Regulation In Collaborative Learning Research Articles

Interactions for Socially Shared Regulation in Collaborative Learning: An Interdisciplinary Multimodal Dataset

Socially shared regulation plays a pivotal role in the success of collaborative learning. However, evaluating socially shared regulation of learning (SSRL) proves challenging due to the dynamic and infrequent cognitive and socio-emotional interactions, which constitute the focal point of SSRL. To address this challenge, this paper gathers interdisciplinary researchers to establish a multi-modal dataset with cognitive and socio-emotional interactions for SSRL study. Firstly, to induce cognitive and socio-emotional interactions, learning science researchers designed a special collaborative learning task with regulatory trigger events among triadic people for the SSRL study. Secondly, this dataset includes various modalities like video, Kinect data, audio, and physiological data (accelerometer, EDA, heart rate) from 81 high school students in 28 groups, offering a comprehensive view of the SSRL process. Thirdly, three-level verbal interaction annotations and non-verbal interactions including facial expression, eye gaze, gesture, and posture are provided, which could further contribute to interdisciplinary fields such as computer science, sociology, and education. In addition, comprehensive analysis verifies the dataset’s effectiveness. As far as we know, this is the first multimodal dataset for studying SSRL among triadic group members.

Self‐regulation and shared regulation in collaborative learning in adaptive digital learning environments: A systematic review of empirical studies

AbstractAdaptive learning technologies are closely related to learners' self‐regulatory processes in individual and collaborative learning. This study presents the outcomes of a systematic literature review of empirical evidence on adaptive learning environments to foster self‐regulation and shared regulation of learning in collaborative settings. We provide an overview of what and how adaptive technologies have been used to understand and promote self‐regulated learning in collaborative contexts. A search resulted in 38 papers being analysed. Specifically, we identified the seven main objectives (feedback and scaffolding, self‐regulatory skills and strategies, learning trajectories, collaborative learning processes, adaptation and regulation, self‐assessment, and help‐seeking behaviour) that the adaptive technology research has been focusing on. We also summarize the implications derived from the reviewed papers and frame them within seven thematic areas. Finally, this review stresses that future research should consider developing a converging theoretical framework that would enable concrete monitoring and support for self‐regulation and socially shared regulation of learning. Our findings set a baseline to support the adoption and proliferation of adaptive learning technology within self‐regulated learning research and development. Practitioner notesWhat is already known about this topic By providing personalized and learner‐centric adaptive learning environments (ADLEs), adaptive learning technology can support and foster self‐regulated learning (SRL) practices. It is possible to create a more student‐centred and effective learning environment by combining adaptive learning and collaborative learning. Socially shared regulatory activities can involve planning, monitoring, controlling and reflecting on a group's learning processes. What this paper adds Provides a systematic literature review of empirical evidence on ADLEs, SRL and socially shared regulation of learning (SSRL) in collaborative contexts. Summarizes the insights on (S)SRL through ADLEs in collaborative learning. Identifies challenges and opportunities for ADLEs to support (S)SRL in collaborative learning. Implications for practice and/or policy Learning analytics and educational technology researchers will be able to use the systematic review as a guide for future research. Learning analytics and educational technology practitioners will be able to use the systematic review as a summary of the field's current state.

Feedforward‐ or feedback‐based group regulation guidance in collaborative groups

AbstractBackgroundCollaboration is a crucial concept in learning and has the potential to foster learning. However, the fact that collaborative groups act with a common understanding in a common task brings many difficulties. Therefore, there is a need for group regulation and guidance to support effective group regulation in collaborative learning. On the other hand, the focus should be on the effect of group regulation guidance intervention on the improvement of both individual and group performance, modes of motivational regulation strategies, and the progression of group metacognition.ObjectivesIn this study, group regulation guidance approaches were developed to support the collaborative learning of Computer Science university students. The developed group regulation guidance approach consists of feedback and a feedforward mechanism. The purpose of this study was to examine the effectiveness of these approaches on academic performance, group regulation performance perception, self‐, co‐, and socially shared regulation of intrinsic motivation, and group metacognition levels.MethodsThe study was patterned with a quasi‐experimental design. 65 students participated in the study, and various scales were used to collect data. Data were analysed using paired sample t‐tests, analysis of covariance, and multivariate analysis of covariance.Results and ConclusionAccording to the research results, academic achievement showed an increase in the experimental groups after the intervention compared with the pre‐intervention. The group in which the feedforward‐supported regulation guidance approach was used showed better learning success. In the group using the feedback‐supported regulation guidance approach, better group regulation, motivational regulation strategies, and group metacognition scores were obtained. Modes of motivational regulation strategies and group metacognition scores differed according to the experimental groups.ImplicationsWhile the results provide important clues for teachers and instructional designers, the developed approaches can be used as a framework for different fields.

