Adaptive context-aware learning environments (ACALEs) can detect the learner’s context and adapt learning materi-als to match the context. The support for context-awareness and adaptation is essential in these systems so that they can makelearning contextually relevant. Previously, several related surveys have been conducted, but they are either outdated or they donot consider the important aspects of context-awareness, adaptation and pedagogy in the domain of ACALEs. To alleviate this,a comprehensive literature search on ACALEs was first performed. After filtering the results, 53 studies that were publishedbetween 2010 and 2018 were analyzed. The highlights of the results are: (i) mobile devices (PDAs, mobile phones, smartphones)are the most common client types, (ii) RFID/NFC are the most common sensors, (iii) ontology is the most common context mod-eling approach, (iv) context data typically originates from the learner profile or the learner’s location, (v) rule-based adaptationis the most used adaptation mechanism, and (vi) informative feedback is the most common feedback type. Additionally, we con-ducted a trend analysis on technology usage in ACALEs throughout the covered timespan, and proposed a taxonomy of contextcategories as well as several other taxonomies for describing various aspects of ACALEs. Finally, based on the survey results,directions for future research in the field were given. These results can be of interest to educational technology researchers andto developers of adaptive and context-aware applications.

This is the second of two comprehensive volumes that provide a thorough and multi-faceted research into the emerging field of augmented reality games and consider a wide range of its major issues. These first ever research monographs on augmented reality games have been written by a team of 70 leading researchers, practitioners and artists from 20 countries. 

Volume II explores the most important and challenging issues that have been raised by the use of the Augmented Reality approach and technology in the gamification of education, healthcare, medicine and art. The volume deals with a systematic analysis of educational augmented reality games, their use for health promotion in old age and for improving people’s well-being, the gamification of augmented reality art and immersive reading experiences, among other topics.

An increasing number of countries have recently included programming education in their curricula. Similarly, utilizing programming concepts in gameplay has become popular in the videogame industry. Although many games have been developed for learning to program, their variety and their correspondence to national curricula remain an uncharted territory. Consequently, this paper has three objectives. Firstly, an investigation on the guidelines on programming education in K‐12 in seven countries was performed by collecting curricula and other relevant data official from governmental and non‐profit educational websites. Secondly, a review of existing acquirable games that utilize programming topics in their gameplay was conducted by searching popular game stores. Lastly, we compared the curricula and made suggestions as to which age group the identified games would be suitable. The results of this study can be useful to educators and curriculum designers who wish to gamify programming education.

Recently, commercial physiological sensors and computing devices have become cheaper and more accessible, while computer systems have become increasingly aware of their contexts, including but not limited to users’ emotions. Consequently, many studies on emotion recognition have been conducted. However, boredom has received relatively little attention as a target emotion due to its diverse nature. Moreover, only a few researchers have tried classifying boredom using electroencephalogram (EEG). In this study, to perform this classification, we first reviewed studies that tried classifying emotions using EEG. Further, we designed and executed an experiment, which used a video stimulus to evoke boredom and non-boredom, and collected EEG data from 28 Korean adult participants. After collecting the data, we extracted its absolute band power, normalized absolute band power, differential entropy, differential asymmetry, and rational asymmetry using EEG, and trained these on three machine learning algorithms: support vector machine, random forest, and k-nearest neighbors (k-NN). We validated the performance of each training model with 10-fold cross validation. As a result, we achieved the highest accuracy of 86.73% using k-NN. The findings of this study can be of interest to researchers working on emotion recognition, physiological signal processing, machine learning, and emotion-aware system development.

Step counting (SC) algorithms can be applied to different areas such as well-being applications, games, and indoor navigation. Many existing SC algorithms for smartphones use data from inertial sensors to infer the number of steps taken, but their usefulness in real-life situations is limited since typically only a few positions and orientations are supported. Moreover, the algorithms may suffer from dynamic orientation and position changes during walking. To alleviate these shortcomings, we propose the Position and Orientation Independent Step Counting Algorithm (POISCA), which uses an accelerometer and a gyroscope to count the number of steps while allowing the smartphone’s position and orientation to change dynamically. In a nutshell, the algorithm first determines the orientation of the smartphone, and then detects zero crossings with a predetermined buffer range. 48 young adults (36 males, 12 females) participated in an experiment that simulated a real-life scenario to evaluate the performance of POISCA against three other step counting algorithms. The data from 24 participants were randomly assigned to a training group, which was then used to establish threshold parameters for POISCA. The remaining 24 participants’ data were used for accuracy measurement. The results show that POISCA outperforms the other algorithms with a Symmetric Mean Absolute Percentage Error of 4.54%, which can be lower if the algorithm is calibrated for each user. The results suggest that POISCA has potential for use in real-life situations where changes in position and orientation of the smartphone are dynamic.

