Since the outbreak of COVID-19, online learning has gained popularity among educators and learners, where Community of Inquiry (CoI) has caught researchers’ attention. To bibliometrically analyze the framework of CoI over twenty-five years, we adopted both qualitative and quantitative research methods to examine the framework of CoI in online learning contexts. We concluded that teaching presence, social presence, cognitive presence, metacognition, and self-efficacy played important roles in the framework of CoI. This study also explored the top ten authors, sources, organizations, and countries using VOSviewer and established citation networks through the clustering techniques in CitNetExplorer. Future research could focus on how to motivate the educational institutes and educators to change their traditional educational methods and whether to include both metacognition and self-efficacy in the CoI framework. 

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A bibliometric analysis of Community of Inquiry in online learning contexts over twenty-five years

PurposeThis study intends to examine whether the reasons learners like or dislike a learning environment can help explain the differences in the characteristics of the learner and whether learner characteristics can influence a learner's preference for a learning environment.Design/methodology/approachThis study adopted an exploratory sequential mixed methods research design. The study first conducted focus groups with university students to uncover their learner characteristics by examining the reasons they liked or disliked a learning environment. This was followed by a questionnaire survey to explore how these learner characteristics influenced learner inclination for a learning environment. The survey data were analysed using exploratory and confirmatory factor analysis (partial least squares structural equation modelling).FindingsThe findings showed that two types of learner characteristics, i.e. online learner characteristics and classroom learner characteristics, significantly affected learner inclination for a learning environment. Analyses also indicated that learner demographics had no major moderating effect between learner characteristics and learner inclination for a learning environment.Practical implicationsThe findings can be useful for education institutions, learning designers and academics to design engaging learning activities to better support different learning needs.Originality/valueThis study makes a novel attempt to distinguish learner characteristics based on the reasons learners like or dislike a learning environment and establishes that individual learners' characteristics play a role in influencing their preference for a specific learning environment.

Exploring the role of learner characteristics in learners' learning environment preferences

Writing is a challenging task for English Foreign Language (EFL) instruction. Based on artificial intelligence technology, Automatic Writing Evaluation (AWE) has received considerable attention from the EFL research community in recent years, since it can provide timely and personalized feedback to EFL writing learners. However, researchers have pointed out that while AWE can provide satisfactory feedback on vocabulary use and grammar, it is relatively inadequate at providing efficient feedback on organization, coherence, and content. Spherical Video-based Virtual Reality (SVVR) can provide a highly immersive and in-depth interaction learning environment that makes up for this shortcoming. Authentic experiences help enhance EFL writing learners’ perceptions and understanding of context, and assist students in creating constructive internal connections between their personal experiences and the topic of their writing, thus improving their writing quality. Therefore, the current study proposed an approach which integrated SVVR and AWE to investigate its effects on EFL writing. To investigate the effectiveness of the proposed approach, a quasi-experiment was carried out in a university’s EFL writing course. The experimental group (37 students) used the SVVR–AWE approach, while the control group (39 students) used the conventional approach with AWE. The results showed that the learning method not only considerably enhanced the students’ EFL writing performance, but also raised their motivation, self-efficacy, and sense of presence, as well as reduced their EFL writing anxiety. Furthermore, interviews were performed and a thematic inductive qualitative analysis of the interview data was conducted to investigate the impact of this learning method on students’ learning behaviors and perceptions.

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An Integrated Automatic Writing Evaluation and SVVR Approach to Improve Students’ EFL Writing Performance

Background There is a strong association between interactions and cognitive engagement, which is crucial for constructing new cognition and knowledge. Although interactions and cognitive engagement have attracted extensive attention in online learning environments, few studies have revealed the evolution of cognitive engagement with interaction levels. Objectives The study aims to automatically identify learners' interactions and cognitive engagement and then analyse the evolution of learners' cognitive engagement with interaction levels and during different stages of online learning. Methods The participants of the study were learners who participated in an online open course. Their text data from discussion forums on five learning themes were collected. Data were analysed using text mining and ENA. Results Learners' cognitive engagement in online learning was related to interaction levels. As learners' online interaction levels changed from surface to deep, cognitive engagement levels changed from low to high. With the continuous occurrence of deep interactions, cognitive feedback became more complex. At the social–emotional interaction level, although learners' cognitive engagement levels began to change from low to high, complex cognitive feedback was still insufficient. In addition, the analysis of the evolution of cognitive engagement during different stages of online learning showed that learners' patterns of cognitive engagement changed significantly as the learning process continued, from initially dynamic and complex to a stable development pattern. Implications The results of the study are of theoretical significance and practical guidance for further understanding the relationship between online interaction levels and cognitive engagement as well as the process of online collaborative knowledge exploration, construction, and even connectivity. 

