23,899 research outputs found

    Implications of learning analytics for serious game design

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    This paper addresses the implications of combining learning analytics and serious games for improving game quality, monitoring and assessment of player behavior, gaming performance, game progression, learning goals achievement, and user’s appreciation. We introduce two modes of serious games analytics: in-game (real time) analytics, and post-game (off-line) analytics. We also explain the GLEANER framework for in-game analytics and describe a practical example for off-line analytics. We conclude with a brief outlook on future work, highlighting opportunities and challenges towards a solid uptake of SGs in authentic educational and training

    A Smart Collaborative Educational Game with Learning Analytics to Support English Vocabulary Teaching

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    Learning Analytics (LA) approaches have proved to be able to enhance learning process and learning performance. However, little is known about applying these approaches for second language acquisition using educational games. Therefore, this study applied LA approaches to design a smart collaborative educational game, to enhance primary school children learning English vocabularies. Specifically, the game provided dashboards to the teachers about their students in a real-time manner. A pilot experiment was conducted in a public primary school where the students’ data from experimental and control groups, namely learning and motivation test scores, interview and observation, were collected and analyzed. The obtained results showed that the experimental group (who used the smart game with LA) had significantly higher motivation and performance for learning English vocabularies than the control group (who used the smart game without LA). The findings of this study can help researchers and practitioners incorporate LA in their educational games to help students enhance language acquisition

    Creating opportunities to learn social skills at school using digital games

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    Acquiring skills for social and emotional well-being is important for inclusive societies and academic achievement. Studies have demonstrated the beneficial link between prosocial behaviours and improved results in curriculum topics. This paper describes a Prosocial Learning (PSL) process for creation and delivery of digital games for children (7-10 yrs) within educational systems that support learning of prosocial skills. The approach combines prosocial pedagogies with advanced ICT technologies and cloud delivery models to create attractive and exciting learning opportunities for children; produce novel digital game-based pedagogies and simplify deployment.Prosociality is a concept that refers to an individual’s propensity towards positive social behaviours. Individuals with prosocial skills are, for example, able to join in conversations, talk nicely, identifying feelings and emotions in themselves and others, identify someone needs help and ask for help. PSL classifies these skills in terms of Friendship, Feelings and Cooperation. By using interactive digital games supported by additional instructive and reflective activities, PSL allows children to learn social skills that can be generalised to real life situations in the classroom, playground and at home.PSL is implemented through a technology platform offering systematic pedagogical support for prosocial games developed by an ecosystem of teachers and games companies. Capabilities include multi-modal sensors to observe emotional affect, game interaction and decision-making. Information is acquired through standard protocols (e.g. xAPI) and evaluated by learning analytics algorithms to provide real-time feedback on player behaviours that are be used for in-game feedback and adaptation, and by teachers to shape follow-up activities. PSL is validated through short and longitudinal studies at European schools to gather evidence for effectiveness. This paper provides early evidence from short studies that will steer larger pan-European trials to test hypotheses, promote to policy makers and to increase adoption of game-based learning in school

    Using Game Learning Analytics for Validating the Design of a Learning Game for Adults with Intellectual Disabilities.

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    Serious Games, defined as a game in which education (in its various forms) is the primary goal rather than entertainment, have been proven as an effective educational tool for engaging and motivating students (Michael & Chen, 2006). However, more research is needed to sustain the suitability of these games to train users with cognitive impairments. This empirical study addresses the use of a Serious Game for training students with Intellectual Disabilities in traveling around the subway as a complement to traditional training. Fifty-one (51) adult people with Down Syndrome, mild cognitive disability or certain types of Autism Spectrum Disorder, all conditions classified as intellectual disabilities, played the learning game Downtown, A Subway Adventure which was designed ad-hoc considering their needs and cognitive skills. We used standards-based Game Learning Analytics techniques (i.e. Experience API –xAPI), to collect and analyze learning data both off-line and in near-real time while the users were playing the videogame. This article analyzes and assesses the evidence data collected using analytics during the game sessions, like time completing tasks, inactivity times or the number of correct/incorrect stations while traveling. Based on a multiple baseline design, the results validated both the game design and the tasks and activities proposed in Downtown as a supplementary tool to train skills in transportation. Differences between High-Functioning and Medium-Functioning users were found and explained in this paper, but the fact that almost all of the students completed at least one route without mistakes, the general improvement trough sessions and the low-mistake ratio are good indicators about the appropriateness of the game design.pre-print311 K

    Project sanitarium:playing tuberculosis to its end game

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    Interdisciplinary and collaborative projects between industry and academia provide exceptional opportunities for learning. Project Sanitarium is a serious game for Windows PC and Tablet which aims to embed learning about tuberculosis (TB) through the player taking on the role of a doctor and solving cases across the globe. The project developed as a collaboration between staff and undergraduate students at the School of Arts, Media and Computer Games at Abertay University working with academics and researchers from the Infection Group at the University of St Andrews. The project also engaged industry partners Microsoft and DeltaDNA. The project aimed to educate students through a workplace simulation pedagogical model, encourage public engagement at events and through news coverage and lastly to prototype whether games could be used to simulate a virtual clinical trial. The project was embedded in the Abertay undergraduate programme where students are presented with real world problems to solve through design and technology. The result was a serious game prototype that utilized game design techniques and technology to demystify and educate players about the diagnosis and treatment of one of the world’s oldest and deadliest diseases, TB. Project Sanitarium aims to not only educate the player, but allows the player to become a part of a simulated drug trial that could potentially help create new treatments in the fight against TB. The game incorporates a mathematical model that is based on data from real-world drug trials. The interdisciplinary pedagogical model provides undergraduates with workplace simulation, wider industry collaboration and access to academic expertise to solve challenging and complex problems

    Dialogue as Data in Learning Analytics for Productive Educational Dialogue

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    This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances have the potential to foster effective dialogue using learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting such dialogue. However, the translation of much prior learning science research to online contexts is complex, requiring the operationalization of constructs theorized in different contexts (often face-to-face), and based on different datasets and structures (often spoken dialogue). In this paper, we explore what could constitute the effective analysis of productive online dialogues, arguing that it requires consideration of three key facets of the dialogue: features indicative of productive dialogue; the unit of segmentation; and the interplay of features and segmentation with the temporal underpinning of learning contexts. The paper thus foregrounds key considerations regarding the analysis of dialogue data in emerging learning analytics environments, both for learning-science and for computationally oriented researchers
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