Digital games have traditionally been targeted at younger generations, although the proportion of older adult players is increasing. However, the design processes of digital games often do not consider the special needs of older adults. Co-design is a potential method to address this, but there is little research on co-designing games with older adults. In our study, we proposed a co-design process model that considers the intergenerational perspective. Using this model, eight older adults (two males and six females aged 47–80) and 22 sixth graders (11 males and 11 females aged 12–13) co-designed a digital game. The content of the game was based on old concepts used by the designers during their childhood. Similarly, game content involving new words and concepts were produced by the sixth graders. We collected data using semi-structured interviews and observations during the co-design process over a period of 24 months and then processed the data using grounded theory. The results indicated that the older adults identified seven game elements as essential to make games fun—appearance and aesthetics, competition, manageability of gameplay, social impact, familiarity, unpredictability, and intergenerational gameplay. Furthermore, we identified six assets that older adults have as game co-designers and five challenges that co-designing games with older adults may entail.

Exergames, i.e. games which aim to increase player’s physical activity, are a prominent sub-category of serious games (SGs). Recently, location-based games (LBGs) similar to Pokémon GO have gained the attention of exergame designers as they have been able to reach people who would otherwise not be motivated to exercise. Multiple studies have been conducted on Pokémon GO alone, identifying positive outcomes related to, for example, exercise and social well-being. However, with substantial findings derived from a single game, it is unclear whether the identified benefits of playing Pokémon GO are present in other similar games. In order to broaden the understanding of LBGs as exergames, this study investigates the gameplay features and initial reactions of early adopters to a game called Harry Potter: Wizards Unite (HPWU) which was launched in summer 2019. A questionnaire (N=346) was sent to HPWU players to measure the effects playing the game has on their physical activity. During the first week of play, an increase in mild physical activity was recorded among HPWU players, similar to what has been reported with Pokémon GO. Also almost half of respondents (46,82%) reported to play the game socially, showcasing how LBGs can generally have a positive impactalso on players’ social well-being.

In recent years, Virtual Reality (VR) technology has found their way into higher education. Its power lays in its ability to provide immersive three-dimensional (3D) experiences that help conveying educational content whilst providing rich interaction possibilities. Especially in mining engineering education, VR has high potential to reshape the provided learning content. Field trips, i.e. mine visits, are an integral part of the education and necessary to transfer knowledge to students. However, field trips are time and cost intensive and mines often have tight entry regulations. As a result, the number of field trips is limited. VR-based field trips offer a considerable alternative presupposed they replicate the complex mining environment realistically. In addition, VR mines have the advantage of taking students close to events (e.g. explosions) that are impossible to demonstrate in a real mine. However, generating realistic 3D content for VR still involves complex, and thus time consuming tasks. Therefore, we present the design of a VR Framework for teaching mining students based on 360° video data, its evaluation in three different lectures, and its extension based on the feedback we received from students and teachers from four different universities.

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.

Mobile augmented reality (MAR) has emerged as a mainstream technology to provide novel visualization and interaction opportunities across application domains. The primary forte of MAR is its ability to bridge the real world with virtual worlds by bringing virtual elements onto a real-world view, and by adapting the experience according to the user’s location and other context parameters. Research has shown that MAR possesses a multitude of affordances in the field of education. These affordances can be amplified in educational MAR games (EMARGs) due to the motivational value and the fun factor provided by intriguing game elements. However, there is a gap in research on design guidelines for EMARGs, especially regarding the connection to motivators and disturbance factors that may have positive and negative effects respectively on the learning experience. In this chapter, we first describe related background, and then present two MAR case studies—a treasure hunt and a story-driven adventure game—to illustrate our experiences in designing EMARGs. We conduct a qualitative analysis of the case studies based on questionnaire answers and interviews of 29 and 112 participants respectively, to identify motivators (16, 20) and disturbance factors (11, 25) in the participants’ gameplay experiences. Through an analysis of the motivators, disturbance factors and our design experiences, we proposed 24 design guidelines in six categories that can potentially strengthen motivators and diminish disturbance factors in MAR applications.

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.

Despite the high number of educational games released, only a few games have a strong story that is more than an excuse for players’ actions. Furthermore, even fewer story-based games utilise the affordances of augmented reality (AR) to concretise abstract concepts while engaging players.Based on our literature review, we were inspired to merge AR into a story-based educational mobile game for teaching fractions to elementary school students. The game Tales & Fractions was created through a two-phase multidisciplinary development process. In order to successfully integrate AR into a story-based educational game, we employed an adapted version of the Scrum agile software development method implemented by a multidisciplinary team of experts from computer science, pedagogy, design and arts. During the development process, we faced many issues that other story-based AR game developers could meet. We summarised the encountered issues with our solutions which could be useful for developers to avoid common pitfalls and to enrich the user engagement.