Sympathetic arousal commonalities and arousal contagion during collaborative learning: How attuned are triad members?

Detection of dynamic changes of electrodermal activity to predict the classroom performance of college students

It is emphasized in the Self-regulated learning (SRL) framework that self-monitoring of learning state is vital for students to keep effective in studying. However, it's still challenging to get an accurate and timely understanding of their learning states during classes. In this study, we propose to use electrodermal activity (EDA) signals which are deemed to be associated with physiological arousal state to predict the college student's classroom performance. Twenty college students were recruited to attend eight lectures in the classroom, during which their EDA signals were recorded simultaneously. For each lecture, the students should complete pre- and after-class tests, and a self-reported scale (SRS) on their learning experience. EDA indices were extracted from both time and frequency domains, and they were furtherly mapped to the student's learning efficiency. As a result, the indices relevant to the dynamic changes of EDA had significant positive correlations with the learning efficiency. Furthermore, compared with only using SRS, a combination with EDA indices had significantly higher accuracy in predicting the learning efficiency. In conclusion, our findings demonstrate that the EDA dynamics are sensitive to the changes in learning efficiency, suggesting a promising approach to predicting the classroom performance of college students.

Exploring what synchronized physiological arousal can reveal about the social regulatory process in a collaborative argumentation activity

Combining physiological measures with observational data (e.g., video or self-reports) to further capture and understand the temporal and cyclical process of social regulation has become a trend in the field. Synchronized physiological arousal is a particularly meaningful situation in collaboration. However, little attention has been given to synchronized physiological arousal episodes and their relationship with the social regulatory process. In addition, only a few research utilized heart rate (HR) as a physiological measure in the current collaboration literature. More research is necessary to reveal the potential of HR to expand the diversity of physiological indicators in the field. Therefore, the current study aimed to explore what synchronized physiological arousal can further reveal about the social regulatory process. To achieve this goal, this study designed a collaborative argumentation (CA) activity for undergraduates (mean age 20.3). It developed an arousal-regulation analysis platform, which could automatically detect synchronized physiological arousal in HR and align them with coding challenges and social regulation based on the timeline. In total, 14 four-member groups were recruited. After analyzing both videos and HR data, several findings were obtained. First, only one-third of episodes were synchronized physiological arousal episodes, and the situations where four members were all in arousal states were rare during CA. Second, synchronized physiological arousal was more sensitive to socio-emotional aspects of collaboration as the shared physiological arousal more frequently co-occurred with socio-emotional challenges and socio-emotional regulation, while it happened the least under motivational challenges. Third, synchronized physiological arousal has also been found to be associated with the challenges being regulated. Finally, pedagogical implications were suggested.

Investigating the effect of synchronized movement on toddlers' word learning

The effect of interpersonal behavioral synchrony on children's behavior is an emerging field rich with research potential. While studies demonstrate its effect on affiliative and prosocial outcomes, the role of synchronized movement on children's specific learning outcomes has not yet been investigated experimentally. One possibility is that synchrony, as a coordinated social activity, encourages perceived social bonds, leading to heightened attention, and better information retention. Equally likely is that physiological, rather than social learning, mechanisms mediate the effect, given the previously demonstrated role of autonomic arousal in attentional fluctuations, cognitive engagement, problem solving, exploration, and curiosity. The present study investigated the behavioral and physiological effects of synchrony conceptualized as induced, interpersonal, behavioral, movement-based interaction, on word learning in 2.5-year-old children. In a laboratory experiment, toddlers engaged in either a synchronous or an asynchronous movement-based interaction with an adult experimenter while listening to an upbeat children's song. After the (a)synchronous movement episode, the same experimenter engaged children in a word learning task. During the (a)synchrony and learning phases, children's physiological arousal was continuously recorded, resulting in heart rate and skin conductance response measures. Following a caregiver-child free play break, children were tested on their novel word retention. The results indicated that children learned novel labels at equal rates during the learning phase in both conditions, and their retention at test did not differ between conditions: although above chance retention of novel labels was found only following the synchronous, but not the asynchronous episode, the cross-episode comparisons did not reach statistical significance. Physiological arousal indices following the (a)synchrony episode did not differ between conditions and did not predict better word learning, although skin conductance response was higher during the learning than the movement episode. This study contributes to our understanding of the underlying cognitive and physiological mechanisms of interpersonal behavioral synchrony in the knowledge acquisition domain and paves the way to future investigations.

Affective states and regulation of learning during socio-emotional interactions in secondary school collaborative groups

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.