Understanding the evolution of cognitive engagement with interaction levels in online learning environments: Insights from learning analytics and epistemic network analysis

The COVID-19 pandemic has affected medical and dental care in Korea. This study aimed to investigate the factors influencing the intention to use dental care in industrial workers, an economically active population. An online questionnaire survey was completed by 301 industrial workers. Stress and anxiety to viral epidemics-6 (SAVE-6), attitude, subjective norm, perceived behavior control, and intention to use dental care, based on the theory of planned behavior (TPB), were measured. Predisposing, enabling, and need factors based on the Andersen model were measured as control variables for influencing factors. In the final hierarchical multiple regression analysis, the most significant relevant factors affecting intention to use dental care were attitude (β = 0.598, p < 0.001), followed by experience of dental clinic visits (β = 0.237, p < 0.001), subjective norm (β = 0.125, p < 0.001), perceived behavior control (β = 0.114, p = 0.004), SAVE-6 (β = −0.073, p = 0.025), and gingival bleeding (β = 0.062, p = 0.029). Dental care use decreased to 58.5%, and socio-psychological factors based on TPB were closely associated with the intention to use dental care. Therefore, awareness must be raised regarding oral health practices to increase the use of dental care among industrial workers.

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Factors Related to the Intention to Use Dental Care by Industrial Workers Due to COVID-19: Application of Anderson Model and Planned Behavior Theory

The interactions among all members of an online learning community significantly impact collaborative reflection (co-reflection). Although the relationship between learners’ roles and co-reflection levels has been explored by previous researchers, it remains unclear when and with whom learners at different co-reflection levels tend to interact. This study adopted multiple methods to examine the interaction patterns of diverse roles among learners with different co-reflection levels based on 11,912 posts. First, the deep learning technique was applied to assess learners’ co-reflection levels. Then, a social network analysis (SNA) was conducted to identify the emergent roles of learners. Furthermore, a lag sequence analysis (LSA) was employed to reveal the interaction patterns of the emergent roles among learners with different co-reflection levels. The results showed that most learners in an online learning community reached an upper-middle co-reflection level while playing an inactive role in the co-reflection process. Moreover, higher-level learners were superior in dialog with various roles and were more involved in self-rethinking during the co-reflection process. In particular, they habitually began communication with peers and then with the teacher. Based on these findings, some implications for facilitating online co-reflection from the perspective of roles is also discussed.

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Identifying Learners’ Interaction Patterns in an Online Learning Community

The emotion of humans is an important indicator or reflection of their mental states, e.g., satisfaction or stress, and recognizing or detecting emotion from different media is essential to perform sequence analysis or for certain applications, e.g., mental health assessments, job stress level estimation, and tourist satisfaction assessments. Emotion recognition based on computer vision techniques, as an important method of detecting emotion from visual media (e.g., images or videos) of human behaviors with the use of plentiful emotional cues, has been extensively investigated because of its significant applications. However, most existing models neglect inter-feature interaction and use simple concatenation for feature fusion, failing to capture the crucial complementary gains between face and context information in video clips, which is significant in addressing the problems of emotion confusion and emotion misunderstanding. Accordingly, in this paper, to fully exploit the complementary information between face and context features, we present a novel cross-attention and hybrid feature weighting network to achieve accurate emotion recognition from large-scale video clips, and the proposed model consists of a dual-branch encoding (DBE) network, a hierarchical-attention encoding (HAE) network, and a deep fusion (DF) block. Specifically, the face and context encoding blocks in the DBE network generate the respective shallow features. After this, the HAE network uses the cross-attention (CA) block to investigate and capture the complementarity between facial expression features and their contexts via a cross-channel attention operation. The element recalibration (ER) block is introduced to revise the feature map of each channel by embedding global information. Moreover, the adaptive-attention (AA) block in the HAE network is developed to infer the optimal feature fusion weights and obtain the adaptive emotion features via a hybrid feature weighting operation. Finally, the DF block integrates these adaptive emotion features to predict an individual emotional state. Extensive experimental results of the CAER-S dataset demonstrate the effectiveness of our method, exhibiting its potential in the analysis of tourist reviews with video clips, estimation of job stress levels with visual emotional evidence, or assessments of mental healthiness with visual media.

