Supporting groups’ emotion and motivation regulation during collaborative learning

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.

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

Emotional Reactivity in Posttraumatic Stress Disorder:Behavioural and Neurobiological Correlates of Underlying Mechanisms and the Role of Emotional Memory Modification

Commonalities and differences in strategies for regulating motivation and emotion in academic settings: A within-person approach

A large number of studies highlight the importance of regulation in collaborative learning (CL). Nevertheless, only some studies describe how to get or support pupils to learn to regulate group work in school. In this context, we aim to understand if a specifically designed tangible environment can promote a regulated collaboration process during a face-to-face activity in school. Therefore, we conducted a 2-year design-based research (DBR) study. This article describes the DBR cycles in detail, and the steps we have executed to develop, test, implement, and evaluate a set of tangible artifacts called Collective attention led by a Mediated environment (CalMe). First, we identified three specific dimensions that interfere with CL in the classroom: team building and collective decision-making, task regulation awareness, and over-solicitation limitation. Second, we proposed design choices leading to the first iteration of a CalMe device that meets these needs. We then assessed usability and acceptability before the pedagogical validation steps. Third, through a pilot study, we evaluated the pedagogical potential of the second iteration of the CalMe device in a real context of use in an elementary school class. We collected and analyzed data from surveys, focus groups, log data, and video recordings through all the steps. Moreover, to allow for duplication of the study, we propose and detail our methodological approach. This study shows an empirical example of a DBR process that allows responding as closely as possible to the needs of both pupils and teachers. This work also provides input to teachers regarding a better understanding of collaborative problem-solving activities. Although there is still room for improvement on specific dimensions related to task regulation, such as better management of ambient noise or work tempo, the results indicate that the CalMe device allows for a regulated collaboration process in schools. It shows an human–computer interaction design process that can be an example of how to influence classroom activities through technology to promote positive CL experiences.

Socially shared regulation in learning (SSRL) contributes to successful collaborative learning (CL). Empirical research into SSRL has received considerable attention recently, with increasingly available multimodal data, advanced learning analytics (LA), and artificial intelligence (AI) providing promising research avenues. Yet, integrating these with traditional datasets remains a challenge in SSRL research due to the misalignment between theoretical constructs, methodological assumptions, and data structure. To address this challenge and expand our understanding of the nature of SSRL, the present research adopted a human–AI collaboration approach in a three-layer analysis to examine group interactions in response to cognitive and emotional regulation triggering events. Two-level theoretical lenses — macro-level (regulatory aspects) and micro-level (deliberative interactions) — were used to analyze 2,125 utterances from video-recorded tasks of ten groups of three Finnish secondary students (N=30). Results showed two types of deliberation patterns for SSRL, namely 1) the Plan and Implementation Approach (PIA) associated with adaptive patterns, and 2) the Trials and Failure Approach (TFA) associated with maladaptive patterns. Our findings revealed that groups often fail to recognize, or are ill-equipped to respond to, emerging regulatory needs. These findings advance SSRL theories and research methodologies by utilizing AI-enhanced LA to offer new insights into group dynamics and regulatory strategies.

Negative interactions in collaborative learning can impact students' emotions, making emotion regulation a critical factor. This study investigates the causes of negative emotions in online collaborative learning, explores the underlying reasons for these emotions, and identifies other issues contributing to learners' negative emotional experiences. Additionally, it examines the types of emotion regulation strategies learners employ to manage their emotions and mitigate these challenges. This study investigates the factors leading to social negative emotions among learners in the online collaborative learning environment and examines the types of emotion regulation learners employ during collaboration. Building on existing research, the study reclassifies socio-emotional problems within the context of online collaboration. It introduces a new scale for measuring negative social emotions in online collaborative learning and a corresponding scale for assessing the instrumental aspects of emotion regulation. Data for the scales and WeChat were collected from 44 learners enrolled in modern educational technology courses at Chinese provincial normal universities. The findings indicate that problems related to goal motivation are more likely to trigger learners' social negative emotions. To better cope with these social negative emotions, learners typically employ socially shared regulation. Further analysis through Pearson correlation and multiple regression analysis reveals that ER, CoER, and SSER all show significant negative correlations with social negative emotions. Among them, SSER exhibits a stronger negative correlation with social negative emotions and forms the best regression model for negative affect. These findings enhance our understanding of the sources of negative emotions in online collaborative learning and inform more effective social regulation strategies, which are crucial for improving its overall quality.

