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Interrelation between regulatory and socioemotional processes within collaborative groups characterized by facilitative and directive other-regulation
... On the behavioural dimension, students solve problems through online activities and coordinate their creation of knowledge artifacts as they regulate each other's behaviour (Fiore et al., 2010;Stahl, 2017). On the socio-emotional dimension, students need to engage in active listening, encourage participation, and contribute towards the creation of cohesive groups in order to foster active engagement, relaxation of tension, and the emergence of social motivation (Kwon et al., 2014;Rogat & Adams-Wiggins, 2015). More importantly, during the CPS process, this multiplicity of dimensions come together to form a complex, interdependent set of relationships that ultimately contribute to an adaptive, self-organizing system with multilevel and multilayered characteristics (Byrne & Callaghan, 2014;Hilpert & Marchand, 2018). ...
... The behavioural dimension analyzed students' online behaviours, including resource management, concept mapping and observation. The socio-emotional dimension included active listening and respect, encouraging participation and inclusion, as well as fostering cohesion during the CPS processes (Rogat & Adams-Wiggins, 2015). It is important to note that although these theory-driven dimensions are indeed useful to label student interactions, they are proxies for tacit learning processes and certain levels of overlap between them are expected. ...
... A student monitored the progress of tasks, evaluated the timeline for completing the task, and summarized what had been done and what needed to be done Socio-emotional (Rogat & Adams-Wiggins, 2015) Active listening and respect (ALR) ...
Collaborative problem solving (CPS) enables student groups to complete learning tasks, construct knowledge, and solve problems. Previous research has argued the importance of examining the complexity of CPS, including its multimodality, dynamics, and synergy from the complex adaptive systems perspective. However, there is limited empirical research examining the adaptive and temporal characteristics of CPS, which may have led to an oversimplified representation of the real complexity of the CPS process. To expand our understanding of the nature of CPS in online interaction settings, the present research collected multimodal process and performance data (i.e., speech, computer screen recordings, concept map data) and proposed a three-layered analytical framework that integrated AI algorithms with learning analytics to analyze the regularity of groups’ collaboration patterns. The results surfaced three types of collaborative patterns in groups, namely the behaviour-oriented collaborative pattern (Type 1) associated with medium-level performance, the communication-behaviour-synergistic collaborative pattern (Type 2) associated with high-level performance, and the communication-oriented collaborative pattern (Type 3) associated with low-level performance. This research further highlighted the multimodal, dynamic, and synergistic characteristics of groups’ collaborative patterns to explain the emergence of an adaptive, self-organizing system during the CPS process. According to the empirical research results, theoretical, pedagogical, and analytical implications were discussed to guide the future research and practice of CPS.
... Although collaborative learning is an efficient way to deal with complex problems and has many benefits for students' learning, high-quality collaborative learning does not always happen in genuine contexts (Rogat & Adams-Wiggins, 2015;Sohr et al., 2018). It has been shown that apart from cognitive interaction (Avry et al., 2020), socio-emotional interactions can also influence the effectiveness of collaborative learning (Kwon et al., 2014). ...
... Conversely, negative socio-emotional interactions involve undermining, insulting, or ignoring others (Isohätälä et al., 2018;Rogat & Adams-Wiggins, 2015), which may lower students' engagement in collaboration, lead to inefficient collaboration and cause unfavorable learning results (Bakhtiar et al., 2017;Tao et al., 2021). In the worst case, these negative interactions can degrade into battles with disruptive outcomes for interpersonal relationships, motivation, and learning processes (Mänty et al., 2020;Rogat & Adams-Wiggins, 2015). ...
... Conversely, negative socio-emotional interactions involve undermining, insulting, or ignoring others (Isohätälä et al., 2018;Rogat & Adams-Wiggins, 2015), which may lower students' engagement in collaboration, lead to inefficient collaboration and cause unfavorable learning results (Bakhtiar et al., 2017;Tao et al., 2021). In the worst case, these negative interactions can degrade into battles with disruptive outcomes for interpersonal relationships, motivation, and learning processes (Mänty et al., 2020;Rogat & Adams-Wiggins, 2015). However, negative socio-emotional interactions could also promote learning by enabling students to explicitly express their emotions, thereby bringing group negative experiences to the surface. ...
Socio-emotional interaction is considered a crucial factor that may affect the effectiveness and efficiency of collaborative argumentation and deserves further exploration. This study explored what temporal and sequential patterns of socio-emotional interaction could promote or hinder productive collaborative argumentation by comparing the differences between four high- and four low-performing groups. The 8 target groups were selected from a larger sample of 14 groups according to their scores of groups’ argument maps after participating in a 90-min collaborative argumentation activity. Using content analysis method, groups’ video recordings were coded and analyzed. The results indicated several differences between the high- and low-performing groups. First, more positive socio-emotional interactions were found in the high-performing groups while more negative socio-emotional interactions were discovered in the low-performing groups. Second, using smiles or laughter to express disagreement (P2) and interrupting others (N1) were more essential factors that may affect argumentation quality in the Chinese context. Third, temporal analysis suggested that in low-performing groups, the sharp rise in negative socio-emotional interactions may be provoked by more cognitive interactions in the initial phase that aimed to persuade opponents. Fourth, sequential analysis showed that repeated negative interactions in low-performing groups may lead to a negative atmosphere and thus, made the increasing tension less likely to be relieved by positive interactions. Whereas in high-performing groups, frequent transitions between positive socio-emotional interactions helped create a positive atmosphere, which could avoid conflict escalation and resolve the potential tension caused by negative interactions. Finally, pedagogical implications were suggested.
... On the behavioural dimension, students solve problems through online operations and coordinate their behaviours on knowledge artefacts and the behaviours of others (Fiore et al., 2010;Stahl, 2017). On the socio-emotional dimension, students need to build active listening, encourage participation and create cohesive groups to foster active engagement, relaxation of tension, and the emergence of social motivation (Kwon et al., 2014;Rogat & Adams-Wiggins, 2015). More importantly, during the CPS process, various dimensions form complex, interdependent relationships to ultimately contribute to an adaptive, self-organizing system with multilevel, multilayered characteristics (Byrne & Callaghan, 2014;Hilpert & Marchand, 2018). ...
... The behavioural dimension analyzed students' online behaviours, including resource management, concept mapping and observation. The socio-emotional dimension included active listening and respect, encouraging participation and inclusion, and fostering cohesion during the CPS processes (Rogat & Adams-Wiggins, 2015). It is important to note that although these theory-driven dimensions are indeed useful to label student interactions, they are proxies of tacit learning processes and certain levels of overlap between them are expected. ...
... A student read and explained the problems or questions of the tasks Regulative (Malmberg et al., 2017) Goal setting and planning (GSP) A student discussed the purpose of the task, divided the task into specific steps, and planned what to do next Monitoring and reflection (MR) A student monitored the progress of tasks, evaluated the timeline for completing the task, and summarized what had been done and what needed to be done Socioemotional (Rogat & Adams-Wiggins, 2015) Active listening and respect (ALR) ...
Collaborative problem solving (CPS) enables student groups to complete learning tasks, construct knowledge, and solve problems. Previous research has argued the importance to examine the complexity of CPS, including its multimodality, dynamics, and synergy from the complex adaptive systems perspective. However, there is limited empirical research examining the adaptive and temporal characteristics of CPS which might lead to an oversimplified representation of the real complexity of the CPS process. To further understand the nature of CPS in online interaction settings, this research collected multimodal process and performance data (i.e., verbal audios, computer screen recordings, concept map data) and proposed a three-layered analytical framework that integrated AI algorithms with learning analytics to analyze the regularity of groups collaboration patterns. The results detected three types of collaborative patterns in groups, namely the behaviour-oriented collaborative pattern (Type 1) associated with medium-level performance, the communication - behaviour - synergistic collaborative pattern (Type 2) associated with high-level performance, and the communication-oriented collaborative pattern (Type 3) associated with low-level performance. The research further highlighted the multimodal, dynamic, and synergistic characteristics of groups collaborative patterns to explain the emergence of an adaptive, self-organizing system during the CPS process.
... Existen distintas metodologías para analizar interacciones, tales como el método IPA (Interaction Proccess Analysis) propuesto por (Bales, 1950) que permite detectar problemas de comunicación, integración, tensión, decisión, control y evaluación. Incluso existen metodologías específicas que analizan los tipos de interacciones que se esperan que ocurran en situaciones de ACSC y que están relacionadas con la tarea y los planos afectivoemocional (Janssen, Erkens, Kirschner, & Kanselaar, 2012;Järvelä, Malmberg, & Koivuniemi, 2016;Kwon, Liu, & Johnson, 2014;Meier et al., 2007;Näykki et al., 2014;Rogat & Adams-Wiggins, 2015;Soller, 2001;Zheng & Huang, 2016). Sin embargo, estas técnicas exigen un trabajo de etiquetado manual de datos lo cual implica mucho trabajo para un docente más aún si la cantidad de alumnos involucrados es elevada. ...
... En el caso particular de que el docente necesite reconocer conflictos en las interacciones debería revisar cada una de ellas siguiendo algún tipo de protocolo para poder identificarlos, tal como, la propuesta de Millar (Millar, Rogers, & Bavelas, 1984) que sostiene que tres intentos consecutivos de ganar el control en una conversación es síntoma de conflicto. Para un docente reconocer y diferenciar los conflictos es importante, por un lado, es conveniente que las actividades que proponga a los grupos tengan la capacidad de generar conflictos sociocognitivos o conflictos de tarea (Buchs, Butera, Mugny, & Darnon, 2004), pero debería también identificar casos en los que se produzcan los conflictos de relaciones porque son síntomas (entre otras causas) de carencia de habilidades interpersonales (Lee et al., 2015;Slof, Nijdam, & Janssen, 2016) y aplicación de estilos de interacción sociocognitivos negativos (Rogat & Adams-Wiggins, 2015;Zheng & Huang, 2016) a los cuales el docente podría responder proporcionando retroalimentación que permita al alumno aprender estilos de interacción adecuados para el trabajo en grupo (Pauli et al., 2008). ...
... Motiva esta idea los estudios en el campo del ACSC los cuales reconocen la vinculación existente entre conflictos y emociones (Dreu & Weingart, 2003;K. a Jehn, 1997;Jiang et al., 2013;Lee et al., 2015) y la importancia de considerar el plano afectivo a fin de asegurar interacciones apropiadas para el proceso de aprendizaje en grupo (Baker, Andriessen, & Järvelä, 2013;Rogat & Adams-Wiggins, 2015;Zheng & Huang, 2016). ...
