoring task understanding and planning, while socially shared learning activities occurred during planning and motivation regulation. After the task execution phase, self-regulatory processes were focused on monitoring and reflecting. The examples of high learning outcome tasks show that high learning outcome is characterized by students sharing their regulation of learning in each phase, especially the process phases, such as socially shared planning and socially shared motivation regulation. In Figs. 6 and 7, two examples of self-regulatory and socially shared regulatory activities from low learning outcome tasks among group B and C are presented in sequential order. In both

Fig. 6. Example of group B self- and shared regulation in a low learning outcome task.

Fig. 4. Example of group A self- and shared regulation in a high learning outcome task.

Fig. 7. Example of group C self- and shared regulation in a low learning outcome task.

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tasks, before the task execution phase, the self-regulatory processes were related to task understanding, planning, and monitoring. During the task execution phase, the self-regulated learning processes focused mostly on monitoring and task understanding. After the learning task, self-regulatory processes included reflecting, task understanding, and monitoring. There were no socially shared regulatory activities. When contrasting these four examples of microlevel sequences that represent high and low learning outcome tasks, there are two major differences. First, the two high learning outcome examples involved the socially shared regulation of learning activities, whereas the two low learning outcome examples lacked socially shared regulation. Second, the high learning outcome tasks involve more interactive actions between group members. This has also been found in existing research on collaborative learning, showing that members of successful groups contribute to each other's thinking, whereas members of less successful groups typically ignore each other's proposals (Barron, 2003). These examples show that when the learning outcome was low, individual students tended to use mainly internal self-regulatory activities. Therefore, the collaboration in low-outcome groups reflected multiple individual students regulating their own learning without a joint understanding of the tasks, goals, or plans. 6. Conclusions The aim of this study was to provide empirical evidence concerning how self- and shared regulation activities are used in CSCL and whether they are useful for collaborative learning outcomes. The study introduces an approach for analysing the temporal sequences of online chat discussions and log file traces when examining self- and shared regulation in online collaboration. This is needed when seeking to understand the exact nature of regulated learning and acknowledging the interactive roles of emotion, motivation, metacognition, and strategic behaviour (Zimmerman & Schunk, 2011). The results show that during collaborative learning, an individual student's self-regulated learning activities focus on the metacognitive aspects of learning (e.g., task understanding and monitoring), whereas socially shared regulation involves the coordinative activities of collaboration, such as planning and strategy choices. It was also found that the socially shared regulation of motivation is important in maintaining productive collaboration. According to the theoretical models of self-regulated learning (Winne & Hadwin, 2008), students engage in constructing an idiosyncratic perception of the learning task. That is, students engage in processing the available information about the learning task from the resources provided by the learning environment (e.g., checking the task instructions). It was found that planning activity increased during the task execution phase. This might be due to the fact that the students did not have a well-established idea of what the assignment was about at the beginning of the learning task. Thus, individual student's monitoring activities increased during the task execution, which indicates that the students did check their progress on the task according to their plans (Johnson et al., 2011). However, this finding might also indicate that the students had trouble with the process of constructing task perceptions. Greene, Hutchison, Costa, and Crompton (2012) found that the quality of the students' task perceptions was negatively related to the amount of monitoring activities the students used during learning. According to their conclusions, this might be evidence that students who have successfully translated the learning task into the in-depth task definition are less likely to face situations where they need to frequently apply monitoring activities. When considering the contribution of socially shared regulation