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Emotion Recognition from Large-Scale Video Clips with Cross-Attention and Hybrid Feature Weighting Neural Networks

The goal of this study is to explore the affordances of spherical video-based virtual reality (SVVR)-based learning activities by comparing the students’ learning engagement models and their writing learning outcomes in a double-loop SVVR-based (DL-SVVR) learning activity and a single-loop SVVR-based (SL-SVVR) learning activity, respectively. For this purpose, we conduct an empirical study involving 82 fourth-grade students. The statistical results show that the students in the DL-SVVR group had higher learning behavioral engagement and better writing learning outcomes than those in the SL-SVVR group. Furthermore, as for the interrelationship between the subscales of students’ learning engagement, we find that, for the students in SL-SVVR group, their behavioral engagement can be positively predicted by the emotional engagement, while in the DL-SVVR group, students’ behavioral engagement can be positively predicted by their social engagement. For both groups, their social engagement can positively predict the emotional engagement. Importantly, our empirical results also show that the double-loop learning approach can potentially promote students’ writing learning outcomes by shifting their social engagement and behavioral engagement. The qualitative analysis results indicate that peer interaction has a positive impact on improving students’ learning interest and writing outcomes.

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Investigating Learners’ Engagement and Chinese Writing Learning Outcomes with Different Designs of SVVR-Based Activities

Multimodal sentiment analysis (MSA), which goes beyond the analysis of texts to include other modalities such as audio and visual data, has attracted a significant amount of attention. An effective fusion of sentiment information in multiple modalities is key to improving the performance of MSA. However, aligning multiple modalities during the process of fusion faces challenges such as maintaining modal-specific information. This paper proposes a Text-centered Fusion Network with crossmodal Attention (TeFNA), a multimodal fusion network that uses crossmodal attention to model unaligned multimodal timing information. In particular, TeFNA employs a Text-Centered Aligned fusion method (TCA) that takes text modality as the primary modality to improve the representation of fusion features. In addition, TeFNA maximizes the mutual information between modality pairs to maintain task-related emotional information, thereby ensuring that the key information of modalities from input to fusion is preserved. The results of our comprehensive experiments on the multimodal datasets of CMU-MOSI and CMU-MOSEI show that our proposed model outperforms methods in terms of most metrics used. 

TeFNA: Text-centered fusion network with crossmodal attention for multimodal sentiment analysis

Students always suffer from burnout during online learning. Although social support and cognitive engagement are associated with learning burnout, limited information is available on whether and how these relationships change over time in an online learning setting. This study investigated the longitudinal relationships between social support, cognitive engagement, and learning burnout and the further relations associated with performance. Forum data and self-reported data from 116 online college students were analyzed at two different timepoints. We found received social support is not related to learning burnout but shows a positive relationship with cognitive engagement that increases over time. Perceived social support is always positively correlated with cognitive engagement and negatively associated with learning burnout. Cognitive engagement has a negative relation with learning burnout that increases over time. Moreover, the negative relationship between learning burnout and performance emerges in the middle of the term but not at the end, thus indicating a significant change over time. The findings have significant ramifications for both preventing and intervening in online learning burnout. 

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Xuemei Wu

Papers

Identifying Learners’ Interaction Patterns in an Online Learning Community

Emotion Recognition from Large-Scale Video Clips with Cross-Attention and Hybrid Feature Weighting Neural Networks

Investigating Learners’ Engagement and Chinese Writing Learning Outcomes with Different Designs of SVVR-Based Activities

TeFNA: Text-centered fusion network with crossmodal attention for multimodal sentiment analysis

Longitudinal exploration of online learning burnout: the role of social support and cognitive engagement