El Aprendizaje Colaborativo Soportado por Computadora (ACSC) es una situación de aprendizaje donde dos o más estudiantes trabajan juntos con el objetivo de aprender. La comunicación mantenida por los grupos para llevar a cabo su trabajo puede ser sincrónica o asincrónica. La comunicación de tipo sincrónica demanda que los miembros concuerden en un horario para poder interactuar. Un ejemplo de aplicación que puede soportar este tipo de comunicación es el chat. En la comunicación asincrónica no es necesario que los miembros del grupo concuerden en un horario para poder interactuar. En ambos tipos de comunicaciones, los miembros pueden participar en el dialogo colaborativo estando en distintos lugares. Un ejemplo de aplicación que soporta la comunicación asíncrona es el foro. La interacción entre los estudiantes influye positivamente en los procesos cognitivos de los participantes cuando la colaboración es exitosa. Muchos factores pueden incidir en el éxito de un proceso de aprendizaje colaborativo. Uno de estos factores es la estabilidad emocional del grupo. Sin embargo, esta estabilidad emocional puede verse afectada por la ocurrencia de una diversidad de eventos, entre ellos, los conflictos. Los conflictos son desacuerdos entre dos o más miembros de un grupo causado por disposiciones individuales y la diversidad de objetivos, puntos de vista y experiencias previas. Cuando el conflicto se manifiesta en el seno del grupo hay una tendencia a que el sistema cognitivo se vea resentido. Esto ocurre debido a un incremento en la carga cognitiva que genera el conflicto. A su vez, este fenómeno conduce a que la capacidad de procesamiento del grupo se bloquee. Si bien existe una connotación negativa en los conflictos, es importante reconocer que existen distintos tipos de ellos. Se pueden identificar los conflictos cognitivos o de tarea, los conflictos de proceso y los conflictos de relaciones. De estos tipos de conflictos, se reconoce que los conflictos cognitivos pueden contribuir positivamente en el aprendizaje. Sin embargo, los otros dos tipos de conflictos también influyen en el rendimiento del grupo, tal es el caso de los conflictos de relaciones que impactan negativamente. A pesar de la negatividad de ciertos tipos de conflictos, la ocurrencia de conflictos abre la oportunidad a que los estudiantes aprendan a trabajar en grupo, una competencia demandada por el mercado laboral actual. Sin embargo, para que esto ocurra el docente debe guiar a los estudiantes hacia la resolución de los conflictos cuando aquellos no puedan hacerlo por sí mismos. Esto significa que el docente necesita poder responder en tiempo real a las situaciones de conflicto para ofrecer recomendaciones en cuanto al intercambio de roles, la compartición del liderazgo, realizar cambios en la carga de trabajo, promover la reflexión, entre otros. Para lograr esta función, el docente necesita realizar un seguimiento de las situaciones de conflicto. Sin embargo, realizar este seguimiento es una tarea que insume tiempo y mucho trabajo. Lo analizado anteriormente pone de manifiesto la necesidad de proveer a los entornos de ACSC, que emplean herramientas de comunicación síncronas basadas en texto para promover los procesos de aprendizaje en grupo, la funcionalidad de reconocimiento de conflictos para facilitar el monitoreo por parte del docente y propiciar su oportuna intervención. En esta tesis se planteó la hipótesis de que en las situaciones de ACSC síncronas basadas en texto, los mensajes de texto intercambiados entre los miembros del grupo pueden tener la suficiente información para detectar conflictos. Particularmente, se idearon dos técnicas que permiten reconocer conflictos teniendo en cuenta el intercambio de información socio-afectiva. La primera técnica implementada modela un diálogo colaborativo como un grafo dirigido donde los nodos representan a los estudiantes y las aristas indican la transferencia de sentimientos negativos durante las interacciones. Luego, aplicando conceptos de la teoría de grafos se emplea una matriz de commute time escalada para detectar miembros del grupo en conflicto. La segunda técnica se basa en la aplicación de aprendizaje máquina supervisado. Particularmente, se realiza la aplicación de algoritmos de aprendizaje ensamblados, formalizando el proceso de extracción de características y definiendo el concepto de valencia de interacciones atómicas como principal característica empleada para entrenar el clasificador supervisado. Para evaluar las técnicas propuestas se llevó a cabo una validación experimental que demandó la recolección de interacciones de estudiantes en situaciones de ACSC. Estas interacciones fueron analizadas aplicando una técnica de análisis de contenido y sirvieron de base para el posterior entrenamiento y validación de los clasificadores. Los resultados de las técnicas propuestas resultaron satisfactorios, obteniéndose un valor de F1 de 0.72 para la primera técnica, y un F1 de 0.81 para la segunda. Estos resultados muestran que es posible reconocer conflictos teniendo en cuenta el intercambio de emociones negativas. Esta tesis proporciona importantes contribuciones al campo del ACSC al permitir reconocer conflictos mediante la aplicación de técnicas de Aprendizaje Máquina (AM), Análisis de Redes Sociales (ARS) y Análisis de Sentimiento (AS).
... Research has shown a positive relationship between students' regulatory behaviors and collaborative learning performance (e.g., Lee et al. 2015;Malmberg et al. 2015;Zheng et al. 2019). For example, Rogat and Adams-Wiggins (2015) examined 7th grade students' regulatory processes and their socioemotional interactions as they worked on three inquirybased science tasks in small groups and found that recognizing the key role of the social aspects of group collaborative learning and the successful regulation of these challenges both exhibited positive effects on group performance. In line with their findings, an examination of 144 students' sequential patterns of self-and socially shared regulation of STEM learning in a collaborative learning environment identified a positive relationship among students' regulatory activities -SRL monitoring, SRL elaborating, and SSRL task analysis -as well as the collaborative learning performance (Zheng et al. 2019). ...
... Previous research has investigated the regulation of learning in collaborative tasks, mainly in face-to-face learning settings (e.g., Näykki et al. 2017a;Rogat and Adams-Wiggins 2015). Online collaborative learning settings, especially those supported by social media tools, being different from the traditional face-to-face collaborative learning settings, may affect students' regulation of learning (e.g., Su et al. 2018;Wu 2015). ...
... Student teachers' content monitoring behavior enabled group members to complete learning tasks through division of labor, choose appropriate teaching methods, and achieve a deep understanding of integrating information and communication technology into curriculum (Røkenes and Krumsvik 2016). This finding is consistent with the findings by Rogat and Adams-Wiggins (2015) and Su et al. (2018). ...
Interest in understanding regulation in the context of collaborative learning has increased in the past decade. Existing studies have investigated how regulated learning evolves in collaborative learning by focusing on external behaviors, and how different types and strategies of regulation are effective in promoting collaborative learning. Due to the cyclical and dynamic characteristics of regulation, there is a need for new methods that can trace the dynamic emergence of regulatory processes in diver collaborative learning contexts, so as to provide some insight into effective learning design. In the context of 45 student teachers participating in multi-layered online collaborative activities, this study investigated their regulatory patterns during various stages of online collaborative learning activities over an eight-week semester via content analysis and epistemic network analysis (ENA). Quantitative analyses indicated that student teachers demonstrated active social aspects of regulation and had many regulatory behaviors in content monitoring in the designed online collaborative learning activities. Through identifying and comparing the regulatory patterns of the high-performing group and the low-performing group across the stages of learning activities, the results showed that the group demonstrating ample regulatory patterns in “content monitoring”, “evaluating”, and “social emotional regulatory behavior” performed better on the collective score of group product. Furthermore, the analysis elucidated how groups regulated their collaboration variously in different stages of online learning activities. Suggestions about regulated learning at both cognitive and social emotional aspects are provided to teachers and learning designers for designing and implementing online collaborative learning activities.
... Within QT S , evidence of promoting positive socioemotional interactions and SoRL is perhaps most prominent in the rules for participating in the intervention (see Table 1; . Specifically, rules 2, 3, 5, and 6, as well as the use of the phrase "we talk" (i.e., fostering cohesion through the use of a plural pronoun; Rogat & Adams-Wiggins, 2015) throughout, are directly comparable to literature on positive socioemotional interactions (Lajoie et al., 2015;Rogat & Adams-Wiggins, 2015;Rogat & Linnenbrink-Garcia, 2011). The rules also promote the use of SoRL processes during the group discussions. ...
... Accordingly, we implemented episodic coding to capture the inherently interactive nature of individual and group regulation, defining an episode as a continued pattern in content and collaboration, ending with a clear shift in either content or collaboration. For each episode, we assigned codes based on a revised version of the SoRL coding scheme developed by Rogat and colleagues (e.g., Rogat & Linnenbrink-Garcia, 2011;Rogat & Adams-Wiggins, 2015). First, we identified the mode of regulation (i.e., SRL, CoRL, SSRL, or External). ...
... Shelia from Group 2, on the other hand, was more facilitative in her efforts to help the group, which was met with respect and higher group cohesion. Thus, we posit that as individuals within groups adopt informal leadership roles, the type of regulatory behaviors that they exhibit can have implications for the socioemotional patterns of the entire group, which aligns with similar findings from the SoRL literature (Rogat & Adams-Wiggins, 2015). This extends previous work in the field that highlights a "more knowledgeable other" (Panadero & Järvelä, 2015, p. 191), suggesting instead the importance of a "more regulated other" to a group's regulatory patterns and subsequent socioemotional patterns during collaborative learning tasks. ...
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.
... In contrast, the directive other-regulation group is always related to imbalanced participation (Rogat & Adams-Wiggins, 2014), which is due to that the directive other-regulator offers few opportunities for other group members to participate by constantly ignoring or rejecting their contributions (Eilam & Aharon, 2003;Kumpulainen & Mutanen, 1999;Rogat & Adams-Wiggins, 2014;Volet & Mansfield, 2006). Another study conducted by Rogat and Adams-Wiggins (2015) investigated how the socio-emotional process differ for groups characterized by facilitative or directive other-regulation. Their results indicated that members in directive other-regulation groups tended to engage in highly critical and socially comparative discourse, which enabled negative socio-emotional interactions dominant in the group. ...
... This finding is consistent with the idea that the emergence of critical cognitive processes necessitates a favourable group climate where group members feel safe to provide counter ideas (Baker, 1999;King, 2002). Previous research pointed out that groups characterized by facilitative other-regulation tend to employ positive socio-emotional interactions to foster a favourable group climate (Rogat & Adams-Wiggins, 2015). ...
... Previous studies have indicated that cognitive conflicts can play a positive role in joint activities when group members negotiate these divergent views in informational ways (De Dreu & Weingart, 2003;Jehn & Mannix, 2001). The appeal of this sequential pattern may be due to the respect and inclusion of the alternative opinions (Rogat & Adams-Wiggins, 2015), which promotes the further explanation or clarification of different views. Besides, the facilitative other-regulation groups presented the significant sequence of modifying the proposed product to discovering contradictions or reaching agreement (SKC4-SKC2, SKC4-SKC5). ...
Many studies have explored the role of regulation of learning in supporting social knowledge construction. Other-regulation is a common regulation type in collaborative learning. However, few studies have examined learners` social knowledge construction in other-regulation groups. This study attempts to provide a new lens to understand the role of regulation of learning in supporting social knowledge construction and broaden our knowledge about two forms of other-regulation within groups. Toward that end, this study compares social knowledge construction in groups characterized by facilitative and directive other-regulation. The two case groups of four in this study were selected from a larger sample (N=22). Content analysis and sequential analysis were used to analyze the online chat log collected from two groups. The comparison was made in terms of the frequency and behaviour pattern of social knowledge construction between the two groups. Qualitative analysis was adopted to explore the interrelation between social knowledge construction and two forms of other-regulation. Results indicate that the facilitative other-regulation group engaged in more high-level social knowledge construction and demonstrated more continuous and systematic behaviour patterns. Further qualitative analysis reveals that facilitative other-regulation occurred concurrently with social knowledge construction and played a promoting role in this process. In contrast, directive other-regulation followed social knowledge construction but failed to guide the subsequent knowledge construction moves, ending in impeding the ongoing of social knowledge construction smoothly.
... Socio-emotional interactions refer to the purposeful interchanges between group members to express and shape the perceptions of emotions and the socio-emotional climate (Bakhtiar et al., 2018;Kreijns, Kirschner, & Jochems, 2003;. Overall, groups' positive socio-emotional interactions have been linked to positive outcomes in groups' engagement, motivation and regulated learning processes (Lajoie et al., 2015;Rogat & Adams-Wiggins, 2015). At best, groups' positive socio-emotional interactions can boost the learning of the individual group members, but also the group as an entity, if all group members' goals are aligned and if they are motivated and contribute to the learning process (Bakhtiar et al., 2018;Volet, Summers, & Thurman, 2009;Zschocke et al., 2015). ...
... In the previous research literature, negative socio-emotional interactions have been seen as the ones that often challenge a group's learning process, affecting the quality of learning activities (Rogat & Adams-Wiggins, 2015; and the emotional and motivational reactions of group members . Negative interactions can derive from various sources, such as cognitive challenges (Andriessen et al., 2013;Järvenoja & Järvelä, 2009), motivational issues or interpersonal dynamics (Blumenfeld, Marx, Soloway, & Krajcik, 1996;. ...
... It is evident that a group's ability to appropriately regulate, in particular, negative socio-emotional interactions is important with respect to the interactions turning into more beneficial ones in terms of learning activities (Lajoie et al., 2015;Rogat & Adams-Wiggins, 2015;. However, the reason for regulation failure may be, for example, the application of inadequate or inappropriate regulation strategies for that situation (Bembenutty, 2011;Cleary, Velardi, & Schnaidman, 2017;Järvenoja et al., 2019;Kurki et al., 2017). ...
This study explores how groups’ negative socio-emotional interactions and related emotion regulation during a collaborative physics task are interconnected with 12-year-old primary school students’ (N = 37) situated individual emotional experiences. To accomplish this, the study relates group-level video data analysis with students’ self-reported emotional experiences. The results indicate that students’ negative emotional experiences related to the task prior to collaborative working increase the group’s emotion regulation during the collaboration and that negative group interactions negatively affect students’ emotional experiences after the task. The study also shows that even though group-level regulation is more likely to change the valence of the group’s interaction from negative to positive, regulation does not always succeed in making a difference to the students’ overall emotional experiences.
... The social-emotional process refers to the process in which students make efforts to maintain cohesive and respectful social interaction (Rogat & Adams-Wiggins, 2015) and is dynamically generated by learners in the social interaction in the interpersonal environment (Van den Bossche et al., 2006). Existing research demonstrates that positive social-emotional processes contribute to effective collaboration (Hu et al., 2021, pp. ...