How are achievement goals associated with self-, co-, and socially shared regulation in collaborative learning?

It has been suggested that self-organized study groups need to regulate their learning at three levels: the self-, the co-, and the socially shared level. Yet, little is known about how individual learner characteristics influence these regulation processes. In this study, we investigate how students’ achievement goals are associated with regulation processes within groups. Two hundred and seventy-seven undergraduates were asked to imagine being part of a self-organized study group with comprehension-related and motivational problems and to name strategies they would apply to regulate their learning in this situation in an open-ended format. Achievement goals were measured using a standardised questionnaire. Results indicated that mastery- and performance-approach goals are positively associated with regulatory effort across all three levels of regulation. Performance-avoidance goals seem to have no significant relationship with regulatory effort.

Predicting regulatory activities for socially shared regulation to optimize collaborative learning

This study utilized multimodal learning analytics and AI-based methods to examine the patterns of the socially shared regulation of collaborative learning (CL). The study involved multimodal data involving video and electrodermal activities (EDA) data collected from ninety-four secondary school students (N = 94) during five science lessons to reveal trigger events in CL. A novel concept of trigger events is introduced, which are challenging events and/or situations that may inhibit collaboration and will, therefore, require strategic adaptation in the regulation of cognition, motivation, and emotion within the group. The ANOVA results for the Skin Conductance Responses (SCRs) analysis indicated the disparity of physiological behavior activated in relation to different types of interactions for regulation. Process analysis and episode-rule mining were applied to reveal regulatory patterns in CL, while an AI approach with long short-term memory (LSTM) deep-learning networks were designed for pattern prediction. LSTM has emerged as the most widely applied artificial recurrent neural network (RNN) architecture for sequential data analysis and classification. The proposed AI network holds the potential for designing solutions for similar signal-processing problems in studying learning regulation. This study contributes to developing AI-enabled real-time support for regulation in collaborative learning.

Sequential Patterns in Social Interaction States for Regulation in Collaborative Learning

This study explored sequential patterns in social interaction states for group-level regulation of learning during collaborative learning. The participants were secondary school students ( N = 92) performing collaborative physics tasks. The videotaped sessions were analyzed regarding participation, social interaction, and group-level regulation types of co- and socially shared regulation. The results show that group-level regulation emerged most frequently in social interaction state that included cognitive and socioemotional interaction and whole-group participation, which also led to and followed regulation most frequently. The findings highlight the role of joint participation in social interactions for regulation of learning in collaborative group settings.

Examining socially shared regulation and shared physiological arousal events with multimodal learning analytics

AbstractSocially shared regulation contributes to the success of collaborative learning. However, the assessment of socially shared regulation of learning (SSRL) faces several challenges in the effort to increase the understanding of collaborative learning and support outcomes due to the unobservability of the related cognitive and emotional processes. The recent development of trace‐based assessment has enabled innovative opportunities to overcome the problem. Despite the potential of a trace‐based approach to study SSRL, there remains a paucity of evidence on how trace‐based evidence could be captured and utilised to assess and promote SSRL. This study aims to investigate the assessment of electrodermal activities (EDA) data to understand and support SSRL in collaborative learning, hence enhancing learning outcomes. The data collection involves secondary school students (N = 94) working collaboratively in groups through five science lessons. A multimodal data set of EDA and video data were examined to assess the relationship among shared arousals and interactions for SSRL. The results of this study inform the patterns among students' physiological activities and their SSRL interactions to provide trace‐based evidence for an adaptive and maladaptive pattern of collaborative learning. Furthermore, our findings provide evidence about how trace‐based data could be utilised to predict learning outcomes in collaborative learning. Practitioner notesWhat is already known about this topic Socially shared regulation has been recognised as an essential aspect of collaborative learning success. It is challenging to make the processes of learning regulation ‘visible’ to better understand and support student learning, especially in dynamic collaborative settings. Multimodal learning analytics are showing promise for being a powerful tool to reveal new insights into the temporal and sequential aspects of regulation in collaborative learning. What this paper adds Utilising multimodal big data analytics to reveal the regulatory patterns of shared physiological arousal events (SPAEs) and regulatory activities in collaborative learning. Providing evidence of using multimodal data including physiological signals to indicate trigger events in socially shared regulation. Examining the differences of regulatory patterns between successful and less successful collaborative learning sessions. Demonstrating the potential use of artificial intelligence (AI) techniques to predict collaborative learning success by examining regulatory patterns. Implications for practice and/or policy Our findings offer insights into how students regulate their learning during collaborative learning, which can be used to design adaptive supports that can foster students' learning regulation. This study could encourage researchers and practitioners to consider the methodological development incorporating advanced techniques such as AI machine learning for capturing, processing and analysing multimodal data to examine and support learning regulation.

