Jake McMullen, Phuong Bui, Boglárka Brezovszky, E. Lehtinen, M. Hannula-Sormunen
The traditional classroom setting presents challenges when it comes to strengthening adaptive expertise in mathematics education through deliberate practice. This study aimed to investigate whether the Number Navigation Game (NNG) could help promote deliberate practice, and whether students’ performance in the game was related to their development of Adaptive Number Knowledge, perceived challenge, flow, and math interest. NNG is a game-based learning environment that requires students to progress by solving increasingly complex arithmetic problems, which is crucial for promoting adaptive number knowledge. Game performances of 214 Finnish students were analyzed and compared to the best possible performance for each game level. A growth mixture model based on the students' relative performance levels was used to gain insight into how students' game performance changed throughout the game, and how this related to their knowledge gains, perceived challenge, math motivation, and flow. There were four different profiles of students' game performance. The largest profile consisted of students who steadily improved their performance in the game, despite initially having lower-than-average performance. This group experienced lower levels of flow but achieved larger learning gains than the other groups, suggesting that their engagement may be more aligned with deliberate practice.
{"title":"Mathematical game performance as an indicator of deliberate practice","authors":"Jake McMullen, Phuong Bui, Boglárka Brezovszky, E. Lehtinen, M. Hannula-Sormunen","doi":"10.17083/ijsg.v10i4.634","DOIUrl":"https://doi.org/10.17083/ijsg.v10i4.634","url":null,"abstract":"The traditional classroom setting presents challenges when it comes to strengthening adaptive expertise in mathematics education through deliberate practice. This study aimed to investigate whether the Number Navigation Game (NNG) could help promote deliberate practice, and whether students’ performance in the game was related to their development of Adaptive Number Knowledge, perceived challenge, flow, and math interest. NNG is a game-based learning environment that requires students to progress by solving increasingly complex arithmetic problems, which is crucial for promoting adaptive number knowledge. Game performances of 214 Finnish students were analyzed and compared to the best possible performance for each game level. A growth mixture model based on the students' relative performance levels was used to gain insight into how students' game performance changed throughout the game, and how this related to their knowledge gains, perceived challenge, math motivation, and flow. There were four different profiles of students' game performance. The largest profile consisted of students who steadily improved their performance in the game, despite initially having lower-than-average performance. This group experienced lower levels of flow but achieved larger learning gains than the other groups, suggesting that their engagement may be more aligned with deliberate practice.","PeriodicalId":196187,"journal":{"name":"Int. J. Serious Games","volume":"14 8","pages":"113-130"},"PeriodicalIF":0.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139238191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stefanie Vanbecelaere, Febe Demedts, B. Reynvoet, F. Depaepe
Adaptive digital games for learning have been introduced as a motivating way for children to learn as they can provide instant feedback, embed the learning content in an attractive narrative, and adapt instruction according to individual needs of students. Although studies showed benefits of using adaptive digital educational games, a framework for analyzing research on the effectiveness of adaptive digital games is lacking. In this paper, we propose such a framework that accounts for a broad evaluation and is defined by (1) the learner variables that can affect effectiveness, (2) the adaptivity implemented in the tool, and (3) the learning outcomes being assessed . Next, this framework is used to describe recent intervention studies on the effectiveness of adaptive digital games in the context of K-12 education. We end with some concluding thoughts on the merits of such a framework for assessing the effectiveness of digital games and perspectives for future research.
