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

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 self- and 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.     

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

Socially 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 notes

What 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.

     

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.     

“We're looking good”: Social exchange and regulation temporality in collaborative design

Collaborative tasks do not always promote equal learning. Varying levels of social interactions and regulation at the individual and group levels can influence knowledge construction efforts and learning success. To understand which collaboration patterns may be more conducive to learning, this study examined the relation between social exchange, regulation, and learning outcomes. Four project-based engineering undergraduate teams were audiotaped in collaborative tasks (7514 talk turns). Discourse was coded for regulation processes and types (self and socially shared regulation), and analyzed with Epistemic Network Analysis and Process Mining. We find that teams who reported more frequent social exchange engaged in shared regulation together with planning and monitoring more frequently, while teams with less exchange engaged in long durations of collaboration. Furthermore, students in teams with more engaged regulation reported enhanced beliefs in group efficacy to solve collaborative tasks. The study illustrates the potential of applying quantitative approaches to analyzing rich discourse.     

Group characteristics and profiles of shared regulation in collaborative environments involving asynchronous communication / Características y perfiles grupales de regulación compartida en entornos colaborativos de comunicación asíncrona

The processes of socially shared regulation in small groups of students who perform collaborative tasks through forums involving asynchronous communication are explored in this article. The specific aim of the study is to identify profiles of shared regulation in groups of students who have different performance levels on the task, depending on the regulatory activities exercised by the groups and their distribution over time. For that purpose, a case study was developed; six small groups of university students collaboratively performed a complex task (for 29 days) in a virtual environment based on asynchronous discussion forums. The results revealed three different profiles: (i) a profile with stable and high regulation; (ii) a profile with partially stable and medium regulation; and (iii) a profile with unstable and low regulation. The first two profiles were observed in the groups with high performance levels on the task, while the third profile was observed in the low-performing groups. Therefore, some recommendations are suggested to support processes of shared regulation in asynchronous collaborative learning situations.     

基于社会网络分析(SNA)的共享调节学习评价:概念框架与解释案例

共享调节学习评价涉及协作学习的不同维度和过程,同时兼具群体感知的功能。现有的评价存在分析维度单一、数据利用不足、缺乏对互动过程的挖掘和跟踪等问题。将社会网络分析(Social Network Analysis,SNA)引入共享调节学习的评价,可以充分反映共享调节学习特有的理论观照,呈现调节过程不同阶段的特点、不同评价维度的关联性以及调节的内在机制。基于SNA的共享调节学习评价框架,在数据收集以及分析工具方面,拓展了现有的共享调节评价维度,其引入群体交流模式、群体互动紧密性、个人或群体角色、不同关系维度、多模社会关系等评价分析工具,对共享调节学习过程进行评价与跟踪,促进了群体的自我感知。以华东师范大学教育信息技术学系的一门专业选修课程为解释案例说明概念框架的应用过程,也表明了基于SNA的共享调节评价能够评估共享调节学习的协作过程,促进共享调节水平的提高,相应的评价工具能够促进协作学习活动开展,强化成员的群体感知。未来仍需要进一步深化实证研究,完善评价工具,拓展应用案例,进一步挖掘概念框架的潜在价值。     

The Sequential Composition of Collaborative Groups’ Emotion Regulation in Negative Socio-emotional Interactions

Research indicates that to adjust a group’s emotional atmosphere for successful collaborative learning, group members need to engage in group-level emotion regulation. However, less is known about the whys and ways regulation is activated at a group level. This research explores what triggers 12-year-old primary school students’ (N = 37) negative socio-emotional interactions during a collaborative science task and whether the nature of the trigger makes a difference to group-level emotion regulation strategies and their sequential composition in these interactions. Groups’ collaborative working was videotaped, and triggers and strategies were analysed. The results reveal that the triggers of negative interactions are linked to the groups’ activated regulation strategies. Motivation control strategies were more represented in situations where negative interactions were triggered by task-related issues, whereas socially related triggers were associated with behavioural regulation strategies. Furthermore, the results illustrate that strategies are concatenated to a series of strategic actions, which mostly begin with sharing an awareness of the trigger. The results indicate a need to focus on the series of strategic actions activated in group interactions. This will help reveal how socially shared regulatory processes build a group’s emotional atmosphere.