An Effective Learning Evaluation Method Based on Text Data with Real-time Attribution - A Case Study for Mathematical Class with Students of Junior Middle School in China

In today's intelligent age, the vigorous development of education-based information analysis technology has had a profound impact on the education and teaching process. The use of computational linguistics technology to extract teaching data for learning evaluation is an important hot domain in this research field. Therefore, the study of student learning assessment method based on text data has become a key issue. The text data extracted from the education process has attributes related to time and operational attributes, which are important indicators to measure the effect of student learning effect. However, these attributes are not focused by the traditional educational effect evaluation method, which make the learning effect of students is difficult to measure comprehensively and effectively. In response to this problem, this article first uses perception technology to extract learning text data based on time and operational attributes. Secondly, according to the real-time attributes of text data, such as time and operation attributes, a learning evaluation method based on real-time text data is proposed. Finally, this article compares the traditional evaluation method with the proposed method. The results show that using real-time attribute text data is more effective in students’ learning measure.

Mobile Sensing with Smart Wearables of the Physical Context of Distance Learning Students to Consider Its Effects on Learning

Research shows that various contextual factors can have an impact on learning. Some of these factors can originate from the physical learning environment (PLE) in this regard. When learning from home, learners have to organize their PLE by themselves. This paper is concerned with identifying, measuring, and collecting factors from the PLE that may affect learning using mobile sensing. More specifically, this paper first investigates which factors from the PLE can affect distance learning. The results identify nine types of factors from the PLE associated with cognitive, physiological, and affective effects on learning. Subsequently, this paper examines which instruments can be used to measure the investigated factors. The results highlight several methods involving smart wearables (SWs) to measure these factors from PLEs successfully. Third, this paper explores how software infrastructure can be designed to measure, collect, and process the identified multimodal data from and about the PLE by utilizing mobile sensing. The design and implementation of the Edutex software infrastructure described in this paper will enable learning analytics stakeholders to use data from and about the learners’ physical contexts. Edutex achieves this by utilizing sensor data from smartphones and smartwatches, in addition to response data from experience samples and questionnaires from learners’ smartwatches. Finally, this paper evaluates to what extent the developed infrastructure can provide relevant information about the learning context in a field study with 10 participants. The evaluation demonstrates how the software infrastructure can contextualize multimodal sensor data, such as lighting, ambient noise, and location, with user responses in a reliable, efficient, and protected manner.

Studying Collaboration Dynamics in Physical Learning Spaces: Considering the Temporal Perspective through Epistemic Network Analysis

The role of the learning space is especially relevant in the application of active pedagogies, for example those involving collaborative activities. However, there is limited evidence informing learning design on the potential effects of collaborative learning spaces. In particular, there is a lack of studies generating evidence derived from temporal analyses of the influence of learning spaces on the collaborative learning process. The temporal analysis perspective has been shown to be essential in the analysis of collaboration processes, as it reveals the relationships between students’ actions. The aim of this study is to explore the potential of a temporal perspective to broaden understanding of the effects of table shape on collaboration when different group sizes and genders are considered. On-task actions such as explanation, discussion, non-verbal interaction, and interaction with physical artefacts were observed while students were engaged in engineering design tasks. Results suggest that table shape influences student behaviour when taking into account different group sizes and different genders.

Review and Challenges of Technologies for Real-Time Human Behavior Monitoring

A person's behavior significantly influences their health and well-being. It also contributes to the social environment in which humans interact, with cascading impacts to the health and behaviors of others. During social interactions, our understanding and awareness of vital nonverbal messages expressing beliefs, emotions, and intentions can be obstructed by a variety of factors including greatly flawed self-awareness. For these reasons, human behavior is a very important topic to study using the most advanced technology. Moreover, technology offers a breakthrough opportunity to improve people's social awareness and self-awareness through machine-enhanced recognition and interpretation of human behaviors. This paper reviews (1) the social psychology theories that have established the framework to study human behaviors and their manifestations during social interactions and (2) the technologies that have contributed to the monitoring of human behaviors. State-of-the-art in sensors, signal features, and computational models are categorized, summarized, and evaluated from a comprehensive transdisciplinary perspective. This review focuses on assessing technologies most suitable for real-time monitoring while highlighting their challenges and opportunities in near-future applications. Although social behavior monitoring has been highly reported in psychology and engineering literature, this paper uniquely aims to serve as a disciplinary convergence bridge and a guide for engineers capable of bringing new technologies to bear against the current challenges in real-time human behavior monitoring.