{"title":"Toward a framework for analyzing adaptive digital games' research effectiveness","authors":"Stefanie Vanbecelaere, Febe Demedts, B. Reynvoet, F. Depaepe","doi":"10.17083/ijsg.v10i4.618","DOIUrl":"https://doi.org/10.17083/ijsg.v10i4.618","url":null,"abstract":"Adaptive digital games for learning have been introduced as a motivating way for children to learn as they can provide instant feedback, embed the learning content in an attractive narrative, and adapt instruction according to individual needs of students. Although studies showed benefits of using adaptive digital educational games, a framework for analyzing research on the effectiveness of adaptive digital games is lacking. In this paper, we propose such a framework that accounts for a broad evaluation and is defined by (1) the learner variables that can affect effectiveness, (2) the adaptivity implemented in the tool, and (3) the learning outcomes being assessed . Next, this framework is used to describe recent intervention studies on the effectiveness of adaptive digital games in the context of K-12 education. We end with some concluding thoughts on the merits of such a framework for assessing the effectiveness of digital games and perspectives for future research.","PeriodicalId":196187,"journal":{"name":"Int. J. Serious Games","volume":"16 1","pages":"77-91"},"PeriodicalIF":0.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139236571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kristian Kiili, Juho Siuko, Elizabeth B. Cloude, M. Dindar
Accumulating evidence indicates that game-based learning is emotionally engaging. However, little is known about the nature of emotions in game-based learning. We extended previous game-based learning research by examining epistemic emotions and their relations to motivational constructs. One-hundred-thirty-one (n=131) 15–18-year-old students played the Antidote COVID-19 game for 25 minutes. Data were collected on their epistemic emotions, flow experience, situational interest, and satisfaction that were measured after the game-playing session. Learners reported significantly higher intensity levels of positive epistemic emotions (excitement, surprise, and curiosity) than negative ones (boredom, anxiety, frustration, and confusion). The co-occurrence network analyses provided new insights into the relationships between motivational and emotional states, where high-intensity flow experience, situational interest, and satisfaction co-occurred the most often with positive epistemic emotions. Results also revealed that a high-intensity flow can be experienced without high levels of situational interest in the topic. That is, gameplay can engage learners even though the learning topic does not interest them. This highlights the importance of intrinsically integrating the learning content with core game mechanics, ensuring the processing of the learning content. The study demonstrated that epistemic emotions, flow experience, satisfaction, and situational interest reveal different qualities of game-based learning. The results suggest that at least flow, situational interest, and epistemic emotions should be measured to understand different dimensions of engagement in game-based learning. Overall, the study advances prior research by clarifying relationships between epistemic emotions and motivational constructs.
{"title":"Demystifying the Relations of Motivation and Emotions in Game-Based Learning: Insights from Co-Occurrence Network Analysis","authors":"Kristian Kiili, Juho Siuko, Elizabeth B. Cloude, M. Dindar","doi":"10.17083/ijsg.v10i4.629","DOIUrl":"https://doi.org/10.17083/ijsg.v10i4.629","url":null,"abstract":"Accumulating evidence indicates that game-based learning is emotionally engaging. However, little is known about the nature of emotions in game-based learning. We extended previous game-based learning research by examining epistemic emotions and their relations to motivational constructs. One-hundred-thirty-one (n=131) 15–18-year-old students played the Antidote COVID-19 game for 25 minutes. Data were collected on their epistemic emotions, flow experience, situational interest, and satisfaction that were measured after the game-playing session. Learners reported significantly higher intensity levels of positive epistemic emotions (excitement, surprise, and curiosity) than negative ones (boredom, anxiety, frustration, and confusion). The co-occurrence network analyses provided new insights into the relationships between motivational and emotional states, where high-intensity flow experience, situational interest, and satisfaction co-occurred the most often with positive epistemic emotions. Results also revealed that a high-intensity flow can be experienced without high levels of situational interest in the topic. That is, gameplay can engage learners even though the learning topic does not interest them. This highlights the importance of intrinsically integrating the learning content with core game mechanics, ensuring the processing of the learning content. The study demonstrated that epistemic emotions, flow experience, satisfaction, and situational interest reveal different qualities of game-based learning. The results suggest that at least flow, situational interest, and epistemic emotions should be measured to understand different dimensions of engagement in game-based learning. Overall, the study advances prior research by clarifying relationships between epistemic emotions and motivational constructs.","PeriodicalId":196187,"journal":{"name":"Int. J. Serious Games","volume":"217 ","pages":"93-112"},"PeriodicalIF":0.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139237114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study examines how 12-15-year-old students use information while playing Geome, a mixed reality game intended for museum school visits. Geome presents environmental issues, prompting students and asking them to discern and dismiss misinformation and rumors. The study aims to analyze the students' playful learning experience and their perspective on the game. It focuses on the students' critical thinking, interactions and beliefs about knowledge and knowing, referred to as personal epistemology. Adopting a qualitative approach, the research was conducted across three classes in a museum. A combination of audio, video, and in-game interactions was collected from specific moments during gameplay and analyzed according to epistemological dimensions (Certainty, Simplicity, Source, Justification). Video analysis suggests that when faced with ill-structured problems within a playful scenario, some students are spurred to actively process information and develop critical thinking skills. Meanwhile others remain entrenched in their initial conceptions about the nature of knowledge and the act of knowing. The study discusses how the game's characteristics shape students' personal epistemology. Overall, this research demonstrates that games in museum contexts have the potential to promote active learning and critical thinking in some students, when confronted with complex or ill-structured problems.