Socially Shared Regulation and Performance in Group Work on Creativity Tasks: Analyzing Regulation Utterances

This study examined socially shared regulation of learning (SSRL) and motivation processes in a collaborative learning task that required creativity using the ICT tool of mind mapping. Thirty university students formed three groups, collaborating face-to-face to generate creative ideas. The following results were obtained from qualitative and quantitative data using psychological scales and utterance analysis. In the middle phase of the collaborative activity, there was a significant weak-to-moderate positive correlation between socially shared regulation of cognition, self-regulation, co-regulation, and socially shared regulation of intrinsic motivation and a deep level of regulation utterances. Moreover, there were significant weak-to-moderate correlations between behavioral and cognitive engagement, SSRL of monitoring and cognition, and the three modes of motivational regulation. Creative performance was significantly and moderately positively associated with socially shared regulation of cognition and total frequency of utterances in the group. Based on these findings, the implications for practice in university education are discussed from the perspective of socially shared regulation in collaborative learning.

Capturing the Formation and Regulation of Emotions in Collaborative Learning: The FRECL Coding Procedure.

Despite recent increases in research on emotions and regulation in collaborative learning, measuring both constructs remains challenging and often lacks structure. Researchers need a systematic method to measure both the formation of emotions and subsequent regulation in collaborative learning environments. Drawing from the Formation and Regulation of Emotions in Collaborative Learning (FRECL) model, I introduce a new observational coding procedure that provides comprehensive guidelines for coding these phenomena. The FRECL coding procedure has been implemented successfully in other studies and is described here in detail. Specifically, I detail the ideal situations for using the procedure, discuss background information and present a codebook and empirical examples for each stage of the FRECL model, and provide additional considerations that allow researchers flexibility based on their own experiences and preferences. This procedure extends past research by providing an accessible observational protocol that is both systematic and comprehensive. The FRECL coding procedure can benefit future research by providing more organized consistency to the measurement of collaborative emotions and regulation.

Социоэмоциональная регуляция в условиях коллаборативного обучения: обзор исследований

We analyze studies on socio-emotional regulation in collaborative learning. The features and advantages of collaborative learning are described. We consider The role of academic emotions in the learning process. The emotional background plays an important role in the learning process in general and collaborative learning in particular. Students often experience problems in the emotional sphere during learning process, for the solution of which they use the strategy of “emotion suppression”. In this context, social-emotional interaction plays an important role in the learning process. Positive social and emotional interaction contributes to increased motivation, harmonious functioning of the group and greater involvement in the learning process. Negative socio-emotional interaction creates difficulties for the process of group learning and is realized in directive forms of social regulation. The N.G. Lobczowski’s model describes the process of formation and regulation of emotions in the process of collaborative learning. According to this model, emotion formation spans the first four stages (context, stimulus event, appraisal, and emotional response) leading to the regulation. An important aspect of the socio-emotional interaction is the socio-emotional discourse. To create positive social and emotional conditions for learning, it is important to show interest in other members of the group, express social responsiveness, positively assess the progress in completing the task and the actions of group members, and express mutual support between participants. Solving problems and conflicts between group members is necessary to maintain a sense of cohesion among group members. The solution of the problems associated with the socio-emotional interaction requires the socio-emotional regulation, that is, responding to the manifestation of emotions in the group, solving the problems of the group and supporting positive interaction between group members. Students can apply behavioral, interpersonal, cognitive, motivational and motivational-cognitive socioemotional regulation strategies.

