Fostering students’ emotion regulation during learning : design and effects of a computer-based video training

Emotions have an essential impact on students’ learning outcome. Empirical findings show negative correlations between negative emotions and learning outcome. Negative emotions during learning are quite common and become more frequent over the course of an academic career. Thus, regulating these emotions is important. Existing studies indicate that university students lack the ability to successfully regulate their emotions during learning. However, interventions to foster university students’ inherent emotion regulation during learning are missing. In an attempt to identify interventions, this study investigates the effect of a video-based emotion regulation training for university students on emotion regulation strategies, emotions, and learning outcome. One hundred and sixteen university students either received training in emotion regulation (n = 60) or in workplace design (n = 56) before learning in a computer-based learning environment about probability theory. The emotion regulation training lead to improved emotion regulation (more cognitive reappraisal, less suppression) and less frustration and anxiety, but did not affect learning outcome. The results confirm that university students experience significant emotion regulation difficulties and suggest that they need intensive training in emotional regulation.peer-reviewe

Progressive modulation of resting-state brain activity during neurofeedback of positive-social emotion regulation networks

Neurofeedback allows for the self-regulation of brain circuits implicated in specific maladaptive behaviors, leading to persistent changes in brain activity and connectivity. Positive-social emotion regulation neurofeedback enhances emotion regulation capabilities, which is critical for reducing the severity of various psychiatric disorders. Training dorsomedial prefrontal cortex (dmPFC) to exert a top-down influence on bilateral amygdala during positive-social emotion regulation progressively (linearly) modulates connectivity within the trained network and induces positive mood. However, the processes during rest that interleave the neurofeedback training remain poorly understood. We hypothesized that short resting periods at the end of training sessions of positive-social emotion regulation neurofeedback would show alterations within emotion regulation and neurofeedback learning networks. We used complementary model-based and data-driven approaches to assess how resting-state connectivity relates to neurofeedback changes at the end of training sessions. In the experimental group, we found lower progressive dmPFC self-inhibition and an increase of connectivity in networks engaged in emotion regulation, neurofeedback learning, visuospatial processing, and memory. Our findings highlight a large-scale synergy between neurofeedback and resting-state brain activity and connectivity changes within the target network and beyond. This work contributes to our understanding of concomitant learning mechanisms post training and facilitates development of efficient neurofeedback training

Progressive modulation of resting‑state brain activity during neurofeedback of positive‑social emotion regulation networks

Neurofeedback allows for the self-regulation of brain circuits implicated in specific maladaptive behaviors, leading to persistent changes in brain activity and connectivity. Positive-social emotion regulation neurofeedback enhances emotion regulation capabilities, which is critical for reducing the severity of various psychiatric disorders. Training dorsomedial prefrontal cortex (dmPFC) to exert a top-down influence on bilateral amygdala during positive-social emotion regulation progressively (linearly) modulates connectivity within the trained network and induces positive mood. However, the processes during rest that interleave the neurofeedback training remain poorly understood. We hypothesized that short resting periods at the end of training sessions of positive-social emotion regulation neurofeedback would show alterations within emotion regulation and neurofeedback learning networks. We used complementary model-based and data-driven approaches to assess how resting-state connectivity relates to neurofeedback changes at the end of training sessions. In the experimental group, we found lower progressive dmPFC self-inhibition and an increase of connectivity in networks engaged in emotion regulation, neurofeedback learning, visuospatial processing, and memory. Our findings highlight a large-scale synergy between neurofeedback and resting-state brain activity and connectivity changes within the target network and beyond. This work contributes to our understanding of concomitant learning mechanisms post training and facilitates development of efficient neurofeedback training.publishedVersio

Language learners’ emotion regulation and enjoyment in an online collaborative writing program

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.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

Revealing the Hidden Structure of Affective States During Emotion Regulation in Synchronous Online Collaborative Learning

This study aims to explore the use of advanced technologies such as artificial intelligence (AI) to reveal learners' emotion regulation. In particular, this study attempts to discover the hidden structure of affective states associated with facial expression during challenges, interactions, and strategies for emotion regulation in the context of synchronous online collaborative learning. The participants consist of 18 higher education students (N=18) who collaboratively worked in groups. The Hidden Markov Model (HMM) results indicated interesting transition patterns of latent state of emotion and provided insights into how learners engage in the emotion regulation process. This study demonstrates a new opportunity for theoretical and methodology advancement in the exploration of AI in researching socially shared regulation in collaborative learning

Emotion regulation strategies and goals of distance learning students in an assessed online collaborative project

Being able to effectively regulate emotions is viewed as a crucial process in online learning. With an increasing use of social and collaborative activities in online learning environments, there are many unique challenges that may impact emotion regulation in these settings, such as a lack of social and emotional cues. Currently, there is limited research exploring emotion regulation strategies (i.e. how learners regulate their emotions) and emotion regulation goals (i.e. why learners regulate their emotions) of students in these online collaborative learning environments. In the present study, the emotion regulation strategies and goals used by 17 distance learning students undertaking an assessed, online, collaborative group project were explored. An online diary was used to gather self-report data at six-time points during the group activity. In each diary entry, participants were asked to select both a pleasant and unpleasant emotion they had experienced in relation to the group project, and indicate whether they had attempted to regulate each emotion. If they had, they were asked to describe how and why they had tried to regulate their emotions. Results revealed that students used a variety of strategies to regulate pleasant and unpleasant emotions. It was also found that participants described having emotion regulation goals primarily aimed at changing the momentary emotional experience (hedonic goals) as well as goals focused on other outcomes resulting from changing the experience or expression of specific emotions (instrumental goals). A brief discussion of the implications for educators and learning designers conclude this presentation