... For example, perceiving a sound and positive emotional climate in a group enables the collaborators to put more effort in cognitive processes and focus on executing proper learning activities . Researchers have identified complex relationships between social emotional and cognitive processes including argumentation (Isohätälä et al., 2018), knowledge construction (Vuopala et al., 2019), and social and co-regulation (Lajoie et al., 2015;Rogat & Adams-Wiggins, 2015). Metacognition is also essential to maintain effective shared regulation in collaborative learning . ...
Emotions play a crucial role in our daily lives, contributing to our mental health as well as to our learning and performance efficiency. Emotions are easily influenced by the surrounding environment and objects, and in response, we may behave differently depending on the interaction between us and the object/environment, which shape social-emotional interactions. In collaborative contexts, social-emotional interactions can affect learners’ cognitive processes, collaboration satisfaction, and learning outcomes. This study selects and reviews current empirical findings on social emotional interactions in collaborative learning contexts, with a special focus on the function of social-emotional interactions in collaboration and how they are measured for research purposes. This paper synthesizes the major findings and addresses the important role shared-regulation plays in maintaining positive emotional interactions in collaborative learning. Furthermore, the paper identifies how emotions are studied in social-contexts and points out advanced methodological applications for future research. Finally, the paper calls for interventions on facilitating sound social emotional-interactions in collaborative learning by providing practical directions for educators and instructors.
... Second, most of the students mainly expressed moderate emotions rather than positive emotions during the PP processes. However, positive social emotion plays an important role to motivate learning interest, lessen tension, and improve social cohesion in collaboration (Rogat & Adams-Wiggins, 2015), such as how Cluster 1 performed in this research. Hence, the engagement of instructors as social supporters during students' collaborative programming, might mobilize the collaborative atmosphere to reach the goal of high-quality PP (Ouyang & Scharber, 2017;Ouyang & Xu, 2022). ...
... SD = 51.33), and 4914 units of facial data (Mean = 258.63; SD = 38.39).Based on the previous relevant literature (Díez-Palomar et al., 2021;Pekrun et al., 2002;Rogat & Adams-Wiggins, 2015;Sun et al., 2020Sun et al., , 2021, a coding framework was proposed to analyze the process data of PP on the verbal communication, operational behavior, and facial expression dimensions (see ...
Pair programming (PP), as a mode of collaborative problem solving (CPS) in computer programming education, asks two students work in a pair to co-construct knowledge and solve problems. Considering the complex multimodality of pair programming caused by students’ discourses, behaviors, and socio-emotions, it is of critical importance to examine their collaborative patterns from a holistic, multimodal, dynamic perspective. But there is a lack of research investigating the collaborative patterns generated by the multimodality. This research applied multimodal learning analytics (MMLA) to collect 19 undergraduate student pairs’ multimodal process and products data to examine different collaborative patterns based on the quantitative, structural, and transitional characteristics. The results revealed four collaborative patterns (i.e., a consensus-achieved pattern, an argumentation-driven pattern, an individual-oriented pattern, and a trial-and-error pattern), associated with different levels of process and summative performances. Theoretical, pedagogical, and analytical implications were provided to guide the future research and practice.
... As aforementioned, with emotion, motivation, and cognitive processes being at the core of learning regulation, emerging technologies would play a vital role in capturing the intricacy of these regulatory processes (Azevedo & Gašević, 2019;Järvelä et al., 2019). While emotions are traditionally assumed to be either an indication of an individual's internal states or an outlet for displaying individuals' orientation to what is happening to others, emotions are also attuned to interpersonal responses (Rogat & Adams-Wiggins, 2015). Emotions have deep influences on learners' cognitive processes, where positive emotions would increase learners' attention, reasoning, and problem-solving, leading to motivating learning behaviors (Tyng et al., 2017). ...
... While the overall learning experiences are evident after an event, it is often difficult to capture and measure the fluidity of the short-term affective states of individuals. In the collaborative learning context, different states of emotions spread and are mimicked amongst all group members through cycles of interactions (Rogat & Adams-Wiggins, 2015). Emotional mimicry, for instance, has been validated by studies as a marker of initial affiliative bond and empathy amongst individuals. ...
Emotional regulation in learning has been recognised as a critical factor for collaborative learning success. However, the "unobservable" processes of emotion and motivation at the core of learning regulation have challenged the methodological progress to examine and support learners' regulation. Artificial intelligence (AI) and learning analytics have recently brought novel opportunities for investigating the learning processes. This multidisciplinary study proposes a novel fine-grained approach to provide empirical evidence on the application of these advanced technologies in assessing emotional regulation in synchronous computer-support collaborative learning (CSCL). The study involved eighteen university students (N=18) working collaboratively in groups of three. The process mining analysis was adopted to explore the patterns of emotional regulation in synchronous CSCL, while AI facial expression recognition was used for examining learners' associated emotions and emotional synchrony in regulatory activities. Our findings establish a foundation for further design of human-centred AI-enhanced support for collaborative learning regulation.
... This study revealed that, in the face of socioemotional challenges that disrupted a group's positive climate, insufficient shared efforts within the group to regulate emotions could undermine group members' enjoyment and engagement in collaborative learning. Rogat and Adams-Wiggins (2015) examined the interrelations between regulatory processes and socioemotional interactions through observations of videotaped collaboration in two four-member groups of middle school students (N = 8) in the United States. Results indicated that facilitative other-directed regulation, such as being inclusive of others' ideas, contributed to a balanced regulation among group members and fostered positive social interactions. ...
... This result suggests that peer-directed regulation contributed to enjoyment in online collaboration indirectly by boosting group-directed regulation. The facilitative role of peer regulation in group regulation supports the argument of Rogat and Adams-Wiggins (2015) and Hadwin et al. (2018) that consistent and productive regulation toward each other in a group enables the entire group's shared regulation to function. ...
Collaborative learning in online contexts is emotionally challenging for language learners. To achieve successful learning outcomes, language learners need to regulate their emotions and sustain positive emotions during the collaborative learning process. This study investigated language learners’ emotion regulation and enjoyment, the most extensively researched positive emotion in foreign language learning, in an online collaborative English learning environment. In the study, we collected data by surveying 336 Chinese students majoring in English who collaboratively completed a series of English language writing tasks in 108 online groups facilitated by a social media app (WeChat). Principal component analysis revealed two primary types of emotion regulation: peer regulation and group regulation. The analysis also revealed one factor underpinning enjoyment: enjoyment of online collaboration. Correlation analysis showed medium and positive relationships between peer regulation, group regulation, and enjoyment of online collaboration. Structural equation modeling analysis further found that group regulation exerted a medium-sized direct effect on enjoyment of online collaboration. Peer regulation affected enjoyment of online collaboration moderately and indirectly via group regulation. The theoretical and pedagogical implications of the findings can help to optimize face-to-face and online collaborative language learning activities.
... In the positive episodes, group affective states remained positive throughout. Previous research has indicated that positive affect and positive socio-emotional interactions can facilitate group level regulation (Linnenbrink-Garcia et al., 2011;Rogat & Adams-Wiggins, 2015). Interestingly, stable positive learning conditions did not seem to invite regulation of learning in the present study, but this is not necessarily at odds with previous findings, as the focus here was on in-situ regulation within short episodes rather than, for example, at learningsession level. ...
... Theoretically, these socio-emotionally critical situations would invite regulation to restore more positive conditions for collaboration (Linnenbrink-Garcia et al., 2011;Mänty et al., 2020). However, previous studies have also shown that when negative interactions are recurring, they can hinder the group's ability to engage in regulation (Bakhtiar et al., 2018;Rogat & Adams-Wiggins, 2015), which might have been the case in this study. In sum, these findings may indicate that socio-emotional interaction can be enough to maintain positive affective states, but strategic regulation of learning may be needed to address socio-emotional challenges before the negative states start accumulating (Bakhtiar et al., 2018;Näykki et al., 2014). ...
Background:
Group affective states for learning are constantly formed through socio-emotional interactions. However, it remains unclear how the affective states vary during collaboration and how they occur with regulation of learning. Appropriate methods are needed to track both group affective states and these interaction processes.
Aims:
The present study identifies different socio-emotional interaction episodes during groups' collaborative learning and examines how group affective states fluctuate with regulation of learning during these episodes.
Sample:
The participants were 54 secondary school students working in groups across four science learning sessions.
Methods:
Multichannel process data (video, electrodermal activity [EDA]) were collected in an authentic classroom. Groups' affective states were measured with emotional valence captured from video data, and activation captured as sympathetic arousal from EDA data. Regulation of learning was observed from the videotaped interactions.
Results:
The study disclosed four clusters of socio-emotional interaction episodes (positive, negative, occasional regulation, frequent regulation), which differed in terms of fluctuation of affective states and activated regulation of learning. These clustered episodes confirm how affective states are constantly reset by socio-emotional interactions and regulation of learning. The results also show that states requiring regulation do not automatically lead to its activation.
Conclusions:
By advancing existing understanding of how group level socio-emotional processes contribute to regulation of learning, the study has implications for educational design and psychological practice. Methodologically, it contributes to collaborative learning research by employing multiple data channels (including biophysiological measures) to explore the various dimensions of socio-emotional processes in groups.
... When working together toward a common goal, group members can experience shared enjoyment of learning (Anttila et al., 2018) or encounter different kinds of socio-emotional challenges (Näykki et al., 2014) creating unique affective experiences for group members. Affect is constantly present as a condition influencing group members' interactions and behaviors (Winne & Hadwin, 2008) and can foster processes beneficial for collaborative learning (Barron, 2003;Rogat & Adams-Wiggins, 2015) but if socio-emotional challenges are not successfully regulated, have detrimental effects on group members' collaboration (Bakhtiar et al., 2018). Understanding emotional variations in group members' shared affective space could be instrumental in studying the role of affect in the collaborative learning process and, for example, in locating emotionally relevant situations to study and support group level emotion regulation in various learning contexts. ...
... Zschocke et al. (2016) studied individual group work appraisals and emotions arising in the group work context and found that appraisals of the cognitive benefits of group work were a significant predictor of positive activating emotions, and experiences of negative activating and deactivating affect were mostly associated with task management and group assessment aspects. Positive socio-emotional interactions have been linked to positive affect , favorable socio-emotional atmosphere (Bakhtiar et al., 2018;Kwon et al., 2014), processes beneficial for collaborative learning such as high-level cognitive processes (Barron, 2003;Isohätälä et al., 2018;Järvelä et al., 2016a), and facilitative group level regulation (Rogat & Adams-Wiggins, 2015;. In turn, negative interaction has been linked to negative affect and, when persistent, shown to constrain groups' regulatory actions (Bakhtiar et al., 2018). ...
During collaborative learning, affect is constantly present in groups’ interactions, influencing and shaping the learning process. The aim of this study was to understand what type of learning situations trigger affective states in collaborative groups, and how these affective states are related to group members’ physiological activation. The participants were 12-year-old primary school students (N = 31, 10 groups) performing a collaborative science task. In the analysis, video data observations were combined with data of group members’ physiological activation. The groups’ situational valence was identified based on the group members’ observed emotional expressions and their physiological activation levels were measured with electrodermal activity (EDA). Results revealed that situations with group members’ simultaneous physiological activation were rare compared with the observable emotional expressions. However, when group members indicated physiological activation simultaneously, they also showed visible emotional expressions more often than in deactivating situations. Moreover, the results showed that socially-related factors were more likely to trigger physiological activation with a mixed group level valence. In turn, task-related factors were more likely to trigger physiological activation with a neutral group level valence. The results of this study imply that by combining different process data modalities revealing the different components of affect, it might be possible to track emotionally meaningful situations that shape the course of the collaborative learning process.
... Sinha et al. (2015) found that low-engagement groups used words that indicate a focus on individual thinking and activity, such as "I think", "I am going to", and "my turn", while high-engagement groups used words that refer to the collective (e.g., we). Rogat and Adams-Wiggins (2015) developed a positive and negative socio-emotional interaction coding scheme. The positive interactions include active listening and respect, inclusion and encouraging participation of a group member or the whole group, group cohesion, discouraging marginalization, appealing to disciplinary norms, and mistakes as informational gaps . ...
Engagement is critical in learning, including computer-supported collaborative learning (CSCL). Previous studies have mainly measured engagement using students’ self-reports which usually do not capture the learning process or the interactions between group members. Therefore, researchers advocated developing new and innovative engagement measurements to address these issues through employing learning analytics and educational data mining (e.g., Azevedo in Educ Psychol 50(1):84–94, 2015; Henrie in Comput Educ 90:36–53, 2015). This study responded to this call by developing learning analytics to study the multifaceted aspects of engagement (i.e., group behavioral, social, cognitive, and metacognitive engagement) and its impact on collaborative learning. The results show that group behavioral engagement and group cognitive engagement have a significantly positive effect on group problem-solving performance; group social engagement has a significantly negative effect; the impact of group metacognitive engagement is not significant. Furthermore, group problem-solving performance has a significant positive effect on individual cognitive understanding, which partially mediates the impact of group behavioral engagement and fully mediates the impact of group social engagement on individual cognitive understanding. The findings have important implications for developing domain-specific learning analytics to measure students’ sub-constructs of engagement in CSCL.