There is considerable evidence that circadian rhythms in mammals can be modulated by emotional state, but how emotional state modulates specific circadian outputs is poorly understood. We analyzed the expression of the circadian clock protein Period2 (PER2) in three regions of the limbic forebrain known to play key roles in emotional regulation, the central nucleus of the amygdala (CEA), the basolateral amygdala (BLA), and the dentate gyrus (DG). We report here that cells in all three regions exhibit daily rhythms in expression of PER2 that are under the control of the master clock, the suprachiasmatic nucleus (SCN). The rhythm in the CEA and the rhythms in the BLA and DG are diametrically opposite in phase and are differentially affected by adrenalectomy. Adrenalectomy completely abolished the PER2 rhythm in the CEA but had no effect on the PER2 rhythms in the BLA and DG. We previously reported a rhythm in PER2 expression in the oval nucleus of the bed nucleus of the stria terminalis that is identical in phase and sensitivity to adrenalectomy to that found in the CEA. Together, these findings show that key structures of the limbic forebrain exhibit daily oscillations in clock gene expression that are controlled not only by input from the SCN but, importantly, by hormonal and neurochemical changes that normally accompany motivational and emotional states. Thus, cells within these areas are strategically positioned to integrate the inputs from the SCN and emotional states to modulate circadian rhythms downstream from the SCN clock.

The objective of this review article was to critically analyze studies discussing tutor subject matter expertise and its effect on students' collaborative learning and performance in undergraduate medical education. The debate regarding tutors' subject matter expertise and its effect on students' learning puzzled educators. Problem-based learning model-advocates were concerned that tutors, having subject matter expertise, would revert back to familiar lecturing habits and interfere with students' collaborative learning. Others showed beneficial results reflected on learning and academic achievement. A Medline and PubMed databases literature review were conducted. Out of 88 relevant articles, 15 of them that compare expert and non-expert tutors were identified and reviewed critically. Literature was not decisive on whether tutors expertise provided beneficial effect(s) on students' learning. Few factors played an important role in these conflicting results. Definition of expertise was not unanimous among articles and measures of effectiveness were different. Medical schools' increasing demand for more Problem-based learning tutors drove the direction of research into biased route and underestimated related to non-expert tutors. Viewing the literature critically, tutor subject matter expertise displayed advantages that were reflected in students' learning sessions and afterwards. Disadvantages of non-expert tutoring should be highly scrutinized before replacing expert tutoring. Educators should focus mainly on developing clinicians' skills to become better teachers/facilitators and nothing else.

Collaborative digital learning is becoming increasingly popular in higher education. However, the use of collaborative digital learning does risk placing too big a responsibility on the learner and reducing face-to-face interaction with the educator. The aim of this quasi-experimental study was to evaluate the effects of a digital educational intervention on collaborative learning in nursing education. The intervention group ( n = 87) studied using a collaborative digital learning environment and the control group ( n = 38) studied in the traditional classroom setting. There were no significant differences between the groups in terms of student satisfaction. However, the students' satisfaction of studying decreased in the intervention group after completion of the course. In the intervention group students had higher satisfaction in the area of promoting collaborative group work and received statistically significant higher grades in the final course evaluation. This study emphasizes that collaborative digital learning can be an effective approach in nursing education in terms of learning outcomes. It also shows that more study is needed on the role of the teacher in collaborative digital nursing education.

