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The purpose of this study is to investigate students’ attitudes, self-efficacy and emotional behaviour associated with online learning and the effectiveness of online learning.

A research model was formulated and analysed with the structural equation modelling technique. The respondents consist of 843 students pursuing their studies at a private university’s foundation, undergraduate and postgraduate levels. A two-step systematic approach was used using the SmartPLS version 3 software to conduct statistical analysis and draw meaningful insights.

The study’s findings have demonstrated that students’ attitudes and self-efficacy exhibit a positive relationship with online learning behaviour (OLB). It is observed that the students’ emotions are related to online learning effectiveness (OLE) and mediate the relationship between OLB and OLE. Furthermore, OLB partially mediates the relationship between attitude and OLE and between self-efficacy and OLE.

The operational instructions and digital resources have proven to be highly effective in providing valuable learning experiences to the students. As a result, the students are now expanding and applying their new encounters to a broader range of learning opportunities. This study has provided valuable insights for stakeholders, including scholars, higher education institutions and the Ministry of Higher Education, in providing the ideas of online learning or Web-based education.

The originality of this study sheds light on the role of OLB as a mediator. It was underlined that emotion is critical in improving students’ OLE. Thus, students’ attitudes and self-efficacy have been essential in reassuring OLB and enhancing OLE.

This paper elucidates the role of anger in error management (EM) and organizational learning behaviors. The study explores how anger can catalyze learning, emphasizing its strategic implications.

A double-layered moderated-mediated model was developed and tested using data from 744 Chinese CEOs. The psychometric properties of the survey instrument were rigorously examined through structural equation modeling, and hypotheses were tested using Hayes's PROCESS macros.

The findings reveal that anger is a precursor for recognizing the value of significant errors, leading to a positive association with learning behavior among top management team members. Additionally, the study uncovers a triple interaction effect of anger, EM culture and supply chain disruptions on the value of learning from errors. Extensive experience and positive grieving strengthen the relationship between recognizing value from errors and learning behavior.

This study uniquely integrates affect-cognitive theory and organizational learning theory, examining anger in EM and learning. The authors provide empirical evidence that anger can drive error value recognition and learning. The authors incorporate a more fine-grained approach to leadership when including executive anger as a trigger to learning behavior. Factors like experience and positive grieving are explored, deepening the understanding of emotions in learning. The authors consider both negative and positive emotions to contribute to the complexity of organizational learning.

We argue this method of inquiry better represents the participants' learning, lives and experiences in the formal neoliberal education system prioritising performativity, categorising and ranking students.

The paper explores using poetry as a research method to reveal the learning experiences of adult learners, who have often had disruptive experiences of the formal schooling system and return to study in community-based education spaces. Inspired by Laurel Richardson’s transgressive technique of presenting sociological data through poetry as method, we use poetic representations of these learners' lives alongside case study research methodology. The research was conducted in conjunction with Neighbourhood Houses in Victoria, Australia. Qualitative data were generated through conducting multiple case studies of learners across various adult community education (ACE) sites. In this research, some case studies were presented in the traditional method of writing biography, others were written in the form of found poetry, which we refer to as data as poetry and text. The paper uses found poetry through participant-voiced poems written from interview transcripts. We argue this method of inquiry better represents the participants' learning, lives and experiences in the formal neoliberal education system prioritising performativity, categorising and ranking students. Our findings highlight the benefits of using poetry to communicate data in case study research as it effectively represents the experiences of adult learners' lives in a creative and concise form, transgressing normative practices of writing education research. These poetic representations of data reveal learner experiences in an embodied and agentic way while providing readers with a deep and rich understanding of these crucial adult learning spaces.

Our findings highlight the benefits of using poetry to communicate data in case study research as it effectively represents the experiences of adult learners' lives in a creative and concise form, transgressing normative practices of writing education research.

This research paper is empirical research and has not been submitted elsewhere for publication.

With the advancement of technology, microlearning has emerged as a promising method to improve the efficacy of teaching and learning. This study aims to investigate the document types, volume, growth trajectory, geographic contribution, coauthor relationships, prominent authors, research groups, influential documents and publication outlets in the microlearning literature.

We adapt the PRISMA guidelines to assess the eligibility of 297 Scopus-indexed documents from 2002 to 2021. Each was manually labeled by educational level. Descriptive statistics and science mapping were conducted to highlight relevant objects and their patterns in the knowledge base.