... Our results show that some groups experienced motivation (or various levels of motivation) within the group as a challenge. The role of emotional and motivational challenges in regulatory processes is increasingly highlighted by the literature (e.g., Rogat & Adams-Wiggins, 2015;Järvenoja et al., 2019). How group members interact with each other can have a significant effect on a group's regulation. ...
In recent years, researchers have shown increased interest in the question of how groups regulate their collaborative work and how this in turn affects their learning experience. There is a lack of empirical studies that explore social regulation in student group work. This study in progress attempts to identify instances of social regulation of learning in group work through examining challenges that students experience throughout interdisciplinary group projects. Building on existing conceptual work, we target different dimensions of social regulation-Planning, Monitoring/Performance and Evaluation. Data is collected from four courses within Tracks-a ten-year educational initiative, aiming to respond to the changing educational needs of future engineers. Within Tracks, students meet and learn collaboratively across programme boundaries and take on relevant challenges with a basis in real-world problems together. Students were asked to self-report in form of reflective writings about challenges and coping strategies. First results indicate that groups employed different forms of social regulation though their affiliation with different study programs made it difficult to schedule collaborative, synchronous meetings. Our findings further highlight the role of motivation in collaborative group work and stimulate a discussion about 'desirable challenges' that act as catalysts for learning in group work.
... Recently emerging CSCL studies situated in this problem space have examined socio-emotional and historical reasoning of diverse groups around historically charged topics (e.g., Schwarz & Goldberg 2013); dynamics between emotional regulation processes and quality of discourse during emotionally charged discussions (Firer et al., 2021); development and evaluation of new technological tools in moderation of charged discussions and how emotions are expressed and regulated in this context (Slakmon & Schwarz, 2019); and developing an understanding for intersubjective meaning-making across disagreements in historically charged discussions (e.g., disagreement and moral judgment during Jewish and Arab students' collaborative inquiry, Pollack & Kolikant 2012). There are also extensive scholarly efforts in CSCL focusing on conceptualizing high quality and dysfunctional collaborative processes more generally (e.g., Borge et al., 2018;Chan et al., 2019;Mercer & Higgins, 2014;Rogat & Adams-Wiggins, 2015). However, these efforts lack a concrete framework for assessing the quality of sense-making discussions that center on communication patterns specific to discussions of politically charged topics or topics that bring the social identities of participants into the discussion as integral aspects of the discussion. ...
Shared meaning-making across differences in today’s polarized society requires a socio-political perspective toward conceptualizing and operationalizing collaborative competence. Thus, there is a pressing need for socio-political pedagogies and designs in CSCL to empower students as cultural-historical agents who can communicate and work effectively across different communities. As the initial steps of our larger efforts to conceptualize and operationalize a model of multicultural collaborative competence (MCC), we explore communication patterns associated with productive and dysfunctional shared meaning-making around difficult topics related to identity (e.g., race, gender) during intergroup dialogues in a CSCL context. We also examine how our preexisting, general model of collaborative competence (GCC) aligns with these communication patterns to explore (1) whether GCC is robust enough to capture the socio-political dynamics of difficult dialogues and (2) the ways in which we could modify it to better address the tensions between GCC and MCC goals. We collected the discussion transcripts of four three-person teams over two-time points from an undergraduate Multicultural Psychology course. We first conducted thematic and cross-case analyses to identify the communication patterns and behaviors associated with productive and dysfunctional shared meaning-making processes in the context of difficult dialogues (i.e., MCC). We then employed another set of cross-case analyses to examine the relationship between the multicultural collaborative competencies (MCC) and general collaborative competencies (GCC). We found four main communication patterns associated with MCC: (1) grounding with narratives and aims, (2) exploring differences and commonalities of narratives/perspectives, (3) critical reflection of diverse narratives/perspectives, and (4) providing emotional support to team members. We also found that although the GCC does not cover these communication patterns and associated behaviors, there were some overlaps between the sophistication of multiculturally competent communication patterns and collaboration quality as defined by the GCC.
... Prior research in this domain of literature has explored interaction patterns, identified processrelated dialogue acts at both individual and social levels, and examined how they relate to collaborative discourse quality (Borge & Caroll, 2010;Borge & Caroll, 2014;Borge et al., 2015;Kwon, Liu, & Johnson, 2014;Rogat & Adams-Wiggins, 2015). However, the number of such studies are scarce, and their findings are inconclusive. ...
... On the contrary, students from type 2 tended to focus more on completing the task on their Miro boards and gazing at their laptops. Type 1 sequences of gaze behaviours might be better associated with interactive and socio-emotional dimensions [12] of collaborative learning. These gaze sequences are more likely to occur when students are interacting with peers, actively listening to others, encouraging participation and inclusion of peers etc. ...
Several studies have shown a positive relationship between measures of gaze behaviours and the quality of student group collaboration over the past decade. Gaze behaviours, however, are frequently employed to investigate i) students’ online interactions and ii) calculated as cumulative measures of collaboration, rarely providing insights into the actual process of collaborative learning in real-world settings. To address these two limitations, we explored the sequences of students’ gaze behaviours as a process and its relationship to collaborative learning in a face-to-face environment. Twenty-five collaborative learning session videos were included from five groups in a 10-week post-graduate module. Four types of gaze behaviours (i.e., gazing at peers, their laptops, tutors, and undefined objects) were used to label student gaze behaviours and the resulting sequences were analyzed using the Optimal Matching (OM) algorithm and Ward’s Clustering. Two distinct types of gaze patterns with different levels of shared understanding and collaboration satisfaction were identified, i) peer-interaction focused (PIF), which prioritise social interaction dimensions of collaboration and ii) resource-interaction focused (RIF) which prioritise resource management and task execution. The implications of the findings for automated detection of students’ gaze behaviours with computer vision and adaptive support are discussed.
... Il est également survenu dans les situations de conflit que l'une des personnes impliquées adopte une approche plus directive, par exemple, en tentant d'imposer sa position ou en rejetant la proposition de l'autre personne, comme il s'est produit dans le groupe Fournisseurs. Dans de telles situations, Rogat et al. (2015) avaient observé que, lorsque des participants tentent de réguler les autres participants de façon directive, qu'elles tentent de gérer et de contrôler les autres, elles peuvent manquer de respect envers les contributions des autres membres du groupe (renforcement de la motivation et de la confiance). De plus, l'ignorance et le désaccord avec un membre de l'équipe peuvent laisser entendre que leurs contributions ne sont pas valorisées (renforcement de la motivation et de la confiance). ...
... Socio-emotional interaction consists of purposeful interchanges between students to express and shape perceptions of emotions and the group's socio-emotional atmosphere (Kreijns et al., 2003;Bakhtiar et al., 2018;Mänty et al., 2020). Positive socio-emotional interactions have been found to facilitate co-and socially shared regulation of learning Lajoie et al., 2015;Rogat and Adams-Wiggins, 2015). Meanwhile, negative socio-emotional interactions hinder the collaborative learning process by affecting the quality of group learning activities and have been linked to negative emotional experiences of collaboration among group members (Mänty et al., 2020). ...
Emotions in collaborative learning both originate from and are externalized in students' socio-emotional interactions, and individual group members evidently contribute to these interactions to varying degrees. Research indicates that socio-emotional interactions within a group are related with the occurrence of co-and socially shared regulation of learning, which poses a need to study individual contributions to these interactions via a person-centered approach. This study implements multimodal data (video and electrodermal activity) and sequence mining methods to explore how secondary school students' (n = 54, 18 groups) participation in socio-emotional interactions evolved across a series of collaborative tasks. On this basis, it identifies subgroups of students with distinct longitudinal profiles. Furthermore, it investigates how students with different socio-emotional interaction profiles contributed to their groups' regulation of learning. Three profiles were identified: negative, neutral, and diverse. Each profile represents a particular socio-emotional interaction pattern with unique characteristics regarding the emotional valence of participation and physiological emotional activation. The profiles relate to students' contributions to group regulation of learning. Students with the diverse profile were more likely to contribute to regulation, whereas the neutral profile students were less likely to contribute. The results highlight the importance of person-centered methods to account for individual differences and participation dynamics in collaborative learning and consequently clarify how they relate to and influence group regulation of learning.
... En los procesos de autorregulación coexisten elementos cognitivos, motivacionales y sociales, los cuales se encuentran fuertemente enraizados en aspectos emocionales que pueden generar conflictos cuando se trabaja de manera conjunta que pueden determinar el éxito o fracaso de una tarea escolar realizada de manera colaborativa (Koivuniemi, Järvenoja & Järvelä, 2018;McCaslin & Murdock, 1991;Panadero, Kirschner, Järvelä & Järvenoja, 2015;Rogat & Linnenbrink-Garcia, 2011;Rogat & Adams-Wiggins, 2014;Rogat & Adams-Wiggins, 2015;Sobonciski, Järvelä, Malmberg & Muhterem, 2020 ;Zheng & Huang, 2015). ...
Se presenta y analiza la adaptación al contexto colombiano de un instrumento para evaluar la naturaleza adaptativa
de la regulación emocional en situaciones de aprendizaje colaborativo (AIRE), propuesto por Järvelä, Järvenoja &
Veermans (2008). Participaron 67 estudiantes de grados 8º, 9º y 10º de una institución femenina del sector público
de la ciudad de Manizales (Colombia) con edades entre 13 y 17 años (M=15,03 años DE=0,97 años). Los participantes,
dispuestos en 12 grupos de trabajo permanentes, siguen una dinámica de aprendizaje colaborativa en todas la áreas
y materias. Se examinaron estadísticos descriptivos y análisis de reducción de dimensiones a través de la técnica
ACP para cada sección del instrumento. Los resultados mostraron que, mientras las secciones uno y tres parecen
coincidir con lo planteado teóricamente en la consolidación del instrumento, la dimensionalidad encontrada difiere
de la propuesta por sus autores en la sección dos. Los resultados mostraron que el instrumento contribuye al análisis
y evaluación de objetivos personales, conflictos socio emocionales, formas de regulación y reflexión metacognitiva
sobre la percepción individual y grupal del objetivo alcanzado. Se propone una nueva dimensionalidad en razón de la
adaptación, los análisis y la congruencia conceptual de algunos de sus ítem
... These individual reactions are also meaningful on a group level since they shape the group's affective state and socio-emotional atmosphere (Bakhtiar et al., 2018). Earlier research has evidenced that students' emotions can foster processes beneficial for collaborative learning, such as high-level cognitive processes (Järvelä et al., 2016;Rogat & Adams-Wiggins, 2015); however, if not successfully regulated, emotions can lead to socio-emotional challenges and have detrimental effects on collaboration (Baker et al., 2013). ...
This study explored the interplay between students' group-level emotion regulation behavior and affective conditions and products of regulation (emotional valence, activation, participation). The participants were 12-year-old students (N = 31, 10 groups) performing a collaborative science task. Conditions, emotion regulation behavior, and products of regulation were captured from video and electrodermal activity data. Results reveal that affective conditions were related to students' regulatory behavior. Students were more likely to initiate regulation when they indicated a personal need to restore affective grounds. Moreover, regulation was activated to restore participation by targeting regulation to non-participating students. While regulation did not always change conditions for collaboration, the results indicate that it was more influential for students who either initiated or were targets for regulation.
... The second coding scheme (Online Appendix C) examined the different types of socio-emotional interactions identified in several previous studies Näykki et al., 2014;Rogat & Adams-Wiggins, 2015). Text-based chats often involve the use of memes, which cannot be simply categorised as positive or negative socio-emotional interactions due to their rich meanings and contexts. ...
Students’ social knowledge construction and socio-emotional interactions in computer-supported collaborative learning (CSCL) are shaped by one another and work together to affect the group’s learning performance. However, few studies have combined both social knowledge construction and socio-emotional interactions and examined how they contribute to improved learning performance. This study examines the dynamics of students’ social knowledge construction and socio-emotional interactions in the context of computer-supported collaborative writing and compares six high- and six low-performing groups. Quantitative content analysis and sequential analysis were used to reveal the characteristics of groups’ behaviour frequencies and patterns. The high-performing groups demonstrated more systematic and meaningful social knowledge construction and socio-emotional interaction patterns, while the low-performing groups only engaged in single repeated behaviours. It is worth noting that memes played different roles in the two groups.