Using emotion regulation to cope with challenges:a study of Chinese students in the United Kingdom

There is increasing research on the challenges that Chinese students experience during their time studying abroad, but limited studies have explored how they self-regulate their emotions to address these challenges. This paper identifies key stressors experienced by Chinese postgraduate students during their study in academic institutions in the United Kingdom as well as the emotion regulation strategies that they employed. Understanding the emotional experiences will provide important insights into Chinese students’ learning behaviour and their choice of different emotion regulation strategies when interacting with peers and academic tutors. Emotion regulation strategies believed to improve individual student experiences and their interaction with tutors and peers are discussed

A game based approach to improve traders' decision-making

Purpose: The development of a game based approach to improving the decision-making capabilities of financial traders through attention to improving the regulation of emotions during trading. Design/methodology/approach: The project used a design-based research approach to integrate the contributions of a highly inter-disciplinary team. The approach was underpinned by considerable stakeholder engagement to understand the ‘ecology of practices’ in which this learning approach should be embedded. Findings: Taken together, our 35 laboratory, field and evaluation studies provide much support for the validity of our game based learning approach, the learning elements which make it up, and the value of designing game-based learning to fit within an ecology of existing practices. Originality/value: The novelty of the work described in the paper comes from the focus in this research project of combining knowledge and skills from multiple disciplines informed by a deep understanding of the context of application to achieve the successful development of a Learning Pathway, which addresses the transfer of learning to the practice environment Key words: Design-based research, emotion-regulation, disposition–effect, financial traders, serious games, sensor-based game

EFL Students’ Self-Regulation in Online Learning during the Covid-19 Pandemic

This study investigates self-regulation in online learning during the Covid-19 pandemic, specifically how students regulate their learning activities amidst challenging circumstances. This research focuses on the challenges faced by students in online learning and the strategies students employed to regulate their learning process. The participants of this study were 38 students from two private universities in Indonesia. Data were collected through FGD and analyzed by using open coding. The results of this study revealed that students dealt with difficulties during online learning, specifically related to an internet connection, an unsupportive learning environment, and negative emotions. However, they utilized self-regulated learning strategies to support them in online learning activities such as self-consequating, environmental structuring, emotion regulation, and time management.     &nbsp

Extending the concept of emotion regulation with model-based fMRI

Effective emotion regulation is essential for our social and emotional well-being. Yet, the concept of emotion regulation, as it is conventionally regarded in the field, does not take important aspects of emotions and emotion regulation into account. The overarching aim of the current thesis was to include such missing aspects and thereby expand the concept of emotion regulation. The expansion occurred in two directions: firstly, the definition of emotion within the field of emotion regulation was widened to include the motivational aspect of emotions in terms of value-based prediction errors and their neural implementation; and secondly, an underestimated type of emotion regulation – the social emotion regulation – and its neural underpinnings were investigated. Projects 1 and 2 of the current thesis expand the emotion part of emotion regulation. Project 1 investigated whether emotion regulation affects not only emotional response-related brain activity but also influences aversive prediction error-related activity, i.e., the motivation-related brain signal. We found that self- initiated reappraisal, a type of cognitive emotion regulation, indeed affected prediction error-related activity, such that this activity was enhanced in the ventral tegmental area, ventral striatum, insula and hippocampus, possibly via a prefrontal-tegmental pathway. Project 2 further examined the way emotion regulation affects emotions and prediction errors, by testing whether self- initiated reappraisal directly targets the brain network for motivated behaviour previously outlined by animal studies. We found that superior (in contrast to inferior) regulators affected the balance of competing influences of ventral striatal afferents on striatal aversive prediction error signals; they reduced the impact of subcortical striatal afferents (i.e., hippocampus, amygdala and ventral tegmental area), while keeping the influence of the prefrontal cortex on ventral striatal prediction errors constant. Inferior regulators, on the other hand, failed to supress subcortical inputs into the ventral striatum and instead counterproductively reduced the prefrontal influence on ventral striatal prediction error signals. Projects 3 and 4 of the thesis extend the regulation part of emotion regulation. Project 3 explored the neural correlates of social cognitive emotion regulation, specifically reappraisal, and directly compared them with those of self-initiated reappraisal. We found that regions of the anterior, the medial parietal, and the lateral temporo-parietal default mode network were specifically involved in social emotion regulation, and that social regulation success and the default mode network involvement during regulation were related to participants’ attachment security scores. Project 4 investigated social emotion modulation and its impact on two distinct types of emotional brain activity – emotional response- and aversive prediction error-related activity. We found – for the simple contrast of being with somebody versus being alone – a three-fold dissociation between signal types and insula subregions, including left and right anterior and posterior insula parts. Social emotion modulation reduced aversive stimulus-related activity in the posterior insula, while simultaneously increasing aversive prediction error-related activity in the anterior insula. Furthermore, the social effect on prediction error-related activity was positively associated with aversive learning in the right, but negatively in the left anterior insula. Altogether, by expanding the concept of emotion regulation, projects of the current thesis provide new insights into both the effects and the neural underpinnings of three distinct emotion regulation types. Considering that problems in both intrapersonal emotion regulation and social interaction are linked to affective disorders, our findings might contribute to a better understanding of these disorders and the disorder-specific emotional and social impairments