{"title":"Museum Games and Personal Epistemology: A Study on Students' Critical Thinking with a Mixed Reality Game","authors":"Simon Morard, Eric Sanchez, Catherine Bonnat","doi":"10.17083/ijsg.v10i4.695","DOIUrl":"https://doi.org/10.17083/ijsg.v10i4.695","url":null,"abstract":"This study examines how 12-15-year-old students use information while playing Geome, a mixed reality game intended for museum school visits. Geome presents environmental issues, prompting students and asking them to discern and dismiss misinformation and rumors. The study aims to analyze the students' playful learning experience and their perspective on the game. It focuses on the students' critical thinking, interactions and beliefs about knowledge and knowing, referred to as personal epistemology. Adopting a qualitative approach, the research was conducted across three classes in a museum. A combination of audio, video, and in-game interactions was collected from specific moments during gameplay and analyzed according to epistemological dimensions (Certainty, Simplicity, Source, Justification). Video analysis suggests that when faced with ill-structured problems within a playful scenario, some students are spurred to actively process information and develop critical thinking skills. Meanwhile others remain entrenched in their initial conceptions about the nature of knowledge and the act of knowing. The study discusses how the game's characteristics shape students' personal epistemology. Overall, this research demonstrates that games in museum contexts have the potential to promote active learning and critical thinking in some students, when confronted with complex or ill-structured problems.","PeriodicalId":196187,"journal":{"name":"Int. J. Serious Games","volume":"70 1","pages":"131-151"},"PeriodicalIF":0.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139238260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wilian Gatti Junior, Emily Marasco, Beaumie Kim, L. Behjat, Marjan Eggermont
Designing engaging serious board games that effectively address students' diverse and complex needs presents a significant challenge for educators. As a possible solution, Large Language Models (LLMs) such as ChatGPT can assist educators in designing and evaluating game-based learning experiences. This study explores three primary ways ChatGPT can enhance educators' game design process. Firstly, ChatGPT can assist with brainstorming, suggesting game themes and mechanisms aligned with curriculum and learning goals. Secondly, it can provide templates or exemplars of game components, allowing educators to create customized games that offer what their students need. Lastly, ChatGPT can offer valuable feedback on game prototypes, identifying areas for improvement and guidance to enhance the game's efficacy as an educational tool. We attempted to advance the ongoing discourse on the roles of artificial intelligence and board games in education by providing valuable insights into the potential of these tools.
{"title":"How ChatGPT can inspire and improve serious board game design","authors":"Wilian Gatti Junior, Emily Marasco, Beaumie Kim, L. Behjat, Marjan Eggermont","doi":"10.17083/ijsg.v10i4.645","DOIUrl":"https://doi.org/10.17083/ijsg.v10i4.645","url":null,"abstract":"Designing engaging serious board games that effectively address students' diverse and complex needs presents a significant challenge for educators. As a possible solution, Large Language Models (LLMs) such as ChatGPT can assist educators in designing and evaluating game-based learning experiences. This study explores three primary ways ChatGPT can enhance educators' game design process. Firstly, ChatGPT can assist with brainstorming, suggesting game themes and mechanisms aligned with curriculum and learning goals. Secondly, it can provide templates or exemplars of game components, allowing educators to create customized games that offer what their students need. Lastly, ChatGPT can offer valuable feedback on game prototypes, identifying areas for improvement and guidance to enhance the game's efficacy as an educational tool. We attempted to advance the ongoing discourse on the roles of artificial intelligence and board games in education by providing valuable insights into the potential of these tools.","PeriodicalId":196187,"journal":{"name":"Int. J. Serious Games","volume":"1 1","pages":"33-54"},"PeriodicalIF":0.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139236563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Antti Koskinen, Kristian Kiili, Francesca de Rosa, M. Dindar, Michael D. Kickmeier-Rust
This International Journal of Serious Games special issue is dedicated to the selected and extended best papers of the 2022 edition of the GALA conference. Professor Kristian Kiili and his team at Tampere University in Finland organized this edition of the conference. Authors of seven highly-rated conference submissions were invited to submit extended papers for this special issue. After peer review process, five extended papers were accepted. These papers shows how game-based learning and serious games continue to evoke extensive research efforts, and clearly demonstrates the breadth of different research approaches used in game-based learning. With such an open-minded approach, it is possible not only to advance our knowledge of game-based learning but also to contribute significantly to the understanding of the factors that influence human learning.