... It would consequently be interesting to include variables such as individual students' academic achievement, learning objectives, prior knowledge, self-efficacy, competence in self-regulation, or experienced cognitive load (Iiskala et al. 2015;Kirschner et al. 2009;Malmberg et al. 2015;Miller and Hadwin 2015) in future studies, to investigate whether and how these affect students' SSMR. Additionally, integrating variables such as positive/negative socio-emotional interactions within RPT-groups, their argumentative discussions, or collaborative learners' feeling of belongingness (Isohätälä et al. 2017;Rogat and Adam-Wiggins 2015) might shed light on the impact of group-related features on the adoption of SSMR and on students' performance. In this respect it would also be worthwhile investigating whether and how the content of peers' discussions is related to students' engagement in SSMR, for it could be that particularly qualitative discussions facilitate the exchange of multiple perspectives (Khosa and Volet 2014). ...
This study investigates (1) the impact of structuring versus reflection-provoking support on university students’ adoption of socially shared metacognitive regulation (SSMR) during face-to-face peer tutoring (PT) and (2) the relation between SSMR and group performance. A quasi-experimental design was adopted, involving 72 educational sciences students who were randomly assigned to PT-groups of six. Each group was provided with either structuring (SS) or reflection-provoking (RS) support. The training and closing PT-session of six groups in each support condition were videotaped (48 h). SSMR was studied by means of systematic observation of video-recorded PT sessions, whereas PT groups’ score on the assignment during the last PT session served as performance measure. The results revealed only significant differences in SSMR between both support conditions, when the proportion of students actively involved in SSMR, was taken into consideration. More specifically, PT groups in the RS condition revealed significantly more SSMR in which (nearly) all students are engaged, as compared with PT groups in the SS condition. The correlational analyses further indicated that only SSMR representing a high participation degree of (nearly) all students is significantly positively related to PT groups’ performance.
... joy, enthusiasm) or negative affect (e.g. frustration, boredom) were found to set the group climate and influence the quality of learning [23]. ...
This study explores the potential of emotional mimicry in identifying the leader and follower students in collaborative learning settings. Our data include video recorded interactions of 24 high school students who worked together in groups of three during a collaborative exam. A facial emotions recognition method was used to capture participants' facial emotions during the collaborative work. Cross-recurrence quantification analysis was applied on the detected facial emotions to see the level and direction of emotional mimicry among the dyads in the same groups. In order to validate the cross-recurrence quantification analysis results, student interactions in terms of leading or following the task were video coded. Our findings showed that the leaders and followers identified by cross-recurrence quantification analysis findings matched the leaders and followers identified by the video coding in 70% of the dyadic interactions across the collaborating groups. The current findings show that video-based facial emotions recognition as a method can add to collaborative learning research, especially explaining some social, and affective dynamics about it. The study further discusses the possible variables that might confound the relationship between emotional mimicry and leader-follower interactions during collaboration.
... Research has shown that groups do not recognize and react to challenging learning situations and, thus, cannot explicitly regulate their learning in such situations (Järvelä et al., 2016;Rogat & Adams-Wiggins, 2015). This means that there is a need for them to be alerted. ...
Collaborative learning (CL) can be a powerful method for sharing understanding between learners. To this end, strategic regulation of processes, such as cognition and affect (including metacognition, emotion and motivation) is key. Decades of research on self‐regulated learning has advanced our understanding about the need for and complexity of those mediating processes in learning. Recent research has shown that it is not only the individual's but also the group's shared processes that matter and, thus, that regulation at the group level is critical for learning success. A problem here is that the “shared” processes in CL are invisible, which makes it almost impossible for researchers to study and understand them, for learners to recognize them and for teachers to support them. Traditionally, research has not been able to make these processes visible nor has it been able to collect data about them. With the aid of advanced technologies, signal processing and machine learning, we are on the verge of “seeing” these complex phenomena and understanding how they interact. We posit that technological solutions and digital tools available today and in the future will help advance the theory underlying the cognitive, metacognitive, emotional and social components of individual, peer and group learning when seen through a multidisciplinary lens. The aim of this paper is to discuss and demonstrate how multidisciplinary collaboration among the learning sciences, affective computing and machine learning is applied for understanding and facilitating CL.
Practitioner Notes
What is already known about this topic Collaborative learning occurs when team members systematically activate, sustain and regulate their cognition, motivation, emotions and behaviors towards the attainment of their goals.
Socially shared regulation in learning contributes to success in collaborative learning.
What this paper adds “Shared” processes in collaborative learning are hard for researchers to study and understand them, for learners to recognize them and for teachers to support them.
Multimodal data collection provides opportunities to study multiple aspects of student behaviors and regulation processes.
With the aid of advanced technologies multidisciplinary collaboration between the learning sciences, affective computing and machine learning can help to study these complex phenomena.
Implications for practice and/or policy The case examples demonstrate how multidisciplinary collaboration can meet the challenges in understanding and facilitating collaborative learning.
Multidisciplinary efforts with multimodal data will contribute to collaborative learning practice by providing theoretically informed feedback and personalized support.
... The curriculum also affords engagement in peer interactions and opportunities to engage in discipline-relevant discourse (Blumenfeld, Kempler, & Krajcik, 2006;Rogat, Witham, & Chinn, 2014). Nonetheless, previous findings suggest learners may engage in practices that lead to reduced participation by groupmates (Adams-Wiggins & Rogat, 2013;Cornelius & Herrenkohl, 2004;Rogat & Adams-Wiggins, 2015). ...
(Free to download through January 2020): Recent research emphasizing disciplinary identities in the classroom indicates the importance of social interaction and inclusion in the classroom, yet only limited work focuses on how peer-initiated exclusion impacts learners. This study addresses that gap by examining the role of microexclusions, or affronts to sense of belonging and competence, in collaborative groups in 7th grade inquiry science classrooms. The qualitative analyses here involved videorecorded observations for 5 small groups of students participating in a semester-long series of inquiry life science units. A total of 19 observations were analyzed across the 5 groups. Five themes were identified across the groups: individualization or splitting of the group, adversarial interactions within the group, uneven access to regulatory roles within the group, lagging group members, and using diffuse status characteristics to redirect group activity. Results indicate that microexclusions redirect learners' behavior toward managing participation dynamics inside the group at the cost of inclusion and group functioning. Implications for equity and science education reform are provided considering findings.
... Therefore, coregulation of learning (CoRL) and socially shared regulation of learning (SSRL) ( Hadwin et al. 2018) are especially critical, as people need to continuously collaborate to solve today's and tomorrow's complex problems. Research has shown that groups do not recognize challenging learning situations and their need for regulation ( Järvelä et al. 2016b), which restricts group members' activation of strategic adaptation in those situations (Rogat and Adams- Wiggins 2015), and thus, they need to be alerted to this need. ...
Self-regulation is critical for successful learning, and socially shared regulation contributes to productive collaborative learning. The problem is that the psychological processes at the foundation of regulation are invisible and, thus, very challenging to understand, support, and influence. The aim of this paper is to review the progress in socially shared regulation research data collection methods for trying to understand the complex process of regulation in the social learning context, for example, collaborative learning and computer-supported collaborative learning. We highlight the importance of tracing the sequential and temporal characteristics of regulation in learning by focusing on data for individual- and group-level shared regulatory activities that use technological research tools and by gathering in-situ data about students’ challenges that provoke regulation of learning. We explain how we understand regulation in a social context, argue why methodological progress is needed, and review the progress made in researching regulation of learning.
Building on social constructivist theory, this case study analyzed how pre-service secondary teachers co-constructed knowledge and expressed socioemotional interaction in online breakout rooms during a collaborative task. Video data was analyzed by content and interaction analysis. There was more higher-level knowledge construction than in most studies from asynchronous settings. Active listening and humor were thoroughly present. Talk about personal experiences occurred at both lower and higher levels of thinking. The teacher educator’s visits to the breakout rooms and purposeful dissonance affected knowledge co-construction and socioemotional interaction. The findings will help in designing high-quality online and blended teacher education.
U ovoj monografiji razmatra se značaj interakcije vršnjaka adolescentskog uzrasta prilikom zajedničkog rešavanja proble¬ma za razvoj njihovog mišljenja. Akcenat je prvenstveno na vršnjačkoj interakciji kao faktoru razvoja mišljenja, dok su karakteristike i razvoj mišljenja tokom adolescencije detaljno razmatrani u prethodnoj monografiji autorke (Stepanović, 2007).
Monografija je podeljena u četiri tematske celine. U prvoj celini, koja se sastoji iz tri poglavlja, predsta¬vljene su teorijske osnove istraživanja uloge vršnjačke interakcije u kognitiv¬nom razvoju tokom adolescencije, odnosno teorije Pijažea i Vigotskog kao naj¬uticajnji pristupi u oblasti razvojne psihologije koji se bave ovim problemom. Druga celina, koju takođe čine tri poglavlja, prati razvoj istraživanja koja su potekla iz pomenuta dva teorijska pristupa. Treća celina, koju čine dva poglavlja, posvećena je radovima autora koji se bave obrazovanjem i posmatraju vršnjačku interakciju kao pedagošku strategiju važnu za ostvari¬vanje obrazovnih ciljeva. Četvrta celina, kroz dva poglavlja, donosi prikaz istraživanja sprovedenih u prethodne dve decenije koja su se bavila dijalogom vršnjaka tokom zajednič¬kog rešavanja problema i ulogom vršnjačke interakcije u kognitivnom razvoju adolescenata. Na kraju autorka sumira sve što je iznela u prehodnim tematskim celinama prikazujući i šemu kojom se mogu predstaviti dosadašnja saznanja o fenomenu kojim se bavi u ovoj momografiji, ali i ukratko razmatra najvažnije implikacije značaja vršnjačke interakcije u ovom razvojnom periodu za obrazovni proces.
In this chapter, we outline how modes of interaction, such as cognitive and socio-emotional, and the regulation of learning provide support for collaborative engagement and examine how it changes over time. We start by framing how regulated learning is embedded in the cognitive and socio-emotional interaction between the group members from both a theoretical and a methodological perspective. We then move to illustrate, with an empirical case example, how multimodal data (i.e., video) and physiological signals, such as electrodermal activity indicating physiological synchrony between the group members, can be used to capture varying levels of collaborative engagement. The empirical example provides a complementary view on group interaction and collaborative engagement. We conclude by discussing how investigating group interaction that targets regulation can reveal how collaborative engagement is built and maintained. Additionally, we discuss future possibilities to harness multimodal data in practice to support collaborative engagement.
My doctoral thesis explored affect as social relational phenomena in groupwork contexts, revealing how affect is co-created by group members and can be used to characterize groups’ co-constructed norms, and group climate. Conceptualizing and exploring the dynamic nature of affect in moment-to-moment group interactions also contributes to broadening the earlier emphasis on group achievement outcomes by considering productivity as a continually facilitated process toward collaborative achievements including relational.
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.
Self-regulated learning (SRL) provides the foundation for building sustainable knowledge and is therefore important for schools, classrooms, and lifelong learning in general. Especially in vocational education and training, the concept of SRL remains fundamental as it relates to preparing future employees. However, further research is needed on how vocational students situationally regulate their learning process and the extent to which this may be related to a dispositional change in their SRL. In this study, we analyzed longitudinal questionnaire data from 159 students who attended either SRL-conducive or regular vocational classes. We refer to Perry and colleagues' (2018) framework of an SRL-conducive learning environment, which focuses on (meta)cognitive, motivational, and emotional aspects of learning. Using multilevel analysis, we found differences in the development of (meta)cognitive components of learning, whereas no clear differences could be identified for motivational and emotional components. The results support the assumption that process analyses can be used to draw a more differentiated picture of SRL in vocational schools. Moreover, indirect approaches to promoting SRL should be designed to include all SRL-relevant aspects.
In this entry, we review small group learning research and discuss practical implications concerning the use of cognitive with social interactional processes in collaborative activity. This review accounts for empirical research beginning in the mid-1990s, when the field began to recognise between-group variation in academic achievement, shifting from an earlier study of group structure and individual cognitive processes for explaining individual achievement outcomes. This paradigm shift forefronted the group as a collective and as a primary subject of study, refocusing the field on the investigation of interactional processes among members of the group. Given that collaborative group activity is fundamentally social, researchers soon realised that cognitive processes, which explained the variations of highly functioning and productive groups, depended on and were interrelated with the socioemotional and motivational processes operating within the group (Barron, 2003); this study of the cognitive with the social is the primary subject of this synthesis. We close our discussion with recommendations for policy and future research.
