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Studies show that executive functions and motor development are associated with each other and with learning ability. A more technological lifestyle combined with digital culture should be considered a viable alternative to stimulate children's development. Therefore, this study aimed to present a systematic mapping of the literature involving executive functions, motor development, and the use of digital games in intervention programs for elementary school children from 6 to 11 years old. Four databases were researched: PubMed, Scielo, Science Direct, and SCOPUS, including publications between 2012 and March 2021. The initial results indicated 4881 records. After the selection process, 15 manuscripts that presented the central theme of the study were selected. The main results indicated that intervention strategies are rather heterogeneous. Most of the studies demonstrated efficient results after intervention protocols, many of them were conducted in Europe, and 46% occurred in a school environment. No research was identified involving technological solutions using executive functions, motor development, and digital games in an integrated manner. Hence, this constitutes a field of future scientific research.

Distance learning has assumed a relevant role in the educational scenario. The use of Virtual Learning Environments contributes to obtaining a substantial amount of educational data. In this sense, the analyzed data generate knowledge used by institutions to assist managers and professors in strategic planning and teaching. The discovery of students' behaviors enables a wide variety of intelligent services for assisting in the learning process. This article presents a literature review in order to identify the intelligent services applied in distance learning. The research covers the period from January 2010 to May 2021. The initial search found 1316 articles, among which 51 were selected for further studies. Considering the selected articles, 33% (17/51) focus on learning systems, 35% (18/51) propose recommendation systems, 26% (13/51) approach predictive systems or models, and 6% (3/51) use assessment tools. This review allowed for the observation that the principal services offered are recommendation systems and learning systems. In these services, the analysis of student profiles stands out to identify patterns of behavior, detect low performance, and identify probabilities of dropouts from courses.

Distance Learning has enabled educational practices based on digital platforms, generating massive amounts of data. Several initiatives use this data to identify dropout contexts, mainly providing teacher support about student behavior. Approaches such as Active Methodologies are known as having good potential to involve and motivate students. This article presents a systematic mapping aiming to identify current Educational Data Mining and Learning Analytics methods. Besides, we identify Active Methodologies' application to mitigate dropout in Distance Learning. We evaluated 668 papers published from January 2015 to March 2020. The results indicate a growing application of Educational Data Mining and Learning Analytics to identify and mitigate students' abandonment in Distance Learning. However, studies with Active Methodologies to minimize dropout and enhance student permanence are scarce. Some works suggest Active Methods as a possible complement of Learning Analytics in dropout. [Note: The page range (171-204) shown on the PDF is incorrect. The correct page range is p171-203.]

The application of ubiquitous technologies in the improvement of education strategies is called ubiquitous learning. This strategy amplifies the pedagogical potential of e-learning through a ubiquitous and contextualized perspective. On the other hand, a ubiquitous technological mediation in learning can also increase the isolation of learners and reduce the integration among colleagues. Strategies to encourage the group learning can minimize these possible side effects. In this sense, this article proposes UbiGroup, an agent-based model for ubiquitous recommendation of educational contents for groups of learners. UbiGroup aims to help teachers to search, select and distribute educational materials for groups. The model considers the group profile and the context where learners are. The recommendation for dynamic groups of learners through a consensus profile is the main scientific contribution of this research. The model was evaluated through simulated scenarios. The results were encouraging and show potential for implementing UbiGroup in real learning environments.

The popularity of Mobile Instant Messaging (MIM) has prompted educators to integrate it in teaching and learning in higher education. WhatsApp® is a multi-platform instant messaging application widely used worldwide, however, there is still little applied research on its use as a platform for educational activities in management higher education. In this article, the authors present a quantitative and qualitative assessment of a concrete experience of WhatsApp® use that involved 140 undergraduate management students. Data were collected through questionnaires answered by the participants after the end of the experience of use, and also via content analysis of their conversations inside their WhatsApp® groups. The results indicate five main educational affordances of MIM that can be considered in management education: interactivity, knowledge sharing, sense of presence, collaboration and ubiquity. The article also explores the limitations of this tool and provides suggestions of good practices of MIM use for teaching and learning.

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. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is an incurable disease and a type of dementia. About 55 million people around the world have AD. However, technologies have been used to assist in the healthcare of AD, supporting physicians in the palliative care of patients. This article presents a systematic mapping study (SMS) to identify articles that use technologies to monitor patients with AD in order to show an overview of the literature, identifying gaps and research opportunities in this field. The scientific contribution of this work is to identify monitoring technologies related to AD and highlight current trends on the subject. The paper uses the term technologies as hardware infrastructure and devices or systems without considering software technologies. In addition, this article proposes a taxonomy for the domain of technologies applied to AD patients. The SMS study was conducted in six databases, including articles from 1997 to 2021. An initial search resulted in 7,781 articles. After applying filter criteria, throwing automatic selection on databases, and manual assortment, 171 articles were selected. Subsequently, a second search was performed to reduce the list of articles and filter by the specific search objective of articles focused on technologies for monitoring with tracking, resulting in 74 works. The main results obtained are: (1) a relevant number of articles (43.42%) reported solutions used in sensor-based devices; (2) several works (33.33%) have the interaction focus on Position/Distance/Proximity/Location sensor type; (3) another group of articles has a secondary focus on Emergency help (18.97%). The results indicated the need for technologies to help caregivers monitor patients, in addition to evidence of research opportunities in palliative care and support for the daily activities of AD patients. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Noncommunicable diseases (NCDs) or chronic diseases are responsible for 41 million deaths each year, equivalent to 71% of all worldwide deaths. Many technologies are used to aid the treatment of NCDs, and data analysis has been used as an approach to improve the understanding of human behaviour related to risk factors. This study aims to distinguish how human behaviour data analysis has been applied to support the treatment and prevention of NCDs, what technologies are currently used, and what gaps are still left unexplored. We conducted a systematic mapping study to analyse academic articles published from 2010 to September 2021. A filtering process mitigated article bias by reviewing, analysing, and classifying 41 works from 12,395 collected. The main results obtained presented that 43% applied data analysis in depression, 17% applied for general NCDs, and 12% for diabetes. Whereas, machine learning represents 60% of technologies found in the articles, mobile devices 58%, and wearables 29%. This study proposes two taxonomies obtained from the analysis of the selected articles that allow systematised guides to access the knowledge produced in the study. In addition, the taxonomies link technologies used to identify human behaviour with associated NCDs. [ABSTRACT FROM AUTHOR]