on collaborative learning outcomes, the correlation analyses showed that groups with higher learning outcomes tended to engage in socially shared planning and socially shared motivation, whereas the groups with low learning outcomes tended to engage more in self-regulated learning. In addition, the case examples illustrated how socially shared regulation was actually used in the high learning outcome tasks in contrast to low learning outcome tasks. Finally, the high learning outcome tasks involved more interactive actions, such as planning and motivational regulation between the group members. Even though decades of research have shown that there is a connection between success in learning and use of self-regulated strategies (Pintrich & De Groot, 1990), previous research has not enabled us to directly link regulation activities to learning outcomes. In this study, micro-analytic techniques (i.e., traces and temporal sequencing) advanced our understanding in this direction and qualitative differences in temporal use of self- and shared regulation activities were reported, which shows how these processes support productive collaboration for learning. We conclude that in collaborative learning, an externalization of individual student regulation can invite other group members to share the regulation of learning, due to an increased awareness of regulatory activities. Increasing students' meta-communicative and metacognitive awareness in computer-based learning hypermedia environments (Azevedo, Chauncey, & Burkett, 2012) has been useful for fostering students' SRL processes. In CSCL, in turn, increasing the social aspects of collaboration, such as sociability, social space, and social presence, has been effective in supporting learning and productive interactions (Kreijns, Kirschner, & Vermeulen, 2013). In this study, we were able to find evidence of whether or not collaborative planned regulatory activities become shared in practice. For example, the groups that achieved good learning results used multiple regulatory processes to support their learning and also reached a level of shared regulation. The four microlevel examples demonstrated patterns of how students activate selfregulation, which may or may not generate shared regulation and assist in simplifying the complex phenomena of the socially shared regulation of learning. Results of the temporal analysis show that the students used different types of self-regulation and socially shared regulation of learning as their work progressed. This might indicate that at the beginning of the collaborative task, individual self-regulatory processes are more salient and internal, but are still important for interactive regulatory activities, which can result in socially shared regulation. In terms of temporality, results show that the students engaged in self-regulatory activities, such as task understanding and monitoring, before they jointly generated plans for how to approach the task. For example, constructing task understanding is an evolving process throughout task execution, which shapes the plans and strategies students need to deploy during task execution (Greene et al., 2012; Hadwin et al., 2011). Thus, without a sufficient understanding of the task, groups might not be able to plan their collaboration strategically. Recently, Schoor and Bannert (2012) study pointed out that socially shared orientation, planning, and evaluation were significant events in successful collaborative learning. Their conclusion, however, was that these processes do not occur spontaneously in collaborative learning situations. In this study, we purposefully promoted these processes; however, there were situations when groups missed opportunities for socially shared regulation for learning. In addition, results indicate that socially shared planning and socially shared motivation regulation were especially beneficial for successful collaborative learning outcomes. These findings indicate that during collaboration, an individual

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student's focus shifts from the initial stage of task understanding towards monitoring activities, whereas socially shared regulation and motivation regulation increase as collaboration progresses. This is to say that during collaboration, a need for the coordination of collaborative activities (Kirschner & Erkens, 2013), and regulating and maintaining the socio-emotional balance of the collaboration, increased. It has also been noted in earlier research that during collaboration, students experience a variety of motivational €rvenoja & Ja €rvela €, 2009). However, through shared challenges (Ja regulation processes, the group members can co-construct and maintain their motivation together. When considering the methodological approach used in this study, log file traces have the potential to reveal frequencies in learner interaction with the environment, temporal sequences of regulated learning over time, and types of regulatory processes during learning (Azevedo et al., 2010; Winne & Nesbit, 2009). However, this method has some limitations too. The main limitation of this method is that, depending on the learning environment and the task, the log data is bound to the learning context. Log file traces are highly situation specific, and they describe learner characteristics in a specific situation. Therefore, caution needs to be exercised when extrapolating the findings of this study beyond its setting. Also, despite recent advances in the temporal analysis of €rvela €, 2014), there is not yet much regulated learning (Molenaar & Ja research evidence that relies on the use of log file traces, such as data that goes beyond frequency measures to understand the actual process of regulated learning. Log file traces provide reliable information concerning students' actions, but a significant flaw is that the traces need to be interpreted regarding what they actually mean in terms of self-regulated learning, which may affect the validity of the current study. For example, when students open the planning notes, we infer but are unable to know whether they actually read the plans to monitor their progress, or whether the planning notes were merely open in the background. We cannot know whether the students really view and engage in the task instructions or planning notes in nStudy, even if the logs say that they opened them. There is a danger of over interpreting the data. Performance data and complementary methods, such as eyetracking, are needed to verify the interpretations. In any case, computer environments, such as nStudy, provide a new source of data for tracing learning processes and new data-driven analytical techniques (Reimann et al., 2014). The future challenge in methodological approaches like those used in this study will be to progress from “more data” to “deep data” to understanding temporally unfolding socially shared regulation processes. Finally, the microanalytic techniques we used in this study are labour intensive and so the sample is small. Replications are needed. Understanding the shared regulation of learning in more detail has practical implications for the design of CSCL tools. The log data offers insights into capturing learning traces in detail regarding what learners actually do while studying, and appropriate prompting can be provided based on the learning patterns that exist (Winne, 2014). To support sharing regulation in this study, nStudy offered both regulation support (planning tools) and collaboration support (chat), which resulted in shared regulation by the group members during collaborative tasks. Based on the findings of this study two kinds of design recommendations can be made. First, designing support that help students coordinate and maintain a positive social and emotional balance, e.g. motivation regulation, in their group work can be a way for “setting a stage” for socially shared regulation and high quality collaborative learning. Second, as earlier research has systematically shown (e.g. Azevedo et al., 2010) designing prompts for goal-setting and planning contributes to SRL. The design of these prompts should be extended from individual to both individual and group level plans and goals.