In order to cultivate students to be able to participate in public affairs and make decisions about socioscientific issues (SSI), a web-based module was designed for students to collaboratively engage in the decision-making (DM) process. This study attempted to identify students’ discourse characteristics that might lead to formulating an evidence-based decision on SSI. Twenty-nine Grade 10 students were randomly divided into eight groups of three or four. The transcribed data of one case from each performance level were compared to investigate the interplay between groups’ DM performances and discourse characteristics. The results showed that the group that gained a high score on the DM group worksheet engaged in the metacognitive discussion for planning procedures of the module tools and in the conceptual exchanges to accomplish the tasks. The members of this group could initiate and extend ideas, provide prompts, and confirm or reject each other’s ideas, resulting in sustained interactive dialogs that allowed them to learn from one another. This indicated that students need to be encouraged to clarify the task goals, plan procedures, monitor their performance, and exchange their ideas actively. The implications of how collaborative discourse promote students’ SSI DM performance, and the better design and enactment of SSI modules are discussed.
In this chapter the construct of metacognition is explored along with its role in the translation process. The chapter discusses major approaches to this concept in psychological, educational and translation theory (Sect. 4.1). To identify the conceptual framework for metacognition, the chapter reviews models of self-regulated learning (Sect. 4.2) and the need for a transition from other-regulation to self-regulation (Sect. 4.3). It highlights the role of metacognitive strategies as a facilitative factor in the professional career of translators (Sect. 4.4). The nature of self-regulation and the possible role that it plays in translation are analysed here, followed by the results of a study on the effects of students’ self-regulation on translation quality. Given the relevance of metacognition as a developmental factor in the professional career of a translator, however complex it may be for the teacher to play yet another role and activate students’ personal resources, introducing metacognition-supportive training appears to be of clear importance.
Collaborative problem solving, as a key competency in the 21st century, includes both social and cognitive processes with interactive, interdependent, and periodic characteristics, so it is difficult to analyze collaborative problem solving by traditional coding and counting methods. There is a need for a new analysis approach that can capture the temporal and dynamic process of collaborative problem solving in diversity online collaborative learning context to provide some insights into online collaborative learning design. During an eight-week semester, a total of 42 student teachers participated in two online collaborative learning activities. Student teachers' discourse data were collected, and the data were coded based on a collaborative problem solving assessment model. This study used Epistemic Network Analysis (ENA) to explore the collaborative problem solving processes of student teachers in different online collaborative learning tasks. The results showed that both the high and low academic performance groups worked to maintain positive communication, but the students in the high academic performance groups negotiated on ideas while the students in the low academic performance groups focused on sharing resources/ideas. Moreover, fine-grained centroid analysis on a weekly basis showed that the high academic performance groups began by maintaining positive communication, and ended by negotiating ideas, while the low academic performance groups began by sharing resources/ideas and ended by regulating problem solving activities. Finally, the implications, limitations, and future research were discussed.
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.
Despite an increase in research on social regulation of learning, studies on socially shared metacognition are still scarce. This has led to a lack of understanding concerning how groups co-construct metacognitive knowledge, skills, and experiences. In this comparative case study, we qualitatively analyzed video recordings from the meetings of six groups of pharmacy graduate students. For this, we developed a coding scheme that characterized the metacognitive processes of small groups in a project-based learning environment. Using log data collected from a collaboration app, we distinguished which groups rated themselves the highest and lowest overall for metacognitive experiences and then examined differences in the socially shared metacognition processes between these groups. We were able to map 100 strategy codes into four categories with various subcategories representing the cognitive and metacognitive processes used by both groups. We also found that the two groups did not differ on the proportions of different modes of regulation but did differ qualitatively, with the high self-rated group's strategy enactment being more deliberate, targeted, and cohesive than that of the lower self-rated group. Our findings expand understanding of socially shared metacognitive strategies, which has implications for those who aim to improve collaboration by promoting appropriate group-level processes.
The aim of this study is to explore how students experience and describe socio-cognitive and socio-emotional challenges in collaborative learning. The participants (N = 20) were teacher education students whose collaborative learning was supported with a designed regulation macro script during a six-week mathematics course. The purpose of the script was to provide structured phases during the collaborative learning tasks for the group members to plan, monitor, and evaluate their workings. The video data of groups' face-to-face work was collected and analysed by focusing on the different types of challenges the groups experienced and the types of challenges they described during the scripted interaction. The results indicate that the groups experienced more socio-cognitive challenges than socio-emotional challenges. The script provided them a moment to verbalize their emotional experiences, name the emotions (i.e. frustration), and attribute the challenges and emotions more precisely than during their mathematical task. The intertwining characteristics of socio-cognitive and socio-emotional challenges were observable. Collaborative learning can be challenging for groups, and thus, the knowledge of and the ability to implement practices for becoming aware of challenges can provide a direction for students to progress towards more productive collaboration.
Naturally, every collaboration will bring conflicts that can affect the performance of a team. The earlier a conflict is detected and managed in a collaborative group, the better. Detecting and tracking conflicts in Computer-Supported Collaborative Learning (CSCL) is laborious work. If the teacher does it, the intervention may be out of time. Although written dialogues in groups having a conflict reveal the increment of negative emotions in comparison to non-conflict dialogues, a classifier that only uses statistics of the valence of consecutive messages in a window of the talk shows poor performance. This paper proposes to use features based on the valence change between a message and its response. In this way the algorithm focuses in the kind of interaction. We study different implementations of the bootstrap aggregating technique to detect conflicts. Results obtained show the viability of the proposed approach.
Students working in small collaborative groups may experience conflicts due to emotional issues at the individual or group level. Students need to regulate these emotions to avoid or reduce negative socioemotional interactions that can interfere with group performance. In this article, we studied the socioemotional regulation strategies used by graduate pharmacy students as they worked together in a small-group project-based learning environment. For this, we video recorded groups of students working on a class project in an authentic learning context. We conducted a qualitative extreme case study of three groups who, over six weeks, collectively rated their emotions as low, medium, and high to determine how the groups regulated their emotions, as well as the similarities and differences between the groups. We conducted three analyses: code mapping, descriptive, and thematic. We found that the socioemotional regulation strategies fell into one of the following five themes: behavioral, interpersonal, cognitive, motivational, and a combination of motivational and cognitive. We found that the most commonly used strategies were interpersonal and that the strategies were used at varying interpersonal levels (i.e., self, peer, and group). We also found that some groups used more appropriate strategies and that the use of strategies may have been connected to individual differences and pre-existing relationships between group members. Understanding which strategies are useful in specific collaborative contexts can help educators guide groups of students to effectively regulate their emotions.
Collaborative learning in small groups is a societally relevant but challenging way of learning. It requires a rich understanding of how people think and co-elaborate knowledge together (cognitive processes) and how they feel and relate to each other (socio-emotional processes). The objective of this dissertation is to explore the interplay of cognitive and socio-emotional processes as it manifests in face-to-face social interaction during collaborative learning. The results were derived from qualitative, process-oriented analyses of video-recorded social interactions in two datasets pertaining to small groups of Finnish teacher education students (N=43) who collaborated on mathematics and environmental science tasks. The results are reported in four empirical articles.
The results show that the cognitive and socio-emotional processes fluctuated in the social interactions over the course of collaborative learning. The socio-emotional processes became especially overt and thematic in the social interactions when groups regulated their learning. During such regulation, groups’ metacognitive planning, monitoring, and evaluating could intertwine expressions of emotion, talking about emotions, or giving socio-emotional support. These moments activated group members’ joint participation and allowed them to establish agreement, respond to challenges, and recognize strengths or weaknesses, which were important functions for collaborative learning. At times, the social interaction was more directed toward cognitive processes when group members concentrated on performing task activities. However, the socio-emotional processes were still intertwined with cognitive processes. This dissertation illustrates how a case episode of argumentation proceeded through a series of counterarguments, reformulations, and elaborations, but also involved subtle ways of expressing claims tentatively, showing consideration of divergent claims, and relaxing tension.
This dissertation highlights that cognitive and socio-emotional processes of collaborative learning are continuously intertwined but fluctuate in social interaction. The intertwining gives rise to meaningful functions for collaborative learning. Attempts to support collaborative learning in education or work must acknowledge the interplay of cognitive and socio-emotional processes in social interaction.
The cognitive and social demands of collaboration can raise significant motivation challenges. Task progression relies on team members strategically taking control of the problems and adapting accordingly. Theory indicates that productive collaboration involves groups using three modes of regulation: self-regulation, co-regulation, and socially shared regulation. Despite research demonstrating the occurrence of all three modes in collaboration, it is unclear how these modes interact and how co-regulation supports the emergence of self-and shared-regulation of motivation. The study aimed to examine the role co-regulation played in dynamically stimulating the emergence of self-and shared-regulation of motivation. A cross-case comparison was conducted between two groups who experienced high levels of motivation challenges but achieved contrasting perceptions of the overall team learning productivity. During analysis, groups' dynamic regulatory processes within the online environment were visually represented using a tool called the Chronologically-ordered Representation for Tool-Related Activity (CORDTRA). Findings demonstrate that co-regulation of motivation may afford and thwart the emergence of self-and shared-regulation, and these processes interacted with the group's situational challenges and the regulatory skills group members possessed. Comparisons between the two groups indicated that groups' motivation regulation should (a) match the demands of the challenges at hand, (b) be positively supported by group members through co-regulation, and (b) involve a more varied strategic responses so that the group may continue to learn and co-construct knowledge effectively as a team.
Collaboration is an important lifelong and career skill, and collaborative learning is a growing pedagogical practice. Students often struggle, however, to negotiate, manage conflict, and construct knowledge with other group members. These struggles can lead to negative interactions, resulting in negative emotions. Students in collaborative settings must be able to effectively regulate emotions at both the individual and group level. More research is needed on the emotions that develop in collaborative learning environments and how they relate to socioemotional regulation (i.e., the collective regulation of emotions in group settings) in order to provide a better conceptualization of emotions in small group learning. In this article, I explore ideas from traditional, social, developmental, and educational psychology, combining key elements from seminal theoretical models to introduce a new model for emotion formation and regulation in collaborative learning environments.
The current study examines variation in other-regulation, conceptualized as efforts by one student to regulate their group's work. This study extends research which has conceptualized other-regulation as temporarily guiding others' conceptual understanding and skill development by broadening the spectrum of other-regulation to include directive forms and considering their differential impact on regulation quality. Qualitative analyses were conducted based on videotaped observations of three groups of 7th graders working on three collaborative activities during an inquiry-based science unit. Findings suggest that directive other-regulation related to employed moderate-low and low quality regulation within the group. Facilitative forms yielded higher quality regulation given co-equal regulation and task contributions, the focus of the other-regulator on integrating ideas using behavioral and group process regulation, as well as sustaining a shared focus on developing the task product through the use of high-quality content and disciplinary regulation. In contrast, directive other-regulation related to an imbalance in participation and regulatory contributions, as well as the other-regulators' focus on controlling the task and ensuring their own contribution remained central to the task product. When group members do not have opportunities to make regulatory contributions, regulation and task quality suffer since the group cannot benefit from the full potential of their shared activity, with implications for learning.
It is commonly observed that during classroom or group discussions some students have greater influence than may be justified by the normative quality of those students' contributions. We propose a 5-component theoretical framework in order to explain how undue influence unfolds. We build on literatures on persuasion, argumentation, discourse, and classroom discussions to develop a framework that models how each participant's level of influence in a discussion emerges out of the social negotiation of influence itself and the following 4 components that interact with it: (a) the negotiated merit of each participant's contributions; and each participant's (b) degree of intellectual authority, (c) access to the conversational floor, and (d) degree of spatial privilege. We then illustrate how the framework works by explaining how 1 student became unduly influential during a heated, student-led scientific debate. Finally, we close by outlining how our framework can be further developed to better understand and address differences in influence in classrooms and other learning contexts.
This study extends prior research on both individual self-regulation and socially shared regulation during group learning to examine the range and quality of the cognitive and behavioral social regulatory sub-processes employed by six small collaborative groups of upper-elementary students (n = 24). Qualitative analyses were conducted based on videotaped observations of groups across a series of three mathematics tasks. Variation in the quality of social regulation as a function of group processes (positive and negative socioemotional interactions, collaborative and non-collaborative interactions) was also considered. Findings suggested that the synergy among the social regulatory processes of planning, monitoring, and behavioral engagement was important for differentiating quality variation between groups. Positive socioemotional interactions and collaboration also appeared to facilitate higher quality social regulation. Implications for comprehensively supporting high quality social regulation, alongside positive socioemotional interactions and collaboration, in small group contexts are discussed.