     

Regulation of emotions in socially challenging learning situations: an instrument to measure the adaptive and social nature of the regulation process

Self-regulated learning (SRL) research has conventionally relied on measures, which treat SRL as an aptitude. To study self-regulation and motivation in learning contexts as an ongoing adaptive process, situation-specific methods are needed in addition to static measures. This article presents an ‘Adaptive Instrument for Regulation of Emotions’ aimed at accessing students’ experiences of individual and socially shared regulation of emotions in a socially challenging learning situation. The instrument, grounded in self-regulated and socially regulated learning theory, comprises four interrelated components: the socio-emotional challenges experienced in a collaborative learning situation; individual and group-level attempts to regulate the immediate emotions evoked by the challenges; the personal goals; and goal attainment pursued in that situation. The theoretical foundation of the instrument and its components are outlined and some reliability issues illustrated. The limitations but also educational potential of the instrument to understand regulation of emotions in socially challenging learning situations are discussed.     

Temporal analysis of multimodal data to predict collaborative learning outcomes

The analysis of multiple data streams is a long-standing practice within educational research. Both multimodal data analysis and temporal analysis have been applied successfully, but in the area of collaborative learning, very few studies have investigated specific advantages of multiple modalities versus a single modality, especially combined with temporal analysis. In this paper, we investigate how both the use of multimodal data and moving from averages and counts to temporal aspects in a collaborative setting provides a better prediction of learning gains. To address these questions, we analyze multimodal data collected from 25 9–11-year-old dyads using a fractions intelligent tutoring system. Assessing the relation of dual gaze, tutor log, audio and dialog data to students' learning gains, we find that a combination of modalities, especially those at a smaller time scale, such as gaze and audio, provides a more accurate prediction of learning gains than models with a single modality. Our work contributes to the understanding of how analyzing multimodal data in temporal manner provides additional information around the collaborative learning process.     

An exploration of social regulation of learning during scientific argumentation discourse

Argumentation and scientific discourse are essential aspects of science education and inquiry in the 21st century. Student groups often struggle to enact these critical science skills, particularly with challenging content or tasks. Social regulation of learning research addresses the ways groups attempt to navigate such struggles by collectively planning, monitoring, controlling, and reflecting upon their learning in collaborative settings. Such regulation and argumentation can also elicit socioemotional responses and interactions. However, little is known regarding how regulation processes and socioemotional interactions manifest among students involved in small-group discourse about scientific phenomena. As such, in this qualitative study, we explored social regulation of learning, scientific argumentation discourse, and socioemotional interactions in the discussions of two groups of high school physics students (n = 7, n = 6). We found key qualitative distinctions between the two groups, including how they enacted planning activities, their emphasis on challenging other’s ideas versus building shared understanding, and how socioemotional interactions drove discourse. Commonalities across groups included how regulation initiation related to discourse, as well as how the difficulty of the content hindered, and teacher support augmented, the enactment of social regulation. Finally, we found overlapping regulation and discourse codes that provide a foundation for future work.     

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.     

Productive group engagement in cognitive activity and metacognitive regulation during collaborative learning: can it explain differences in students’ conceptual understanding?

This paper addresses the nature and significance of productive engagement in cognitive activity and metacognitive regulation in collaborative learning tasks that involve complex scientific knowledge. A situative framework, combining the constructs of social regulation and content processing, provided the theoretical basis for the development of a comprehensive coding scheme for interactive data analysis. An empirical study was conducted with two groups of university students working on two science-learning tasks. It examined the function of metacognitive regulation to control the flow of cognitive activity, and the extent to which group differences in cognitive and metacognitive regulation processes during collaborative learning could explain differences in the groups’ learning outcomes. The findings provide validation of the framework and its derived coding scheme. An example of a way in which a group engages in socially shared metacognitive regulation is presented to demonstrate how the coding scheme was applied to the data. Theoretical and empirical implications of the findings are discussed.     