本期《国际严肃游戏期刊》特刊专门刊载了 2022 年版 GALA 会议的精选和扩展最佳论文。芬兰坦佩雷大学的克里斯蒂安-基利（Kristian Kiili）教授和他的团队组织了这届会议。 会议邀请七篇高分论文的作者为本期特刊提交扩展论文。经过同行评审，五篇扩展论文被录用。这些论文展示了基于游戏的学习和严肃游戏是如何不断唤起广泛的研究努力的，并清楚地表明了在基于游戏的学习中使用的不同研究方法的广泛性。有了这种开放性的研究方法，我们不仅有可能增进对游戏式学习的了解，还能为理解影响人类学习的因素做出重要贡献。
{"title":"Introduction to the Special Issue on GaLA Conf 2022","authors":"Antti Koskinen, Kristian Kiili, Francesca de Rosa, M. Dindar, Michael D. Kickmeier-Rust","doi":"10.17083/ijsg.v10i4.714","DOIUrl":"https://doi.org/10.17083/ijsg.v10i4.714","url":null,"abstract":"This International Journal of Serious Games special issue is dedicated to the selected and extended best papers of the 2022 edition of the GALA conference. Professor Kristian Kiili and his team at Tampere University in Finland organized this edition of the conference. Authors of seven highly-rated conference submissions were invited to submit extended papers for this special issue. After peer review process, five extended papers were accepted. These papers shows how game-based learning and serious games continue to evoke extensive research efforts, and clearly demonstrates the breadth of different research approaches used in game-based learning. With such an open-minded approach, it is possible not only to advance our knowledge of game-based learning but also to contribute significantly to the understanding of the factors that influence human learning.","PeriodicalId":196187,"journal":{"name":"Int. J. Serious Games","volume":"7 1","pages":"75-76"},"PeriodicalIF":0.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139236765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Information and communication technologies, such as serious games, have contributed to addressing the gaps in cognitive rehabilitation for individuals with acquired brain injury (ABI), particularly in the context of the COVID-19 pandemic. Although there are effective software programs and games available for cognitive rehabilitation, they have certain limitations. Most current programs have difficulties to adapt to individual performance, a critical factor in promoting neuroplasticity. Additionally, these programs typically only offer single-player modes. However, patients experience difficulties in social interactions leading to social isolation. To overcome these limitations, we propose a novel platform called CogniChallenge. It introduces multiplayer serious games designed for cognitive and psychosocial rehabilitation, offering competitive and cooperative game modes. This platform facilitates engagement with other patients, family members, caregivers, and virtual agents that simulate human interaction. CogniChallenge consists of three games based on activities of daily life and incorporates a multi-agent game balance system. Future research endeavors will focus on evaluating the usability and gameplay experience of CogniChallenge among healthcare professionals and individuals with ABI. By proposing this innovative platform, we intend to contribute to expanding the application of serious games and their potential to solve problems and limitations in the specific field of cognitive rehabilitation.
