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Simple Summary: Sex estimation based on bones is a technique used to determine the biological sex of an individual from skeletal remains. It relies on the anatomical differences between male and female skeletons. Various bone characteristics have been incorporated into methods for sex estimation. Linear measurements are commonly used features in classification models for sex estimation. On the other hand, angle measurements are rarely included in such models, although they are important characteristics of the geometry of the bones and could provide essential information for the discrimination between the male and female bones. The goal of this research is to examine the potential of cranial angles for sex estimation and to identify the set of the most dimorphic angles by applying machine learning algorithms. The development of current sexing methods largely depends on the use of adequate sources of data and adjustable classification techniques. Most sex estimation methods have been based on linear measurements, while the angles have been largely ignored, potentially leading to the loss of valuable information for sex discrimination. This study aims to evaluate the usefulness of cranial angles for sex estimation and to differentiate the most dimorphic ones by training machine learning algorithms. Computed tomography images of 154 males and 180 females were used to derive data of 36 cranial angles. The classification models were created by support vector machines, naïve Bayes, logistic regression, and the rule-induction algorithm CN2. A series of cranial angle subsets was arranged by an attribute selection scheme. The algorithms achieved the highest accuracy on subsets of cranial angles, most of which correspond to well-known features for sex discrimination. Angles characterizing the lower forehead and upper midface were included in the best-performing models of all algorithms. The accuracy results showed the considerable classification potential of the cranial angles. The study demonstrates the value of the cranial angles as sex indicators and the possibility to enhance the sex estimation accuracy by using them. [ABSTRACT FROM AUTHOR]

While gamification is gaining ground in business, marketing, corporate management, and wellness initiatives, its application in education is still an emerging trend. This article presents a study of the published empirical research on the application of gamification to education. The study is limited to papers that discuss explicitly the effects of using game elements in specific educational contexts. It employs a systematic mapping design. Accordingly, a categorical structure for classifying the research results is proposed based on the extracted topics discussed in the reviewed papers. The categories include gamification design principles, game mechanics, context of applying gamification (type of application, educational level, and academic subject), implementation, and evaluation. By mapping the published works to the classification criteria and analyzing them, the study highlights the directions of the currently conducted empirical research on applying gamification to education. It also indicates some major obstacles and needs, such as the need for proper technological support, for controlled studies demonstrating reliable positive or negative results of using specific game elements in particular educational contexts, etc. Although most of the reviewed papers report promising results, more substantial empirical research is needed to determine whether both extrinsic and intrinsic motivation of the learners can be influenced by gamification.

Digital Libraries (DL) are offering access to a vast amount of digital content, relevant to practically all domains of human knowledge, which makes it suitable to enhance teaching and learning. Based on a systematic literature review, this article provides an overview and a gap analysis of educational use of DLs.

The world is embracing an open education model. The success of this process implies an adequate awareness, an assumption that is inconsistent with recent reports and statistical facts. Despite the major advances in recent years, Open Educational Resources (OER) are still not in the mainstream of Computer Science course development. Motivated by the need to fill this gap, this paper analyzes the evolution of the OER development and the emerging trends relevant to Computer Science education. The aim is to raise the awareness and promote a practical transition process towards an adequate model that aligns the interests of all stakeholders.

Learning is a goal driven social activity determined by motivational factors. To be able to efficiently gamify learning for improved student motivation and engagement, the educators have to understand the related aspects studied in games, motivational psychology and pedagogy. This will help them to identify the factors that drive and explain desired learning behaviors. This paper presents a survey of the main approaches employed in gamification and the emerging new directions in the context of the relevant motivational psychology and pedagogy. The focus is on the motivational factors that impact learning and understanding of behavior change. The purpose of the paper is two-fold: on one side, to provide analysis and guide to relevant works related to gamification along with outlining the emerging trends, and on the other, to provide foundation for evaluation and identification of the areas of possible improvements.