The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) in educational settings has revolutionized the traditional teaching-learning environment, giving rise to the concept of smart classrooms. This transformation not only enhances the monitoring of people and processes but also impacts the dynamics of student behaviors, which are influenced by various factors such as the teaching environment and teacher-student interactions. Recognizing and accurately identifying these behaviors is crucial for educators to implement effective interventions and improve learning outcomes. This study conducts a systematic literature mapping to examine the contemporary landscape of student behavior identification in smart classrooms. The analysis reveals a rich diversity of methodologies and approaches used in this area. Key contributions of this research include the development of taxonomies for 37 technologies deployed, 25 challenges faced, 58 student behaviors observed, as well as the identification of nine subjects benefiting from this data and 10 methodologies for processing behavioral information. By offering an overview of the technological and methodological underpinnings of behavior identification in smart classrooms, this study significantly propels forward the domain of smart classroom research, equipping educators and technologists with a holistic understanding of how to navigate and leverage the complexities associated with monitoring student behavior.

Learning evolves through human interactions and adapting to technological advancements. The fourth industrial revolution elevated the importance of machines in improving competitiveness, efficiency, and product quality. However, this emphasis on technology overshadowed the human-centric perspective. In contrast, Industry 5 aims to foster collaboration between humans, robots, and digital systems. Our study utilized systematic mapping to explore collaboration in I5. Specifically, we investigated how collaborative learning occurs in industrial workplaces. The main objective was to identify prevalent collaborative learning techniques, technologies, challenges, and data types in I5 environments. The results reveal various collaborative learning approaches, highlighting their potential to improve productivity, innovation, and worker participation in I5. The study uncovers significant trends, such as the increasing reliance on digital platforms and Artificial Intelligence (AI)-driven tools, which facilitate collaborative learning while posing unique challenges. Our taxonomy provides a structured framework for understanding these dynamics, serving as a valuable guide for practitioners and researchers. The findings underscore the necessity for adaptive learning strategies and the integration of advanced technologies to promote effective collaboration in industrial settings. This research contributes to the theoretical understanding of collaborative learning in I5 and offers practical insights for its implementation, thereby supporting the evolution of industry practices in this new technological era.

The use of game design elements in non-game contexts, defined as gamification, is being used to increase user engagement in non-game environments, such as workplaces, schools, or software applications. However, the challenge developers face when implementing gamification is identifying which game elements will engage users. Besides, the proposals often tend to support only the most common user types and engagement factors. In response to this challenge, this study proposes a generic gamification model, GamiProM. GamiProM helps to design gamified solutions by using an ontology that encompasses knowledge about gamification elements and mechanics, user types, and types of motivations. Using profile management, GamiProM aims to provide knowledge representation and add semantic value to user characteristics and the information generated by gamification. A case study allowed the evaluation of the model through the gamification of an existing application using GamiProM. The results indicate that GamiProM can identify user profiles that support personalization through a combination of software, rules, and ontologies.

Anxiety and stress are common emotional responses for human beings, but their chronic manifestation can lead to physical and psychological illnesses. The advancement of sensing technologies, such as Internet of Things, has contributed to the understanding and assisting events related to anxiety and stress. However, the main challenge is knowing which approaches can be used to better monitor these emotions and assist people. Based on a systematic literature review, this work analyzed studies both to determine how data is collected, and to monitor anxiety and stress levels. Two taxonomies synthesize the techniques mapped. The results indicated more emphasis on studying stress than anxiety and more focus on detecting anxiety and stress levels than on assisting the user. Among the main techniques to collect data, 62.5% of the studies used physiological data like heart data, and for data analysis techniques, 48% of the studies used Decision Trees.

Although many benefits emerge from the growing capabilities of smartphones, there are also concerns related to the long-term hyper-connected experience. Based on a systematic mapping method, this study investigates the primary factors of problematic smartphone use (PSU). Initially, this mapping considered ten academic databases, which allowed the analysis of 436 studies, and the creation of a taxonomy that categorises technology addiction topics such as the Internet, Smartphones, Video games, and Electronic devices. After the initial search and filtering, the study selected and deeply analysed 115 articles concerning the PSU influences on mental health, proposing a taxonomy to classify mental disorders and common symptoms related to PSU. The outcomes suggest that those who fear missing out on important events, females, depressed, anxious, and bored people are prone to PSU, reinforcing the importance of understanding the factors that lead a person to use smartphones in a problematic way and alternatives to help people cope with PSU. Scales such as the Smartphone Addiction Scale-Short Version (SAS-SV) and strategies such as cognitive-behavioural therapy (CBT) and limiting smartphone access are being used to handle PSU. Finally, this study presents implications and recommendations for future research in this area.

Studies indicate the growth and relevance of systems that can recognize affective states in the most different areas. A tendency to more natural environments could be noticed that considering physical or physiological signs and the individual’s relationship with the environment, other people, and daily activities. Therefore, what is the importance of combining ubiquitous computing, affective computing, and the contributions of context-aware information to provide more accurate and intelligent affective systems? This study presents a systematic literature mapping about the use of contextual information to identify affective states, which covered articles published between 2010 and October 2021, resulting in 1.638 studies. After applying filters, which we explain further in this article, we selected 49 works to answer a set of research questions. The results indicate that physiological data was the main parameter for recognizing affective signs (62.3%, 33/53), followed by visual data (32.1%, 17/53). The links between context and affective signs presented a more significant occurrence in the combination of contexts related to activities and physiological data (34%, 18/53).

In the Project Management context, the right allocation of the individuals about their competences and availability for tasks execution reduces the risk as the planning deviations from the time, cost and quality of the project. To achieve these benefits, it is relevant to identify people with specific skills and knowledge required for the development of each project activity. In this sense, this article proposes a model to support the activity management and allocation of individuals in projects through an ontology based on the concepts of the Theory of Activity, seeking to help through consultations the allocating of appropriate resource. Through the scenarios presented for evaluation was demonstrated the suitability to use the model for assistance of project managers.