This study confirms the increasing trend of microlearning publications over the last two decades, with conference papers dominating the microlearning literature (178 documents, 59.86%). Despite global contributions, a concentrated effort from scholars in 15 countries (22.39%) yielded 68.8% of all documents, while the remaining papers were dispersed across 52 other nations (77.61%). Another significant finding is that most documents pertain to three educational level categories: lifelong learning, higher education and all educational levels. In addition, this research highlights six key themes in the microlearning domain, encompassing (1) Design and evaluation of mobile learning, (2) Microlearning adaptation in MOOCs, (3) Language teaching and learning, (4) Workflow of a microlearning system, (5) Microlearning content design, (6) Health competence and health behaviors. Other aspects analyzed in this study include the most prominent authors, research groups, documents and references.

The finding represents all topics at various educational levels to offer a comprehensive view of the knowledge base.

This study investigates the relationship between feelings of loneliness in the workplace, life satisfaction, affect, hope and expressivity among hotel employees.

The research model was tested via structural equation modeling based on the empirical data collected from hotel employees in Antalya, Turkey.

The research findings suggest that emotional deprivation and social companionship have a significant impact on life satisfaction, that life satisfaction has a significant impact on positive and negative emotions, and that positive and negative emotions have the same impact on pathways and agencies.

The research findings should assist researchers and practitioners to understand the behaviors of hotel employees in continuous interaction and relationship with individuals to motivate them while providing more effective services.

Using sentiment analysis (SA), this study aims to examine the impact of COVID-19 on mental health and virtual learning experiences among 1,125 students at a public Argentinean faculty.

A study was conducted during the COVID-19 pandemic, surveying 1,125 students to gather their opinions. The survey data was analysed using text mining tools and SA. SA was used to extract the students’ emotions, views and feelings computationally and identify co-occurrences and patterns in related words. The study also examines educational policies implemented after the pandemic.

The prevalent emotions expressed in the comments were trust, sadness, anticipation and fear. A combination of trust and fear resulted in submission. Negative comments often included the words “virtual”, “virtual classroom”, “virtual classes” and “professor”. Two significant issues were identified: teachers’ inexperience with virtual classes and inadequate server infrastructure, leading to frequent crashes. The most effective educational policies addressed vital issues related to the “virtual classroom”.

Text mining and SA are valuable tools for decision-making during uncertain times, such as the COVID-19 pandemic. They can also provide insights to recover quality assurance processes at universities impacted by health concerns or external shocks.

The paper makes two main contributions: it conducts a SA to gain insights from comments and analyses the relationship between emotions and sentiments to identify optimal educational policies. The study pioneers exploring the link between emotions, policies and the pandemic at a public university in Argentina. This area of research still needs to be explored.

Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction environment. However, two major challenges exist in the field of audiovisual emotion recognition: (1) how to effectively capture representations of each single modality and eliminate redundant features and (2) how to efficiently integrate information from these two modalities to generate discriminative representations.

A novel key-frame extraction-based attention fusion network (KE-AFN) is proposed for audiovisual emotion recognition. KE-AFN attempts to integrate key-frame extraction with multimodal interaction and fusion to enhance audiovisual representations and reduce redundant computation, filling the research gaps of existing approaches. Specifically, the local maximum–based content analysis is designed to extract key-frames from videos for the purpose of eliminating data redundancy. Two modules, including “Multi-head Attention-based Intra-modality Interaction Module” and “Multi-head Attention-based Cross-modality Interaction Module”, are proposed to mine and capture intra- and cross-modality interactions for further reducing data redundancy and producing more powerful multimodal representations.

Extensive experiments on two benchmark datasets (i.e. RAVDESS and CMU-MOSEI) demonstrate the effectiveness and rationality of KE-AFN. Specifically, (1) KE-AFN is superior to state-of-the-art baselines for audiovisual emotion recognition. (2) Exploring the supplementary and complementary information of different modalities can provide more emotional clues for better emotion recognition. (3) The proposed key-frame extraction strategy can enhance the performance by more than 2.79 per cent on accuracy. (4) Both exploring intra- and cross-modality interactions and employing attention-based audiovisual fusion can lead to better prediction performance.

The proposed KE-AFN can support the development of engaging and empathetic human–computer interaction environment.

The applications of Artificial Intelligence (AI) in various areas of professional and knowledge work are growing. Emotions play an important role in how users incorporate a technology into their work practices. The current study draws on work in the areas of AI-powered technologies adaptation, emotions, and the future of work, to investigate how knowledge workers feel about adopting AI in their work.

