An Investigation of Pre-Service Teachers Using Mobile and Wearable Devices for Emotion Recognition and Social Sharing of Emotion to Support Emotion Regulation in mCSCL Environments

Q: What is the main purpose of using emotion recognition and social sharing in mCSCL environments?

To support pre-service teachers’ emotion regulation by combining emotion recognition with social sharing of emotion in mobile computer-supported collaborative learning settings.

Q: How was emotion recognition measured in the study?

Using emotion tracker tools and physiological signals acquired from mobile and wearable devices, guided by participants’ perceptions.

Q: What did the regression results show about emotion regulation?

The two-predictor models combining emotion recognition and social sharing accounted for 31% of the variance in emotion regulation (p < 0.001, R2 = 0.31).

Q: Which physiological signals were most associated with emotion detection?

Heart rate (HR) and heart rate variability (HRV) were recognized as having the most possibilities for emotion detection among physiological signals.

Oct 15, 2026

Uploaded by nayy☆

Social and emotional skills are increasingly relevant in online learning driven by automation and artificial intelligence, making emotion understanding and management essential in instructional design. This study explores using emotion recognition and social sharing of emotion to support emotion regulation in pre-service teacher education within mobile computer-supported collaborative learning (mCSCL). Perceptions-based tools using emotion trackers and physiological signals were evaluated, and predictive links among emotion recognition, social sharing, and emotion regulation were examined in Thailand.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Research background and rationale

Study objectives and research questions

An Investigation of Pre-Service Teachers Using Mobile and Wearable Devices for Emotion Recognition and Social Sharing of Emotion to Support Emotion Regulation in mCSCL Environments Suthanit Wetcho Faculty of Education, Chulalongkorn University, Thailand ORCID: 0000-0003-4668-528X Jaitip Na-Songkhla Faculty of Education, Chulalongkorn University, Thailand ORCID: 0000-0001-5306-7688 Received: 17 Nov 2021 Accepted: 23 Jan 2022 Abstract In the era of a workforce driven by automation and artificial intelligence, social and emotional skills are becoming increasingly relevant to online learning environments. Since social-emotional learning may be defined as a vital component of the learning process in professional instructional design practices, online learners not only need to develop the ability to apply their knowledge, attitudes, and skills but also to understand and manage their emotions. In which setting and achieving positive goals through social interaction, sharing feelings, and developing empathy for others can help with the process. This paper outlines the possibility of using emotion recognition, and social sharing of emotion techniques to support the regulation of emotion in pre-service teacher education. This study aimed to investigate pre-service teachers’ emotion recognition tools acquired by emotion tracker and physiological signals based on their perceptions (without a concrete experience and knowledge) . Moreover, the predictive ability was examined along with the relationships between emotion recognition, social sharing of emotion, and emotion regulation. Finally, we investigated emotion adjustment techniques that can be adapted into mobile computer-supported collaborative learning (mCSCL) . In this study, 183 pre-service teachers from three different teacher-education institutions in Thailand, were voluntarily participated based on convenience sampling. The results of a self-report via online survey revealed that most pre-service teachers own at least one of the mobile technologies e.g., smartphones, tablets, or laptops. However, there is an increasing number of additional gadgets and wearable devices like EarPods and smartwatches. At the current time, it is nearly impossible to use of the IoT and other wearable devices. According to their subjective impressions in which corresponded to emotion recognition in the scientific literature, Heart rate (HR) and Heart rate variability (HRV) have recognized the most possibilities for emotion detection among physiological signals. Regarding regression analysis, the two-predictor models of emotion recognition and the social sharing of emotion were also able to account for 31% of the variance in emotion regulation, p< .001, R2= .31, and 95% CI [ .70, .77] . In addition, the mCSCL applications and the importance of these variables in different collaboration levels are also discussed. Keywords: social emotional learning, emotion recognition, social sharing of emotion, emotion regulation, mCSCL INTRODUCTION In the current era, social-emotional skills are becoming increasingly relevant to online learning environments. In addition, online learners need to develop the ability to understand and manage emotions, as well as setting and achieving positive goals through social interaction, feelings, and showing empathy towards others. The original socioemotional selectivity theory (Carstensen, 1992) helped us to shape the idea that social motives fall into 1 of 2 categories, which are the acquisition of knowledge and its relation to emotion regulation. This is applicable in accordance with previous research (Carstensen et al., 2003; Dudley & Multhaup, 2005) . As a result, the key factor for success consists not only of the acquisition of knowledge, but also the regulation of emotion in a social world. It was found in previous studies that students of teacher-education faced socioemotional challenges, in which both individual and social elements of collaborati

FAQs

What is the main purpose of using emotion recognition and social sharing in mCSCL environments?

To support pre-service teachers’ emotion regulation by combining emotion recognition with social sharing of emotion in mobile computer-supported collaborative learning settings.

How was emotion recognition measured in the study?

Using emotion tracker tools and physiological signals acquired from mobile and wearable devices, guided by participants’ perceptions.

What did the regression results show about emotion regulation?

The two-predictor models combining emotion recognition and social sharing accounted for 31% of the variance in emotion regulation (p < 0.001, R2 = 0.31).

Which physiological signals were most associated with emotion detection?

Heart rate (HR) and heart rate variability (HRV) were recognized as having the most possibilities for emotion detection among physiological signals.

Oct 15, 2026

Uploaded by nayy☆