Examining the interplay of affect and self regulation in the context of clinical reasoning

There is an active strand of research on how affect and self-regulatory activities influence performance and learning outcomes, but the mechanisms through which they interact during learning remain poorly understood. Additionally, these constructs have been under-researched in medical education. Using multimodal data in the context of a clinical reasoning task for medical students learning case diagnosis, we explored the temporal nature of cognition, affect, motivation, and self-regulation. With a sample of n = 10 medical students, we collected data on self-regulated learning (SRL) processes through think-aloud analyses; emotion data through facial expressions; and achievement goal orientations and habitual emotion regulation strategies through self-report questionnaires. Results from our sequential data mining techniques and quantitative analyses suggested that high-performing medical students (who arrived at an accurate diagnosis) differed from low-performing students (who did not arrive at correct diagnosis): Low performers reported higher performance goal orientation, and expressed more emotions overall, than high performers. High performers exhibited marginally more monitoring SRL behaviours, while low performers tended to orient and reorient throughout the task. High and low performers also differed on the co-occurrences of, and sequential transitions between, emotions and SRL behaviours. The implications of these findings with respect to research and medical education are discussed.     

Understanding the dynamics of motivation in socially shared learning

The aim of this study was to gain an understanding of the dynamics of motivation in socially shared learning from both individual and group perspectives. Higher education students’ motivation was analysed in the context of collaborative learning tasks, applying quantitative and qualitative methods. The research questions were: (1) what kind of motivations and task-specific goals do individual students have and (2) how do they combine their motivations as a group in socially shared learning? Three- to five-member groups of university students (n = 99) were observed as they worked with three different collaborative learning tasks in an educational psychology class. The experiment included differently organised conditions for collaboration, ranging from face-to-face situations to virtual collaboration situations which were perceived as motivationally and emotionally challenging. Self-reports and video-tapings were collected. The results show that the students who studied in face-to-face settings reported significantly more learning goals and fewer performance goals during the collaborative tasks than the students in the virtual group. Therefore, the collaborative process of volunteer face-to-face groups was analysed qualitatively and an attempt was made, by observing their shared motivation, to uncover the reasons why they achieved their learning goals.     

基于项目式学习的同伴在线协作学习活动设计与应用研究

近年来,基于项目的协作学习研究主要集中在项目式学习的模型、程序、分组、策略、优缺点等方面,在项目式同伴协作在线学习过程中,学生如何通过自我调节和共享调节来实现项目目标仍未得到充分研究。文章依据活动理论框架和协作学习中共享调节模型,通过设计持续时间8周的PBL同伴在线协作学习来组织教学活动,借助同伴评价表、自我报告调查表、团队项目成果评价表,进行自我调节和共享元认知调节对项目协作学习结果的影响研究。研究结果表明:自我调节和共享调节在PBL同伴在线协作学习中发挥着关键作用,有效的共享调节主要受到学习者自身强大的自我调节的支持,在共享调节的作用下,协作同伴之间相互影响,共同推进项目协作过程。     

Self-regulated learning with hypermedia: The role of prior domain knowledge

Think-aloud and pre-test data were collected from 49 undergraduates with varying levels of prior domain knowledge to examine the relationship between prior domain knowledge and self-regulated learning with hypermedia. During the experimental session, each participant individually completed a pretest on the circulatory system, and then one 40-min hypermedia learning task during which he or she learned about the circulatory system. Think-aloud data were collected during the 40-min learning task to measure each participant’s use of specific self-regulated learning processes related to planning, monitoring, and strategy use. Results indicate that prior domain knowledge is significantly related to how the participants self-regulated their learning during the 40-min learning task with hypermedia. Specifically, prior domain knowledge is positively related to participants’ monitoring and planning and negatively related to their use of strategies during the hypermedia learning task.     

Regulation During Cooperative and Collaborative Learning: A Theory-Based Review of Terms and Concepts

This article reviews the terms and concepts that have been used for describing regulation of learning during cooperative and collaborative learning and suggests differentiating them on the basis of which parts of a regulatory feedback loop model are being shared. During cooperative and collaborative learning, not only self-regulation but also the regulation of the group process is important. This regulation might occur on both an individual level and a social level. Several modes of regulation have been identified, but the terms used for them vary tremendously—including social regulation, socially shared regulation, coregulation, and other-regulation. This article seeks to clarify the diverse terminology. To this end, we use a theoretical framework based on Winne and Hadwin's (1998) model of self-regulated learning to analyze how the different terms are used in the literature. We make and exemplify suggestions for a consistent usage of terms.