Understanding motivation and classroom modes of regulation in collaborative learning: an exploratory study

ABSTRACT The present paper aims to explore how a certain class mode emerges in the context of collaborative learning, which affects student motivation and learning. Multiple sources of data were collected from 51 high school students in three different classes (named A, B and C) where the same teacher gave them the same lesson content throughout a full year. A quantitative analysis of multiple questionnaire responses revealed that patterns of motivational change and change in perception of pair and group work were markedly different in the three classrooms. A qualitative data analysis of interviews and observations of pair and group activities showed that different classroom modes of regulation are likely to emerge even among homogenous classes consisting of students with the same L1 and similar proficiency levels under the same curriculum. The findings imply that socially-shared regulation among students is one of the keys to creating a positive classroom environment in which students influence and motivate each other.

Using a design-based research approach to develop and study a web-based tool to support collaborative learning

We report on a design-based research study that was conducted over two years. We developed, tested, and implemented Collabucate, a web-based tool for fostering social regulation of learning in collaborative learning. In this paper, we describe two cycles of a design-based research study in which Collabucate was implemented with Doctor of Pharmacy students. Collabucate was created according to Järvelä and colleagues' (2015) design principles for supporting socially shared regulation of learning and our design proposition to provide direct instruction of social regulation strategies. During two implementations, we collected and analyzed log data, student ratings, and focus groups. We improved design elements between the two design-based research cycles. Study results supported Järvelä and colleagues’ (2015) design principles and our design proposition. However, further work is required to improve how design elements are embodied in web-based tools. Based on our experience, we outline achievements, challenges, and future considerations for related tools and research. The study contributes to a growing number of tools created to foster awareness and effective regulation in collaborative learning.

A Collaborative Learning Design for Promoting and Analyzing Adaptive Motivation and Emotion Regulation in the Science Classroom

The aim of this paper is to introduce our current research design to study socially shared regulation processes in a science classroom where a collaborative learning design is implemented. The design is based on a self-regulated learning framework that provides opportunities and support for self-initiated regulation among individual learners and collaborative groups. It utilizes modern technology to structure and support regulated learning in the groups. The paper focuses on elaborating the research design, particularly from the perspective of motivation and emotion, by presenting the dual relationship between designing learning scenario that supports learners’ motivation and emotion regulation with technology and researching the multifaceted role of motivation and emotion as they occur in collaborative learning. To do this, the paper first describes the entire collaborative learning design while paying attention to how technology can be utilized to support the awareness of motivation, emotion, and their regulation. Then, the focus shifts to considering the methodological principles and implementation of multimodal data gathered in relation to authentic collaborative learning tasks. A case example from a secondary school science classroom demonstrates possibilities for multimodal data use in analyzing motivation, emotion, and their regulation in collaborative learning. It also illustrates the dual role of the implemented technological 6Q support tool by showing how data collected from the students’ use of the tool can be utilized in scientific analysis. The paper concludes by providing a short discussion about the current advancements of emerging technology in motivation and emotion research in the learning sciences highlighting the significance of sharing the theoretical premises of the research design as well as practical experiences from implementation of these designs for future research.

Co-regulation in collaborative learning: Grounded in achievement goal theory

This paper examines the relationship between achievement goal orientation and co-regulation. Co-regulation, a concept that has been expanded from self-regulation, describes diverse types of individual- and group-level regulation in collaborative learning. Relationships between three achievement goal orientations (mastery, performance-approach, and performance-avoidance) and two individual-level co-regulation factors (self-regulation in collaborative learning and other-regulation) were hypothesized and examined via structural equation modeling (N = 410). The results demonstrate that mastery goal orientation positively affected both self-regulation in collaborative learning and other-regulation, while performance-approach and avoidance goal orientation had no significant regression path to both self-regulation in collaborative learning and other-regulation. The results showed that mastery goal orientation plays a predominant, positive role when learners regulate themselves as well as others in collaboration.

Emotional regulation in collaborative learning: when do higher education students activate group level regulation in the face of challenges?

