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).

Language learners often face communication problems when they need to express themselves and do not have the ability to do so. On the other hand, continuous advances in technology are creating new opportunities to improve second language (L2) acquisition through context-aware ubiquitous learning (CAUL) technology. Since vocabulary is the foundation of all language acquisition, this article presents ULearnEnglish, an open-source system to allow ubiquitous English learning focused on incidental vocabulary acquisition. To evaluate our proposal, 15 learners used the developed system, and 10 answered a survey based on the Technology Acceptance Model (TAM). Results indicate a favorable response to the application of incidental learning techniques in combination with the learner context. ULearnEnglish achieved an acceptance rate of 78.66% for the perception of utility, 96% for the perception of ease of use, 86.5% for user context assessment, and 88% for ubiquity. Among its main contributions, this study demonstrates a possible tool for ubiquitous use in the future in language learning; additionally, further studies can use the available resources to develop the system.