Little attention has yet been focused on the social nature of metacognition and motivation in adult- or peer-mediated learning, although reciprocal or transactive interaction between individuals is emphasized as a road to learning, that is, in teaching and mediation of knowledge and skills. The present article presents a case analysis and focuses on (a) exploring if and how socially shared-regulation and (b) motivation and coping are manifested in high-ability, 4th grade students' peer-mediated learning in a technology-based game environment, specifically constructed to foster problem solving in mathematics. The case analysis supported the notion that peer-mediated learning can produce high-level learning and, also, transfer of learning. The key conditions for effective collaboration, task-orientation, and social and cognitive competencies, were met in the case of the peers. The analysis further suggested that the notion of shared-regulation could be helpful in understanding of multilevel interaction and regulatory activities in learning. The concept of share-dregulation best seemed to mirror egalitarian, complementary monitoring and regulation over the task, thus bringing the research closer to phenomena relevant to joint, peer-mediated learning. It seemed that regulation in true collaboration fluctuates among the three modes of regulation, self-, other-, and shared-regulation. We concluded, however, that collaborating peers do not regularly meet these ideal conditions, and that the more complete picture of joint problem solving and regulation is complex and variable. Understanding of these multilevel regulatory activities in learning, and their relationship to other, multilevel concepts like motivation, social competence, context, and learning, is a challenge for future research.
This paper focuses on the processes involved in collaboration using a microanalysis of one dyad’s work with a computer-based environment (the Envisioning Machine). The interaction between participants is analysed with respect to a ‘Joint Problem Space’, which comprises an emergent, socially-negotiated set of knowledge elements, such as goals, problem state descriptions and problem solving actions. Our analysis shows how this shared conceptual space is constructed through the external mediational framework of shared language, situation and activity. This approach has particular implications for understanding how the benefits of collaboration are realised and serves to clarify the possible roles of the computers in supporting collaborative learning.
In a longitudinal study, we found that higher group performance was associated with a particular pattern of conflict. Teams performing well were characterized by low but increasing levels of process conflict, low levels of relationship conflict, with a rise near project deadlines, and moderate levels of task conflict at the midpoint of group interaction. The members of teams with this ideal conflict profile had similar pre-established value systems, high levels of trust and respect, and open discussion norms around conflict during the middle stages of their interaction.
This article brings to the fore the sociocognitive aspect of metacognition and processes involved in coregulation. We argue that coregulation in a learning situation that involves the interaction of teachers and students or peers is based on awareness of the partners'cognition, metacognition, affect, and motivation, as well as interpersonal perception processes and/or interpersonal relational control processes. One aspect of metacognition, particularly relevant to coregulation of learning, is metacognitive experience, i.e., how the interacting partners feel and what they think about the task at hand. Awareness of one's own and the other's cognition and of metacognitive experiences is necessary for metacommunication control processes. Evidence from two independent studies suggests that there can be misperception of the interacting partners' metacognitive experiences because of "theory-driven" conceptions of the other person or lack of metacognitive coregulation because of the prevalence of relational control processes. We suggest that this may lead to scaffolding mismatch in instruction, failure in coregulation, and negative feelings and behaviors of the interacting partners in certain learning situations. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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.
This experiment examined the effects of epistemic vs. relational conflicts on the relationship with a partner. Students participated
to a fictitious computer-mediated interaction about a text with a bogus partner who introduced either an epistemic conflict
(a conflict that referred to the content of the text), or a relational conflict (a conflict that questioned participants’
competence). Results indicated that compared to the epistemic conflict, the relational conflict enhanced threat and reduced
the perceived contribution of the partner. Moreover, after a relational conflict, participants were more assertive in their
answers, justified them to a lower extent, and expressed less doubt than after an epistemic conflict. Results also indicated
that the intensity of disagreement predicted different modes of regulation depending on the conflict type. Finally, epistemic
conflict elicited better learning than relational conflict.
La présente expérience a examiné les effets de conflits épistémiques vs. relationnels avec un partenaire. Des étudiants étaient
amenés à participer à une pseudo-interaction médiatisée par ordinateur avec un partenaire factice, à propos d’un texte. Ce
partenaire factice introduisait soit un conflit épistémique (un conflit se référant au contenu du texte) soit un conflit relationnel
(un conflit qui mettait en cause la compétence des participants). Les résultats ont indiqué que comparativement au conflit
épistémique, le conflit relationnel a augmenté la menace et réduit la contribution perçue du partenaire. De plus, après un
conflit relationnel, les participants se sont montrés plus assertifs dans leurs réponses, les ont moins justifiées et ont
exprimé moins de doutes qu’après un conflit épistémique. Les résultats indiquent également que l’intensité des désaccords
prédit différents modes de régulation en fonction du type de conflit. Enfin, le conflit épistémique a entrainé un meilleur
apprentissage que le conflit relationnel.
We report on a large study of how U.S. middle-school students learned to reason scientifically in a science curriculum centered around models and argumentation. We discuss the design of our curriculum, the method of the study, and present selected results related to overall curriculum effects and to methods of promoting growth in students' reasoning.
This paper examines the mediating role of students' goals in group work at university. Research on cooperative and collaborative learning has provided empirical support for the cognitive, motivational and social benefits of group work but the antecedents of motivation and ongoing management of emerging motivational and socio-emotional issues have received less attention. A theory of self-regulation that incorporates students' personal goals and perceptions of context, combined with a sociocultural perspective on co-regulation of individuals and contexts, can help understand why and how some groups resolve their social challenges while others are less successful. An empirical study highlighted the mediating role of students' goals in their appraisals of group assignments, perceptions of various aspects of the contexts, and in turn regulation strategies to achieve their goals. Qualitative differences were found in the regulation strategies of students with positive and negative appraisals.
A team is more than a group of people in the same space, physical or virtual. In recent years, increasing attention has been devoted to the social bases of cognition, taking into consideration how social processes in groups and teams affect performance. This article investigates when and how teams in collaborative learning environments engage in building and maintaining mutually shared cognition, leading to increased perceived performance. In doing so, this research looks for discourse practices managing the co-construction of mutually shared cognition and reveals conditions in the interpersonal context that contribute to engagement in these knowledge-building practices. A comprehensive theoretical framework was developed and tested. The constructs in the model were measured with the Team Learning Beliefs & Behaviors Questionnaire and analyzed using regression and path analysis methodology. Results showed that both interpersonal and sociocognitive processes have to be taken into account to understand the formation of mutually shared cognition, resulting in higher perceived team performance.
Classroom discourse was examined as a predictor of changes in children's beliefs about their academic capabilities. Kindergarten, first-grade, and second-grade students (N = 106) participated in 2 waves of data collection, approximately 1 year apart. During the 1st year of the study, children's verbal interactions with their classmates were observed and recorded. Children rated their self-perceptions of academic competence during the 1st and 2nd years. Analyses revealed that changes over time in children's competence perceptions could be predicted from the types of statements that children made and had directed toward them by classmates. Examining sequences of child and classmate statements proved helpful in explaining the observed changes in children's perceptions of competence.
This study examined the behaviors and experiences of students who needed assistance while working in peer-directed small groups on mathematics problems and the processes that helped or hindered their learning. Students in 4 seventh-grade classes worked in heterogeneous small groups throughout a 3-week unit on operations with decimal numbers. Analyses of the transcripts of audiotapes of students' verbal interaction and their posttest performance confirmed previous research showing that students who learned how to solve the problems received high-level help during group work and, subsequently, correctly solved group-work problems without further assistance. Extending previous findings, this study also showed that the following help-seeking behaviors were important determinants of successful posttest performance: asking for specific explanations instead of calculations or answers or general admissions of confusion, persistence in seeking explanations and modification of help-seeking strategies, and application of the help received to the problem at hand. Important help-giving behaviors included providing explanations with verbally labeled numbers and continued explaining instead of resorting to descriptions of numerical procedures. This article discusses possible reasons for the patterns of help seeking and help giving found here and makes suggestions for further research to improve the quality of helping behavior in collaborative groups.
Background
Research in traditional classrooms and laboratories has indicated that autonomy support by teachers is infrequent and focused on the narrow provision of choice. One explanation for the limited autonomy support in classrooms is that typical school resources and tasks limit the availability of experiences that are interesting, relevant, with meaningful choice. Accordingly, it is critical to extend observation to contexts that enhance the likelihood of detecting significant autonomy support. In this way, it will be possible to (a) determine whether existing conceptualizations map onto behaviors in real classrooms and (b) enrich our understanding of the variety of ways in which teachers provide autonomy when the curriculum is designed not to constrain it but to expand it.
Objective
In the current study, we extend and develop conceptualizations of autonomy support based on our observations within an inquiry context that offers a broad range of forms of autonomy, thus gaining access to a more elaborated understanding of how real teachers offer this support. We elaborate on and richly describe how classroom teachers support autonomy in ways that extend the range of current conceptualizations, with implications for lending validity to the construct and providing concrete description for practitioners.
Research Design
Qualitative analyses were conducted based on videotaped observations of four 7th-grade science teachers, each enacting five inquiry-based science lessons designed to encourage scientific reasoning. We developed a coding protocol grounded in theoretical conceptualizations organized around five autonomy-support dimensions (i.e., procedural and organizational support, rationale and relevance, responsiveness, feedback, cognitive autonomy support). We were exploratory in our use of content analysis in ways that evolved our initial codes, given our aim to enrich and extend available characterizations of autonomy-supportive practice to incorporate new conceptualizations of higher quality practice.
Conclusions
Observed enactments provide support for modifying our conceptualizations of the upper end points of autonomy support to include more academically significant forms as well as for making new distinctions in forms of autonomy support. This high quality and multifaceted enactment was possible because practice was embedded within an inquiry-based curriculum context that expanded opportunities for student agency. Implications for supporting educational leaders in facilitating teacher practice using this thick description and set of exemplars are discussed.
Background/Context
Models of self-regulated learning (SRL) have increasingly acknowledged aspects of social context influence in its process; however, great diversity exists in the theoretical positioning of “social” in these models.
Purpose/Objective/Research Question/Focus of Study
The purpose of this review article is to introduce and contrast social aspects across three perspectives: self-regulated learning, coregulated learning, and socially shared regulation of learning.
Research Design
The kind of research design taken in this review paper is an analytic essay. The article contrasts self-regulated, coregulated, and socially shared regulation of learning in terms of theory, operational definition, and research approaches.
Data Collection and Analysis
Chapters and articles were collected through search engines (e.g., EBSCOhost, PsycINFO, PsycARTICLES, ERIC).
Findings/Results
Three different perspectives are summarized: self-regulation, coregulation, and socially shared regulation of learning.
Conclusions/Recommendations
In this article, we contrasted three different perspectives of social in each model, as well as research based on each model. In doing so, the article introduces a language for describing various bodies of work that strive to consider roles of individual and social context in the regulation of learning. We hope to provide a frame for considering multimethodological approaches to study SRL in future research.
Emotions are ubiquitous in academic settings, and they profoundly affect students’ academic engagement and performance. In this chapter, we summarize the extant research on academic emotions and their linkages with students’ engagement. First, we outline relevant concepts of academic emotion, including mood as well as achievement, epistemic, topic, and social emotions. Second, we discuss the impact of these emotions on students’ cognitive, motivational, behavioral, cognitive-behavioral, and social-behavioral engagement and on their academic performance. Next, we examine the origins of students’ academic emotions in terms of individual and contextual variables. Finally, we highlight the complexity of students’ emotions, focusing on reciprocal causation as well as regulation and treatment of these emotions. In conclusion, we discuss directions for future research, with a special emphasis on the need for educational intervention research targeting emotions.
A team is more than a group of people in the same space, physical or virtual. In recent years, increasing attention has been devoted to the social bases of cognition, taking into consideration how social processes in groups and teams affect performance. This article investigates when and how teams in collaborative learning environments engage in building and maintaining mutually shared cognition, leading to increased perceived performance. In doing so, this research looks for discourse practices managing the co-construction of mutually shared cognition and reveals conditions in the interpersonal context that contribute to engagement in these knowledge-building practices. A comprehensive theoretical framework was developed and tested. The constructs in the model were measured with the Team Learning Beliefs & Behaviors Questionnaire and analyzed using regression and path analysis methodology. Results showed that both interpersonal and sociocognitive processes have to be taken into account to understand the formation of mutually shared cognition, resulting in higher perceived team performance.