ABSTRACTThis study focuses on emotion regulation in the challenging situation of collaborative learning. The aim is to explore what type of challenges trigger group-level emotion regulation and what kind of co- and socially shared emotion regulation strategies emerge in relation to these challenges. The participants are 62 higher education students, who collaborated in groups during a six-week mathematics course. The data consist of 87 h of video-recorded learning sessions. In the analysis, first, the challenges emerging during collaborative learning were located. Second, co- and socially shared emotion regulation activated in the face of challenges were traced and the related emotion regulation strategies were classified. The results provide evidence about the collaborative groups’ emotion regulation, highlighting that group-level emotion regulation can be activated in relation to different types of challenges. The results also revealed four different types of strategies which the group members used to manifest emotion regulation.

Convergences of Joint, Positive Interactions and Regulation in Collaborative Learning

This qualitative study explores the convergences of small groups’ joint, positive interactions and regulation in social interaction during collaborative learning. We analyzed the video-recorded social interactions of five groups of student teachers during environmental science tasks. We examined the frequency and functions of the situations in which joint participation and positive socioemotional interaction converged with regulation (planning, monitoring, and evaluating) in social interaction. The results show that when groups planned, monitored, or evaluated their learning, they participated more jointly in social interaction and showed more socioemotional support than in interactions with no observed regulation. The situations in which these elements converged served three functions: establishing agreement, responding to challenges or mistakes, and discussing strengths and weaknesses. The results suggest that the convergences of joint, positive interactions and regulation in social interaction can serve a function that is meaningful for collaborative learning progress.

Supporting groups’ emotion and motivation regulation during collaborative learning

This study investigates when and how students activate co- and socially shared emotion and motivation regulation in collaborative learning and whether the S-REG mobile application tool can support this regulation. In a mathematics course, 44 higher education students worked with a collaborative assignment. The S-REG tool traced groups' emotional and motivational states in different sessions, and the occurrence of co-regulation and shared regulation of motivation and emotions were coded from video-recorded collaborative work (44 h). The groups activated more co-regulation than shared regulation of emotions and motivation, but the shared-regulation episodes were longer-lasting. The groups’ emotional and motivational states were associated with the occurrence of co-regulation in the beginning of the learning sessions. The results suggest that the S-REG tool balanced collaboration by prompting the groups to regulate emotions and motivation right in the beginning of the motivationally and emotionally challenging learning sessions.

Regulation and socio-emotional interactions in a positive and a negative group climate

Collaboration in an online environment can be a socially and emotionally demanding task. It requires group members to engage in a great deal of regulation, where favourable emotions need to be sustained for the group’s productive functioning. The purpose of this cross-case analysis was to examine the interplay of two groups’ regulatory processes, regulatory modes, and socio-emotional interactions that contribute to or are influenced by emotions and socio-emotional climate perceived in the group. Specifically, this study compared a group of 4 students unanimously reporting a positive climate to a group of 4 students unanimously reporting a negative climate after completing a 90-min online text-based collaborative assignment. By drawing on two data channels (i.e., observed regulatory actions and socio-emotional interactions during collaboration and self-reported data about emotional beliefs and perceptions), four contrasting group features emerged: (a) incoming conditions served as a foundation for creating a positive collaborative experience, (b) regulation of emotions during initial planning, (c) negative emotions served as a constraint for shared adaptation in the face of a challenge, and (d) encouragement and motivational statements served as effective strategies for creating a positive climate. Implications for researching and supporting emotion regulation in collaborative learning are discussed.

Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning

This study examined how temporal sequences of regulated learning events, such as types and processes of regulated learning, emerge during different stages of collaborative learning. Earlier research has focused on individual learning and not on the captured temporal sequences of regulation in collaborative learning. The data were collected during a two-month math didactics course taken by teacher education students who collaborated in three member groups. Twenty-two hours of video data were collected to follow how sequences of regulated learning events, along with task execution, emerged within the six groups as their collaboration advanced. The data were analyzed using qualitative content analysis and lag sequential analysis. The results showed that the groups engaged mostly in co-regulated planning and monitoring. Temporal analysis showed that collaborative interactions focusing on task execution promoted socially shared planning, indicating that task execution provided grounding for socially shared planning and regulation to occur. The sequential analysis illustrated that metacognitive monitoring played a facilitative role in the progress of task execution.

Socially Constructed Self-Regulated Learning and Motivation Regulation in Collaborative Learning Groups

Background/Context Most of the earlier empirical findings deal with motivation regulation in individual learning situations. This study identifies higher education students’ 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 students’ socially constructed motivation regulation in collaborative learning This was studied by collecting data about the students’ (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 methods—namely, adaptive instrument, video-tapings, and group interviews—were 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.