信息和通信技术（如严肃游戏）有助于弥补后天性脑损伤（ABI）患者认知康复方面的不足，尤其是在 COVID-19 大流行的背景下。虽然目前有一些有效的认知康复软件程序和游戏，但它们都有一定的局限性。目前大多数程序难以适应个人表现，而这正是促进神经可塑性的关键因素。此外，这些程序通常只提供单人游戏模式。然而，患者在社交互动中会遇到困难，导致社交孤立。为了克服这些局限性，我们提出了一个名为 "CogniChallenge "的新型平台。它引入了为认知和心理康复设计的多人严肃游戏，提供竞争和合作游戏模式。该平台可促进与其他患者、家庭成员、护理人员以及模拟人类互动的虚拟代理之间的互动。CogniChallenge 包含三个基于日常生活活动的游戏，并结合了多代理游戏平衡系统。未来的研究工作将侧重于评估 CogniChallenge 在医护人员和有 ABI 的个人中的可用性和游戏体验。通过提出这一创新平台，我们打算为扩大严肃游戏的应用及其解决认知康复这一特定领域的问题和局限性的潜力做出贡献。
{"title":"CogniChallenge: Multiplayer serious games' platform for cognitive and psychosocial rehabilitation","authors":"Eliana Silva, Ricardo Lopes, Luís Paulo Reis","doi":"10.17083/ijsg.v10i4.646","DOIUrl":"https://doi.org/10.17083/ijsg.v10i4.646","url":null,"abstract":"Information and communication technologies, such as serious games, have contributed to addressing the gaps in cognitive rehabilitation for individuals with acquired brain injury (ABI), particularly in the context of the COVID-19 pandemic. Although there are effective software programs and games available for cognitive rehabilitation, they have certain limitations. Most current programs have difficulties to adapt to individual performance, a critical factor in promoting neuroplasticity. Additionally, these programs typically only offer single-player modes. However, patients experience difficulties in social interactions leading to social isolation. To overcome these limitations, we propose a novel platform called CogniChallenge. It introduces multiplayer serious games designed for cognitive and psychosocial rehabilitation, offering competitive and cooperative game modes. This platform facilitates engagement with other patients, family members, caregivers, and virtual agents that simulate human interaction. CogniChallenge consists of three games based on activities of daily life and incorporates a multi-agent game balance system. Future research endeavors will focus on evaluating the usability and gameplay experience of CogniChallenge among healthcare professionals and individuals with ABI. By proposing this innovative platform, we intend to contribute to expanding the application of serious games and their potential to solve problems and limitations in the specific field of cognitive rehabilitation.","PeriodicalId":196187,"journal":{"name":"Int. J. Serious Games","volume":"14 1","pages":"3-16"},"PeriodicalIF":0.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139236900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The future of video games lies in Affective Games, as the future of learning lies in Affective Learning that due to the proven role of human emotions in how we think and behave. The main goals behind studying emotions in all areas other than psychology and neuroscience are to maximize beneficial emotions, reduce detrimental ones and to develop adaptive systems. The combination of this concept and recent progress in technology has spurred researchers across various fields to focus on incorporating an emotional aspect into human-computer interaction. In Serious Games context, learner-player can experience two types of emotions, the first is related to the learning elements and the second is referred to video games elements. In both learning and video games contexts, recent studies have introduced affective models to conceptualize the influence of emotions within these spheres. However, a discernible trend emerges in the realm of Serious Games research. Notably, the majority of recent studies within this domain exhibit a notable inclination towards prioritizing aspects such as personalized emotion recognition, adaptive gameplay, real-time feedback mechanisms, emotion regulation training, immersive experiences via mixed reality, longitudinal studies, and collaborative cross-disciplinary initiatives. As a result, the nuanced pursuit of affective modeling for learners-players often experiences relegation, yielding precedence to these innovative trajectories. Within this conceptual framework, this article endeavors to examine the interconnection between the emotional experiences of learner-players and the educational and interactive components inherent in serious games.