9

To conclude, our empirical findings thus far point out that, when designing support for socially shared regulation of learning, regulation in collaboration should not be oversimplified in terms of theory and support. Regulation is always multifaceted, not just about “cognition”, but covers aspects of motivation, emotion, cognition and metacognition (Winne & Hadwin, 1998). Increasing learners' metacognitive awareness and evaluation of their own and others' learning processes sets a stage for progress in socially shared regulation and, ultimately, better collaboration (J€ arvel€ a et al., 2014). Acknowledgements This study was supported by the Finnish Academy grants 127640 and 243012741 awarded to the first author. The authors would like to thank the anonymous reviewers for their helpful comments. Appendix A Data examples of the codings in each category of socially shared regulation of learning: a) Socially shared task understanding Maria: What is our agenda today? Jani: We should brainstorm ideas for constructing our case story. Maria. Oh, that's right. Jani: (reads the task instructions aloud) Maria: … so case description and analysis is needed. Jani: I am wondering why the task deals with motivational problems, not self-regulation? Piia: I think that motivation is part of self-regulation, or is it so …? Jani: Yes, but it is easy to find motivation problem cases without self-regulation Maria: Hmm … doesn't self-regulation actually help to handle motivational problems? Jani: It depends on the problem, but let's integrate these aspects in our case description. b) Socially shared planning Anna: How should we progress to create a coherent answer for the task? Matti: Could any one of us start by integrating the ideas? Mari: Yes, why not first copy/paste all ideas to the word document? Anna: I can do that and write a summary. I like summarizing things. Mari: OK, so you can include volition and feedback and add something about self-efficacy. Anna: That's right. I'll summarize what we have now and send it for your additions by e-mail. Mari: Very good! Matti: I agree! c) Socially shared strategy use Jussi: Minna has an excellent definition about metacognition, why don't we use it, mine is much more superficial. Pauli: I can add a few aspects. Minna: Yes, please, Pauli could add a few, such as a more specific definition of cognition. Pauli: I can think about integrating Minna's and my own thinking.

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Minna: Yes, let's conclude so that will add Pauli's most important issues to the final definition. That will be an excellent answer to the task. d) Socially shared motivation Jani: This is a great case analysis! As we keep on developing it, it will be spectacular! Maria: Yeah, we'll make it great. I'll continue elaborating it, I am good at that. Maria: ….when we do something, we'll do it perfectly finished, not unfinished;-) heh… Piia: Hurrah!

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