We gathered 107,111 tweets about the new AI programmer, GitHub Copilot, launched by GitHub and analysed the data in three stages. First, after cleaning and filtering the data, we applied the topic modelling method to analyse 16,130 tweets posted by 10,301 software programmers to identify the emotions they expressed. Then, we analysed the outcome topics qualitatively to understand the stimulus characteristics driving those emotions. Finally, we analysed a sample of tweets to explore how emotional responses changed over time.

We found six categories of emotions among software programmers: challenge, achievement, loss, deterrence, scepticism, and apathy. In addition, we found these emotions were driven by four stimulus characteristics: AI development, AI functionality, identity work, and AI engagement. We also examined the change in emotions over time. The results indicate that negative emotions changed to more positive emotions once software programmers redirected their attention to the AI programmer's capabilities and functionalities, and related that to their identity work.

Overall, as organisations start adopting AI-powered technologies in their software development practices, our research offers practical guidance to managers by identifying factors that can change negative emotions to positive emotions.

Our study makes a timely contribution to the discussions on AI and the future of work through the lens of emotions. In contrast to nascent discussions on the role of AI in high-skilled jobs that show knowledge workers' general ambivalence towards AI, we find knowledge workers show more positive emotions over time and as they engage more with AI. In addition, this study unveils the role of professional identity in leading to more positive emotions towards AI, as knowledge workers view such technology as a means of expanding their identity rather than as a threat to it.

Cybersecurity competitions can effectively develop skills, but engaging a wide learner spectrum is challenging. This study aims to investigate the perceptions of cybersecurity competitions among Reddit users. These users constitute a substantial demographic of young individuals, often participating in communities oriented towards college students or cybersecurity enthusiasts. The authors specifically focus on novice learners who showed an interest in cybersecurity but have not participated in competitions. By understanding their views and concerns, the authors aim to devise strategies to encourage their continuous involvement in cybersecurity learning. The Reddit platform provides unique access to this significant demographic, contributing to enhancing and diversifying the cybersecurity workforce.

The authors propose to mine Reddit posts for information about learners’ attitudes, interests and experiences with cybersecurity competitions. To mine Reddit posts, the authors developed a text mining approach that integrates computational text mining and qualitative content analysis techniques, and the authors discussed the advantages of the integrated approach.

The authors' text mining approach was successful in extracting the major themes from the collected posts. The authors found that motivated learners would want to form a strategic way to facilitate their learning. In addition, hope and fear collide, which exposes the learners’ interests and challenges.

The authors discussed the findings to provide education and training experts with a thorough understanding of novice learners, allowing them to engage them in the cybersecurity industry.

Construction enterprises are achieving the goal of production safety by increasingly focusing on the critical factor of “human” and the impact of individual characteristics on safety performance. Emotional intelligence is categorized into three models: skill-based, trait-based and emotional learning systems. However, the mechanism of action and the internal relationship between emotional intelligence and safety performance must be further studied. This study intends to examine the internal mechanism of emotional intelligence on safety performance in construction projects, which would contribute to the safety management of construction enterprises.

A structural equation model exploring the relationship between emotional intelligence and safety performance is developed, with political skill introduced as an independent dimension, situational awareness presented as a mediator, and management safety commitment introduced as a moderator. Data were collected by a random questionnaire and analyzed by SPSS 24.0 and AMOS 26.0. The structural equation model tested the mediation hypothesis, and the PROCESS macro program tested the moderated mediation hypothesis.

The results showed that construction workers' emotional intelligence directly correlates with safety performance, and situational awareness plays a mediating role in the relationship between emotional intelligence and the safety performance of construction workers. Management safety commitment weakens the positive predictive relationships between emotional intelligence and situational awareness and between emotional intelligence and safety performance.

This research reveals a possible impact of emotional intelligence on safety performance. Adding political skills to the skill-based model of emotional intelligence received a test pass. Political skill measures the sincere and cooperative skills of construction workers. Using people as a critical element plays a role in the benign mechanism of “Emotional Intelligence – Situational Awareness – Safety Performance.” Improving emotional intelligence skills through training, enhancing situational awareness, understanding, anticipation and coordination and activating management environment factors can improve safety performance. Construction enterprises should evaluate and train workers' emotional intelligence to improve workers' situational awareness and safety performance.