{"title":"A taxonomy of learner-player's emotions in serious games","authors":"A. Hamrouni, Fatima Bendella","doi":"10.17083/ijsg.v10i4.637","DOIUrl":"https://doi.org/10.17083/ijsg.v10i4.637","url":null,"abstract":"The future of video games lies in Affective Games, as the future of learning lies in Affective Learning that due to the proven role of human emotions in how we think and behave. The main goals behind studying emotions in all areas other than psychology and neuroscience are to maximize beneficial emotions, reduce detrimental ones and to develop adaptive systems. The combination of this concept and recent progress in technology has spurred researchers across various fields to focus on incorporating an emotional aspect into human-computer interaction. In Serious Games context, learner-player can experience two types of emotions, the first is related to the learning elements and the second is referred to video games elements. In both learning and video games contexts, recent studies have introduced affective models to conceptualize the influence of emotions within these spheres. However, a discernible trend emerges in the realm of Serious Games research. Notably, the majority of recent studies within this domain exhibit a notable inclination towards prioritizing aspects such as personalized emotion recognition, adaptive gameplay, real-time feedback mechanisms, emotion regulation training, immersive experiences via mixed reality, longitudinal studies, and collaborative cross-disciplinary initiatives. As a result, the nuanced pursuit of affective modeling for learners-players often experiences relegation, yielding precedence to these innovative trajectories. Within this conceptual framework, this article endeavors to examine the interconnection between the emotional experiences of learner-players and the educational and interactive components inherent in serious games.","PeriodicalId":196187,"journal":{"name":"Int. J. Serious Games","volume":"197 3","pages":"17-32"},"PeriodicalIF":0.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139237324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Luca Forneris, Alessandro Pighetti, Luca Lazzaroni, Francesco Bellotti, Alessio Capello, M. Cossu, Riccardo Berta
We propose a new, hierarchical architecture for behavioral planning of vehicle models usable as realistic non-player vehicles in serious games related to traffic and driving. These agents, trained with deep reinforcement learning (DRL), decide their motion by taking high-level decisions, such as “keep lane”, “overtake” and “go to rightmost lane”. This is similar to a driver’s high-level reasoning and takes into account the availability of advanced driving assistance systems (ADAS) in current vehicles. Compared to a low-level decision making system, our model performs better both in terms of safety and speed. As a significant advantage, the proposed approach allows to reduce the number of training steps by more than one order of magnitude. This makes the development of new models much more efficient, which is key for implementing vehicles featuring different driving styles. We also demonstrate that, by simply tweaking the reinforcement learning (RL) reward function, it is possible to train agents characterized by different driving behaviors. We also employed the continual learning technique, starting the training procedure of a more specialized agent from a base model. This allowed significantly to reduce the number of training steps while keeping similar vehicular performance figures. However, the characteristics of the specialized agents are deeply influenced by the characteristics of the baseline agent.
{"title":"Implementing Deep Reinforcement Learning (DRL)-based Driving Styles for Non-Player Vehicles","authors":"Luca Forneris, Alessandro Pighetti, Luca Lazzaroni, Francesco Bellotti, Alessio Capello, M. Cossu, Riccardo Berta","doi":"10.17083/ijsg.v10i4.638","DOIUrl":"https://doi.org/10.17083/ijsg.v10i4.638","url":null,"abstract":"We propose a new, hierarchical architecture for behavioral planning of vehicle models usable as realistic non-player vehicles in serious games related to traffic and driving. These agents, trained with deep reinforcement learning (DRL), decide their motion by taking high-level decisions, such as “keep lane”, “overtake” and “go to rightmost lane”. This is similar to a driver’s high-level reasoning and takes into account the availability of advanced driving assistance systems (ADAS) in current vehicles. Compared to a low-level decision making system, our model performs better both in terms of safety and speed. As a significant advantage, the proposed approach allows to reduce the number of training steps by more than one order of magnitude. This makes the development of new models much more efficient, which is key for implementing vehicles featuring different driving styles. We also demonstrate that, by simply tweaking the reinforcement learning (RL) reward function, it is possible to train agents characterized by different driving behaviors. We also employed the continual learning technique, starting the training procedure of a more specialized agent from a base model. This allowed significantly to reduce the number of training steps while keeping similar vehicular performance figures. However, the characteristics of the specialized agents are deeply influenced by the characteristics of the baseline agent.","PeriodicalId":196187,"journal":{"name":"Int. J. Serious Games","volume":"38 1","pages":"153-170"},"PeriodicalIF":0.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139236544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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