In this study I investigated how collaborative interactions influence problem-solving outcomes. Conversations of twelve 6th-grade triads were analyzed utilizing quantitative and qualitative methods. Neither prior achievement of group members nor the generation of correct ideas for solution could account for between-triad differences in problem-solving outcomes. Instead, both characteristics of proposals and partner responsiveness were important correlates of the uptake and documentation of correct ideas by the group. Less successful groups ignored or rejected correct proposals, whereas more successful groups discussed or accepted them. Conversations in less successful groups were relatively incoherent as measured by the extent that proposals for solutions in these groups were connected with preceding discussions. Performance differences observed in triads extended to subsequent problem-solving sessions during which all students solved the same kinds of problems independently. These findings suggest that the quality of interaction had implications for teaming. Case study descriptions illustrate the interweaving of social and cognitive factors involved in establishing a joint problem-solving space. A dual-space model of what collaboration requires of participants is described to clarify how the content of the problem and the relational context are interdependent aspects of the collaborative situation. How participants manage these interacting spaces is critical to the outcome of their work and helps account for variability in collaborative outcomes. Directions for future research that may help teachers, students, and designers of educational environments learn to see and foster productive interactional practices are proposed. The properties of groups of minds in interaction with each other, or the properties of the interaction between individual minds and artifacts in the world, are frequently at the heart of intelligent human performance (Hutchins, 1993, p. 62).
This study examined the behaviors and experiences of students who needed assis- tance while working in peer-directed small groups on mathematics problems and the processes that helped or hindered their learning. Students in 4 seventh-grade classes worked in heterogeneous small groups throughout a 3-week unit on operations with decimal numbers. Analyses of the transcripts of audiotapes of students' verbal inter- action and their posttest performance confirmed previous research showing that stu- dents who learned how to solve the problems received high-level help during group work and, subsequently, correctly solved group-work problems without further assis- tance. Extending previous findings, this study also showed that the following help-seeking behaviors were important determinants of successful posttest perfor- mance: asking for specific explanations instead of calculations or answers or general admissions of confusion, persistence in seeking explanations and modification of help-seeking strategies, and application of the help received to the problem at hand. Important help-giving behaviors included providing explanations with verbally la- beled numbers and continued explaining instead of resorting to descriptions of nu- merical procedures. This article discusses possible reasons for the patterns of help seeking and help giving found here and makes suggestions for further research to im- prove the quality of helping behavior in collaborative groups.
This article outlines the rationale for an integrative perspective of self- and social regulation in learning contexts. The role of regulatory mechanisms in self- and social regulation models is examined, leading to the view that in real time collaborative learning, individuals and social entities should be conceptualized as self-regulating and coregulated systems at the same time. Living systems theory provides support for the claim that although all forms of regulation have an adaptive function, the distinct, regulatory processes occurring at different systemic levels (e.g. individual, social) are concurrent and interdependent. Challenges for future research from an integrative perspective are discussed.
Two studies (Study 1: n = 137; Study 2: n = 192) were conducted to investigate how upper-elementary students’ affect during small group instruction related to their social-behavioral engagement during group work. A circumplex model of affect consisting of valence (positive, negative) and activation (high, low) was used to examine the relation of affect to social loafing and quality of group interactions. Across both studies, negative affect (feeling tired or tense) was associated with higher rates of social loafing. Neutral to deactivated positive affect, such as feeling happy or calm, was positively related to positive group interactions, while deactivated negative affect (tired) was negatively associated with positive group interactions. Follow-up cross-lagged analyses to examine reciprocal relations suggested that positive group interactions altered affect on subsequent group tasks, but affect was not related to changes in positive group interactions. These quantitative findings were supplemented with a qualitative analysis of six small groups from Study 2. The qualitative analyses highlighted the reciprocal and cyclical relations between affect and social-behavioral engagement in small groups.Research highlights► Negative affect (tense, tired) associated with social loafing. ► Positive affect (happy, calm) associated with positive group interactions. ► Negative affect (tired) negatively related to positive group interactions. ► Cross-lagged analyses suggested positive group interactions shaped affect. ► Cycles of affect and quality of group interaction observed during group work.
This study investigated how metacognition appears as a socially shared phenomenon within collaborative mathematical word-problem solving processes of dyads of high-achieving pupils. Four dyads solved problems of different difficulty levels. The pupils were 10 years old. The problem-solving activities were videotaped and transcribed in terms of verbal and nonverbal behaviors as well as of turns taken in communication (N = 14675). Episodes of socially shared metacognition were identified and their function and focus analysed. There were significantly more and longer episodes of socially shared metacognition in difficult as compared to moderately difficult and easy problems. Their function was to facilitate or inhibit activities and their focus was on the situation model of the problem or on mathematical operations. Metacognitive experiences were found to trigger socially shared metacognition.
La diversité culturelle des équipes de travail a d’abord été définie à partir des catégories dont relève chaque membre. La thèse centrale de cet article est que la diversité renvoie à une expérience subjective des catégories sociales auxquelle le membres ont le sentiment d’appartenir. Ces catégories (ou indentités sociales) deviennent plus ou moins prégnantes dan différents contextes et à différentes périodes. Nous proposons un modèle de la diversité culturelle dans les équipes qui montre à quelles conditions ces identitiés sociales deviennent prégnantes et de quelle façon elles peuvent influencer l’évaluation des résultats et des événements, ce qui peut entraîner des retombées sur les émotions et les conflits. Cette approche dynamique de la diversité nous procure une meilleure compréhension de la ‘boite noire’, des processus cognitifs et affectifs qui peuvent avoir un impact sur la performance de groupe.
Diversity in teams has been previously defined in terms of the nominal categories into which team members “fall”. The core argument of this paper is that diversity is a subjective experience of social categories to which members “feel” they belong. These categories, or social identities, may become more or less salient in different contexts and at different times. We propose a model of diversity in teams that explains under what conditions these social identities become salient and how these social identities may influence appraisals of issues and events. These appraisals, in turn, can influence conflict and emotion. This dynamic view of diversity provides us with a better understanding of the “black box”—the cognitive and affective processes that may help to explain behavior and subsequently team performance.
Genetics is the cornerstone of modern biology and understanding genetics is a critical aspect of scientific literacy. Research
has shown, however, that many high school graduates lack fundamental understandings in genetics necessary to make informed
decisions or to participate in public debates over emerging technologies in molecular genetics. Currently, much of genetics
instruction occurs at the high school level. However, recent policy reports suggest that we may need to begin introducing
aspects of core concepts in earlier grades and to successively develop students’ understandings of these concepts in subsequent
grades. Given the paucity of research about genetics learning at the middle school level, we know very little about what students
in earlier grades are capable of reasoning about in this domain. In this paper, we discuss a research study aimed at fostering
deeper understandings of molecular genetics at the middle school level. As part of the research we designed a two-week model-based
inquiry unit implemented in two 7th grade classrooms (N = 135). We describe our instructional design and report results based on analysis of pre/post assessments and written artifacts
of the unit. Our findings suggest that middle school students can develop: (a) a view of genes as productive instructions
for proteins, (b) an understanding of the role of proteins in mediating genetic effects, and (c) can use this knowledge to
reason about a novel genetic phenomena. However, there were significant differences in the learning gains in both classrooms
and we provide speculative explanations of what may have caused these differences.
KeywordsBiology–Curriculum development–Genetics–Implementation–Middle school–Qualitative research
Situative and sociocognitive analyses were combined to examine engagement in high-level collaborative learning and its relationship with individuals’ cognitions. Video footage of 53 science university students’ (nine groups) collaborative learning interactions as they worked through a case-based project was analysed in combination with students’ appraisals and reflections on the activity. Sizeable group differences in amount of high-level discussion of learning content were revealed. Individual high-level contributions were positively correlated with overall unit performance. Motivation at task onset predicted amount but not depth of content-related group discussion. Interviews with participants suggested that groups’ divergent patterns of engagement with content could be related to different perceptions of the notion of collaborative learning. Results are discussed in terms of implications for collaborative learning research and educational practice.
This paper introduces a descriptive system of analysis of peer group interaction. The method takes a dynamic and process-oriented approach to interaction which is seen as socially and situationally developed in students' moment-by-moment interactions. By concentrating on individual and group functioning, the method aims at highlighting the situated dynamics of peer group interaction and learning. The method consists of a three-dimensional analysis of peer group interaction by focusing on the functions of verbal interaction, and the nature of cognitive processing and social processing. These are investigated with the help of micro-analytical maps drawn out from the data based on video recordings, transcriptions, observations, interviews, and questionnaires. In the first part of the paper the theoretical and methodological background of the analysis will be discussed. That is followed by an introduction to the analysis method highlighted with empirical examples. The paper ends with a reflective analysis of the method.
This study sought to identify ninth grade students’ self-regulated learning (SRL) behaviors, enacted while engaged in a specially designed, long-term, group science inquiry task in an authentic classroom setting. To self-regulate planning and time management, students used yearly and daily planning reports. A high and medium achieving groups’ discourse and behavior were observed and videorecorded; qualitative analysis yielded several categories. Despite the unique learning context, results demonstrated many composites reported in the literature for general SRL models. Students evidenced SRL skill categories including the ability to set goals, plan activities, consider alternatives, monitor and reflect, perceive diverse cues from various sources, readjust plans to improve progress rates, and demonstrate accountability. High achieving students generally exhibited more SRL skills (were better planners and managers of time) than did average achieving students.
This article examines the nature and process of collaborative learning in student-led group activities at university. A situative framework combining the constructs of social regulation and content processing was developed to identify instances of productive high-level co-regulation. Data involves video footage of groups of science students working on a case-based project. Striking group differences in types of interactions were revealed. Regularities in the emergence of high-level co-regulation and features of interactions that contributed to the maintenance of productive collaboration were also identified. The importance of fostering students' development as co-learners is highlighted.
It can be assumed that academic learning is an active, generative and effortful process, that is — a mindful activity. Cooperative student teams are expected to increase participants' mindful engagement in learning and thus to improve its outcomes. Although this is sometimes the case, there are social-psychological effects that debilitate team performance. Two illustrations from recent studies are provided. It is argued that the study of team work cannot be limited to intrapersonal cognitions and to simple interactional processes. Teams are social systems in which cognitive, motivational and behavioral processes become increasingly interdependent and these processes need to be studied. Such interdependencies give rise to negative effects some of which are discussed in this article: the “free rider”, the “sucker”, the “status differential”, and the “ganging up” effects. The article concludes with a few speculations about possible mechanisms to overcome such effects when complex and exploratory tasks are given to student teams.
In this article, interactive processes among group partners and the relationship of these processes to problem-solving outcomes are investigated in 2 contrasting groups. The case study groups were selected for robust differences in the quality of their written solutions to a problem and parallel differences in the quality of the group members' interaction. In 1 group correct proposals were generated, confirmed, docu- mented, and reflected upon. In the other, they were generated, rejected without ratio- nale, and for the most part left undocumented. The analyses identified 3 major contrastive dimensions in group interaction—the mutuality of exchanges, the achievement of joint attentional engagement, and the alignment of group members' goals for the problem solving process. A focus on group-level characteristics offers a distinctive strategy for examining small group learning and paves the way to under- standing reasons for variability of outcomes in collaborative ventures. These dimen- sions may usefully inform the design and assessment of collaborative learning envi- ronments.
There are substantial similarities between deep learning and the processes by which knowledge advances in the disciplines. During the 1960s efforts to exploit these similarities gave rise to learning by discovery, guided discovery, inquiry learning, and Science: A Process Approach (American Association for the Advancement of Science, 1967). Since these initial reform efforts, scholars have learned a great deal about how knowledge advances. A mere listing of keywords suggests the significance and diversity of ideas that have come to prominence since the 1960s: Thomas Kuhn, Imre Lakatos, sociology of science, the "Science Wars," social constructivism, schema theory, mental models, situated cognition, explanatory coherence, the "rhetorical turn," communities of practice, memetics, connectionism, emergence, and self-organization. Educational approaches have changed in response to some of these developments; there is a greater emphasis on collaborative rather than individual inquiry, the tentative nature of empirical laws is more often noted, and argumentation has become an important part of some approaches. But the new "knowledge of knowledge" has much larger educational implications: Ours is a knowledge-creating civilization. A growing number of "knowledge societies" (Stehr, 1994), are joined in a deliberate effort to advance all the frontiers of knowledge. Sustained knowledge advancement is seen as essential for social progress of all kinds and for the solution of societal problems. From this standpoint the fundamental task of education is to enculturate youth into this knowledge-creating civilization and to help them find a place in it. In light of this challenge, both traditional education, with its emphasis on knowledge transmission, and the newer constructivist methods, appear as limited in scope if not entirely missing the point. Knowledge building, as elaborated in this chapter, represents an attempt to refashion education in a fundamental way, so that it becomes a coherent effort to initiate students into a knowledge creating culture. Accordingly, it involves students not only