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 Previously, several related surveys have been conducted, but they are either outdated or they donot consider the important aspects of context-awareness, adaptation and pedagogy in the domain of ACALEs.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.

Music composing is associated with various positive learning outcomes, but in several countries, such as Finland, it is not part of the primary school music curriculum. There are several issues as to why music composing is not taught at schools, such as beliefs that composing requires extensive knowledge of music theory, lack of teachers’ confidence, lack of evidence on the method’s effectiveness and difficulty of assessment. Composing software has the potential of solving some of these issues, as they are connected to mathematics via music theory and technology, and with practical opportunities arising from adopting phenomenon-based learning at schools, the affordances of music composing technologies for learning mathematics are investigated in this study. For this purpose, 57 music composing software were categorised and reviewed. Our analysis identified eight types of music visualisations and five types of note input methods. The music visualisations were compared to the mathematics content in the Finnish primary school curriculum and the note input methods were evaluated based on their relationship to the music visualisations. The coordinate grid-based piano roll was the most common visualisation and the tracker visualisation had the most affordances for learning primary school math. Music composing software were found to have affordances for teaching mathematical concepts, notations and basic calculus skills, among others. Composing methods involving direct interaction with visualisations support the experiential learning of music theory, and consequently, the learning of mathematics. Based on the findings of this study, we concluded that music composing is a promising activity through which mathematics and music theory can be learned at primary schools.

In recent years, affective computing has been actively researched to provide a higher level of emotion-awareness. Numerous studies have been conducted to detect the user’s emotions from physiological data. Among a myriad of target emotions, boredom, in particular, has been suggested to cause not only medical issues but also challenges in various facets of daily life. However, to the best of our knowledge, no previous studies have used electroencephalography (EEG) and galvanic skin response (GSR) together for boredom classification, although these data have potential features for emotion classification. To investigate the combined effect of these features on boredom classification, we collected EEG and GSR data from 28 participants using off-the-shelf sensors. During data acquisition, we used a set of stimuli comprising a video clip designed to elicit boredom and two other video clips of entertaining content. The collected samples were labeled based on the participants’ questionnaire-based testimonies on experienced boredom levels. Using the collected data, we initially trained 30 models with 19 machine learning algorithms and selected the top three candidate classifiers. After tuning the hyperparameters, we validated the final models through 1000 iterations of 10-fold cross validation to increase the robustness of the test results. Our results indicated that a Multilayer Perceptron model performed the best with a mean accuracy of 79.98% (AUC: 0.781). It also revealed the correlation between boredom and the combined features of EEG and GSR. These results can be useful for building accurate affective computing systems and understanding the physiological properties of boredom.

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.