Promoting patient safety among nursing students: A mixed-method study on collaborative cross-cultural learning

Current efforts on Computer Support Collaborative Learning (CSCL) include the design of computational models of collaborative learning interaction such as to improve support guidance to human participation. However, during collaborative learning activities, the interaction process among learners is too complex and it is difficult for teachers or designers to analyze and measure learning effects. Computational models of collaborative learning interaction have been known as a method that provide functional computer-based representations to help educators understand, explain, and identify patterns of group behavior and hence support group learning processes. The aim of this paper is to give an overview on three computational models of learner’s interaction and support possibilities afforded by the various types of computational models of collaborative learning processes. This paper demonstrates how these models can be used to study the nature of interaction patterns within collaborative learning in e-learning

Motivational self-regulation is an important skill supporting task engagement, achievement, and wellbeing during learning and other potentially strenuous tasks. It refers to active thoughts and behaviors used to initiate or maintain one's task engagement by manipulating underlying motivational processes (Wolters, 2003). This involves knowledge about one's own motivation and task characteristics, motivational control strategies, as well as monitoring of motivational processes. Most research on motivation regulation has focused on adolescents and adults; but little is known about motivation regulation in pre- and primary school children and its development. The present integrative review therefore analyzes theoretical accounts of motivation regulation to identify components of the process which may develop during childhood. To draw tentative hypotheses on how the complex process develops during childhood, it reviews research on the development in related areas of self-regulation, for example, self-regulated learning and emotion regulation. Drawing on this, it poses questions for future research on assessment methods, the development of metamotivational knowledge, monitoring, and control, as well as factors influencing their development.

P-160: Motivational reserve: A new concept complementing cognitive reserve

Summary Young children who enter school without sufficient social and emotional learning (SEL) skills may have a hard time learning. Yet early childhood educators say they don't get enough training to effectively help children develop such skills. In this article, Megan McClelland, Shauna Tominey, Sara Schmitt, and Robert Duncan examine the theory and science behind early childhood SEL interventions. Reviewing evaluation results, they find that several interventions are promising, though we need to know more about how and why their results vary for different groups of children. Three strategies appear to make interventions more successful, the authors write. First, many effective SEL interventions include training or professional development for early childhood teachers; some also emphasize building teachers' own SEL skills. Second, effective interventions embed direct instruction and practice of targeted skills into daily activities, giving children repeated opportunities to practice SEL skills in different contexts; it's best if these activities grow more complex over time. Third, effective interventions engage children's families, so that kids have a chance to work on their SEL skills both at school and at home. Family components may include teaching adults how to help children build SEL skills or teaching adults themselves how to practice and model such skills. Are early childhood SEL interventions cost-effective? The short answer is that it's too soon to be sure. We won't know how the costs and benefits stack up without further research that follows participants into later childhood and adulthood. In this context, we particularly need to understand how the long-term benefits of shorter, less intensive, and less costly programs compare to the benefits of more intensive and costlier ones. www. futureofchildren. org To be ready to enter school, young children need social and emotional learning (SEL) skills such as getting along with others, paying attention, following directions, and managing emotions. Yet teachers report that many children enter school without these skills, which can make it challenging for them to learn. (1) And early childhood educators often feel that they don't receive enough training to effectively help children develop SEL skills. (2) In response, policymakers and practitioners have focused on SEL, and interventions that promote SEL skills for young children have proliferated. SEL interventions take many approaches, and their very diversity makes it challenging to determine which components and approaches are most effective. To ensure that all children have the skills to thrive, we need to pinpoint what works under what conditions and with what populations. In this article, we examine the science behind SEL interventions. We start by clarifying key terms related to SEL skills and reviewing the approaches used in current early childhood SEL interventions. We discuss the theories that guide these interventions, as well as results from intervention studies, including a look at how results vary for different groups of children. Next we examine intervention characteristics that relate to SEL growth and review the potential financial and societal benefit of SEL interventions. We conclude by discussing the limitations of current SEL interventions and making recommendations for research and policy. Social and Emotional Learning: Key Terms Social and emotional learning (SEL) refers to a broad range of social, emotional, and behavioral skills for children. We highlight three main components of SEL skills: emotional processes, social/interpersonal skills, and cognitive regulation. (3) The first component, emotional processes, encompasses the skills children need to manage their emotions effectively and recognize the emotions of others. Emotional processes include skills such as emotion knowledge (the ability to recognize and label emotions accurately), emotion regulation (managing emotions and controlling how and when we express them), perspective taking, and empathy. …