Active learning, a student-centered approach, engages students in the learning process and requires them to solve problems using educational activities that enhance their learning outcomes. Augmented Reality (AR) has revolutionized the field of education by creating an intuitive environment where real and virtual objects interact, thereby facilitating the understanding of complex concepts. Consequently, this research proposes an application, called “Cooking Math”, that utilizes AR to promote active learning in sixth-grade elementary school mathematics. The application comprises various educational games, each presenting a real-life problem, particularly focused on cooking recipes. To evaluate the usability of the proposed AR application, a pilot study was conducted involving three groups: (a) 65 undergraduate philosophy and education students, (b) 74 undergraduate engineering students, and (c) 35 sixth-grade elementary school students. To achieve this, (a) the System Usability Scale (SUS) questionnaire was provided to all participants and (b) semi-structured interviews were organized to gather the participants’ perspectives. The SUS results were quite satisfactory. In addition, the interviews’ outcomes indicated that the elementary students displayed enthusiasm, the philosophy and education students emphasized the pedagogy value of such technology, while the engineering students suggested that further improvements were necessary to enhance the effectiveness of the learning experience.

It was late 2015 when Northeast Brazil noticed a worrying increase in neonates born with microcephaly and other congenital malformations. These abnormalities, characterized by an abnormally small head and often neurological impairment and later termed Congenital Zika Syndrome, describe the severity of neurodevelopmental and nephrological outcomes in early childhood, and the implication of microcephaly at birth. The purpose of the study was to describe the neurodevelopmental outcomes in children exposed to Zika virus during fetal life, with and without microcephaly at birth. The systematic review included research studies about the neurodevelopmental outcomes with and without microcephaly, as well as nephrological outcomes in early childhood. We searched PubMed, Crossref, PsycINFO, Scopus, and Google Scholar publications and selected 19 research articles published from 2018 to 2021. Most studies have linked the severity of microcephaly in childbirth to the neurodevelopmental and urinary outcomes in early childhood. However, most children without microcephaly at birth develop typically, while others may be at risk for language impairment.

Education is an activity that involves great cognitive load for learning, understanding, concentrating, and other high-level cognitive tasks. The use of the electroencephalogram (EEG) and other brain imaging techniques in education has opened the scientific field of neuroeducation. Insights about the brain mechanisms involved in learning and assistance in the evaluation and optimization of education methodologies according to student brain responses is the main target of this field. Being a multidisciplinary field, neuroeducation requires expertise in various fields such as education, neuroinformatics, psychology, cognitive science, and neuroscience. The need for a comprehensive guide where various important issues are presented and examples of their application in neuroeducation research projects are given is apparent. This paper presents an overview of the current hardware and software options, discusses methodological issues, and gives examples of best practices as found in the recent literature. These were selected by applying the PRISMA statement to results returned by searching PubMed, Scopus, and Google Scholar with the keywords “EEG and neuroeducation” for projects published in the last six years (2018–2024). Apart from the basic background knowledge, two research questions regarding methodological aspects (experimental settings and hardware and software used) and the subject of the research and type of information used from the EEG signals are addressed and discussed.

The correlation between the kind of cesarean section and post-traumatic stress disorder (PTSD) in Greek women after a traumatic birth experience has been recognized in previous studies along with other risk factors, such as perinatal conditions and traumatic life events. Data from early studies have suggested some possible links between some vulnerable factors and the potential development of postpartum PTSD. The classification of each case in three possible states (PTSD, profile PTSD, and free of symptoms) is typically performed using the guidelines and the metrics of the version V of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) which requires the completion of several questionnaires during the postpartum period. The motivation in the present work is the need for a model that can detect possible PTSD cases using a minimum amount of information and produce an early diagnosis. The early PTSD diagnosis is critical since it allows the medical personnel to take the proper measures as soon as possible. Our sample consists of 469 women who underwent emergent or elective cesarean delivery in a university hospital in Greece. The methodology which is followed is the application of random decision forests (RDF) to detect the most suitable and easily accessible information which is then used by an artificial neural network (ANN) for the classification. As is demonstrated from the results, the derived decision model can reach high levels of accuracy even when only partial and quickly available information is provided.

Education is an activity that involves great cognitive load for learning, understanding, concentrating and other high level cognitive tasks. The use of the electroencephalogram (EEG) and other brain imaging techniques in education has opened the scientific field of neuroeducation. Insights about the brain mechanisms involved in learning and assistance in evaluation and optimization of education methodologies according to student brain responses is the main target of this field. Being a multidisciplinary field, neuroeducation requires expertise in various fields such as education, neuroinformatics, psychology, cognitive science and neuroscience. The need for a comprehensive guide where various important issues are presented and examples of their application in neuroeducation research projects are given is apparent. This paper presents an overview of the current hardware and software options, discusses methodological issues, and gives examples of best practices as found in the recent literature. These were selected applying the PRISMA statement on results returned by searching in PubMed, Scopus and Google Scholar with keywords “EEG and neuroeducation” for projects published on the last six years (2018-2024). Apart from the basic background knowledge, two research questions regarding methodological aspects (experimental settings, hardware and software used) and the subject of research and type of information used from the EEG signals are addressed and discussed.

This work is inspired by the ability of neural systems to control the behavior of specialized actuator mechanisms in living organisms by monitoring the end-effect of their actions. We consider as an example of such an actuator mechanism the human vocal tract where neurons learn to activate its muscles that move the velum, jaw, tongue and the lips, in order to exhibit desired phonetic activity. As a technical approximation to such a setup, we use an artificial neural network (ANN) and a speech synthesizer and we study the capability of the ANN to estimate the synthesizer’s parameters targeting desired speech activity. In this setup, we assume that the training error is obtained by measuring the “perceived distance” between the original (target) and the synthesized speech signals. Thus, the training error needs to be measured after processing the output of the speech synthesizer, instead of measuring it directly at the outputs of the ANN. This operational requirement on error measurement restricts the application of widely used ANN training algorithms that are based on back propagation of gradients but can be met by our earlier proposed “Heuristically Enhanced Gradient Approximation” (HEGA) algorithm. We also propose enhancements to HEGA that further optimize its performance in this demanding application.