The recent proliferation of affordable physiological sensors has boosted research and development efforts of emotion-aware systems. Boredom has received relatively little attention as a target emotion, and we identified a lack of research on the relationship between eye gaze and electroencephalogram (EEG) when people feel bored. To investigate this matter, we first conducted a background study on boredom and its detection by physiological methods. Then, we designed and executed an experiment that uses a video stimulus – specifically designed for this experiment, yet general enough for other boredom research – with an eye tracker and EEG sensor to elicit and detect boredom. Moreover, a questionnaire was used to confirm the existence of boredom. The experiment was based on a hypothesis that participants may feel bored when their gaze deviates from an expected area of interest, thus indicating loss of attention. The results of the experiment indicated correlations between eye gaze data and EEG data with all participants (N = 13) when they felt bored. This study can be useful for researchers who have interest in developing boredom-aware systems.

Understanding engagement in games provides great opportuni- ties for developing motivating educational games. However, even good games may induce disturbances on the learner. Therefore, we go further than present- ing only results and discussion related to the motivation aspects and disturbance factors of the playing experience in UFractions (Ubiquitous fractions) storytell- ing mobile game. Namely, we define the dynamics between these two important game features. Sample of the case study was 305 middle school pupils in South Africa, Finland, and Mozambique.

Guidelines for game developers, users and educators were derived from the interplay of disturbance factors and motivations. Furthermore, we defined six different learning zones deriving from disturbances the player is facing and the player’s motivation level.

As the global demand for programmers is soaring, several countries have integrated programming into their K-12 curricula. Finding effective ways to engage children in programming education is an important objective. One effective method for this can be presenting learning materials via games, which are known to increase engagement and motivation. Current programming education games often focus on a single genre and offer one- size-fits-all experience to heterogeneous learners. In this study, we presented Minerva, a multi-genre (adventure, action, puzzle) game to engage elementary school students in learning programming concepts. The game content is adapted to play and learning styles of the player to personalize the gameplay. We conducted a formative mixed-method evaluation of Minerva with 32 Korean 6th grade students who played the game and compared their learning outcomes with 32 6th grade students who studied the same concepts using handouts. The results indicated that, in terms of retention, learning was equally effective in both groups. Furthermore, the game was shown to facilitate engagement among the students. These results, together with uncovered issues, will guide Minerva’s further development.

This chapter describes how the Ebola virus is considered extremely infectious with a series of physical and psychological traumas on the victims. Common clinical signs associated with the disease include a sudden fever, severe headaches, muscle pain, fatigue, diarrhea, vomiting, and unexplained hemorrhages. In Africa, with strained medical facilities and remote localities, prompt identification and diagnosis of the symptoms of Ebola in a suspected patient are important to the control of the epidemic and in curtailing further spread. This chapter presents the development of an Android mobile application called EbolaDiag (Ebola Diagnosis), which is capable of supporting the diagnosis, screening, and healthcare experts working on the frontline in contact tracing and monitoring of the spread of Ebola. Furthermore, EbolaDiag is suitable for aiding the strained medical facilities in endemic areas. In addressing this gap, the application provided a model for implementing such solutions in pandemic environments. Such a solution becomes more relevant and useful to combat Ebola and several other diseases in similar environments.

Games have emerged as promising tools to make learning more fun. Pedagogical effectiveness of an educational game can increase if its behavior changes according to learners’ play and learning styles. Several models for categorizing learning and play styles exist, but not many studies simultaneously detect and utilize both style groups. To alleviate this, as the first contribution, we analyzed and compared existing learning and play style models, and chose the most suitable one from each group. Personality style models were also discussed. We then created a questionnaire based on Honey and Mumford’s Learning Style Questionnaire and Bartle’s Player Types, and collected data from 127 South Korean elementary school children. The results indicated that specific play styles were clearly more dominant (Killer 18%, Achiever 24%, Explorer 32%, Socializer 41%), whereas dominant learning styles were distributed more evenly (Activist 33%, Reflector 37%, Theorist 20% and Pragmatist 25%). As the second contribution, we presented the foundations of a generic adaptation model for utilizing learning and play styles for designing adaptive educational games.