The development of situational interest during a digital mathematics game

The present study focused on 212 fifth graders' situational interest trajectories during an intervention with a digital mathematics game called Number Navigation. Our aims were to explore the development of situational interest whilst playing the game and to investigate the relationship between situational interest and individual math interest. Growth mixture model analyses showed that in the whole sample situational interest was stable within sessions but decreased across sessions. Three different situational interest trajectories were found. Situational interest trajectories were predicted by pre‐test individual interest. In turn, situational interest had an effect on post‐test individual interest. Students whose situational interest trajectories were stable (either high or low) presented no changes in individual interest, yet the individual interest of students whose situational interest was triggered but not maintained markedly decreased from pre‐test to post‐test. Results suggest that it is important to use game‐based learning not because games are believed to be “motivating”; rather, games with proven learning outcomes should be carefully selected.     

Outcomes for design and learning when teenagers with autism codesign a serious game: A pilot study

Digital educational games research tends to lack ecological validity by not adequately taking into account the views and perspectives of children and young people with autism spectrum disorders (ASD). This paper is a pilot study that explores and analyses an academic‐based educational game that was co‐designed with and for young people with ASD. The serious game aims to help the players learn Geography‐specific knowledge and integrates several strategic features so that users can collaborate together against the computer or compete against each other. The educational game was evaluated over 5 sessions by 3 peer teams from 2 different special educational institutions, involving a total of 6 students with ASD. The participants were positive about their enjoyment, motivation, and social engagement. The results showed that the players' level of competitiveness not only influenced the experience within the game but also the interaction within the peer teams. The game mechanisms did help the participants with ASD increase their knowledge in Geography content. The main conclusion is that there are considerable benefits of including children with ASD in the design process and future research should explore more fully on how their involvement can enhance curriculum‐based learning as well as social engagement within the classroom.     

Effects of anxiety levels on learning performance and gaming performance in digital game‐based learning

Anxiety plays an influential role in foreign language learning. However, a lack of attention was paid to examining the effects of anxiety levels on learning performance and gaming performance in digital game‐based learning. To this end, this study developed a game‐based English learning system and investigated how different levels of anxiety affected learners' learning performance and gaming performance. A quasi‐experiment was conducted in an elementary school. The results showed that high‐anxiety learners performed worse than low‐anxiety learners in speaking, word/sentence match, and overall learning performance. However, they performed similarly in listening performance. Moreover, the results showed that high‐ and low‐anxiety learners demonstrated a similar level of gaming performance. A subsequent analysis showed that significant correlations existed between learning performance and gaming performance for learners with high anxiety whereas such positive correlations were rarely found for learners with low anxiety, indicating that high‐anxiety learners' learning performance could be fostered by their gaming performance. The findings suggested that digital game‐based learning was particularly beneficial to high‐anxiety learners, whose gaming performance was a facilitative factor of their learning performance.     

Enhancing User Engagement through Gamification

Gamification—the use of game elements in non-game contexts—is gaining interest among researchers and practitioners. How gamification enhances user engagement, though, remains unclear, largely due to the lack of a theoretical framework. To narrow the theory gap, this study develops a theoretical model, which draws on cognitive evaluation theory to explain the effects of game dynamics on user engagement, and tests it using data collected from 164 users of a gamified information system. The results reveal that gamification enhances user engagement through the mediation of psychological needs satisfaction (autonomy, competence, and relatedness) between game dynamics and enjoyment. The results suggest that gamification should go beyond merely providing fun and enjoyment; gamification should also generate diverse game dynamics, such as rewards, competition, altruism, and self-expression in a way that helps people to satisfy their psychological needs. The study contributes to a more nuanced understanding of the success of gamification.     

Internet of Things-based student performance evaluation framework

In recent years, solutions based on Internet of Things (IoT) are gaining impetus in educational institutions. It is observed that student performance evaluation system in education institutions is still manual. The performance score of student in traditional evaluation system is confined to its academic achievements while activity-based performance attributes are overlooked. Moreover, the traditional system fails to capitalise information of each student related to different activities in learning environment. In relation to this context, we propose to facilitate automated student performance evaluation system by exploring ubiquitous sensing capabilities of IoT. The system deduces important results about the performance of the students by discovering daily spatial–temporal patterns. These patterns are based on the data collected by the sensory nodes (objects) in the institution learning environment. The information is generated by applying data mining algorithms for each concerned activity. The automated decisions are taken by management authority for each student using game theory. In addition, to effectively manage IoT-based activity data, tensor-based storage mechanism is proposed. The experimental evaluation compares the student performance score generated by the proposed system with the manual student performance evaluation system. The results depict that the proposed system evaluates the performance of the student efficiently.     

Trigonometric transform splitting methods for real symmetric Toeplitz systems

In this paper we study efficient iterative methods for real symmetric Toeplitz systems based on the trigonometric transformation splitting (TTS) of the real symmetric Toeplitz matrix $A$. Theoretical analyses show that if the generating function $f$ of the $n\times n$ Toeplitz matrix $A$ is a real positive even function, then the TTS iterative methods converge to the unique solution of the linear system of equations for sufficient large $n$. Moreover, we derive an upper bound of the contraction factor of the TTS iteration which is dependent solely on the spectra of the two TTS matrices involved.Different from the CSCS iterative method in Ng (2003) in which all operations counts concern complex operations when the DFTs are employed, even if the Toeplitz matrix $A$ is real and symmetric, our method only involves real arithmetics when the DCTs and DSTs are used. The numerical experiments show that our method works better than CSCS iterative method and much better than the positive definite and skew-symmetric splitting (PSS) iterative method in Bai et al. (2005) and the symmetric Gauss–Seidel (SGS) iterative method.     

WSN中基于博弈论的节点功率控制算法

针对如何降低无线传感器网络中节点传输的能量消耗,延长网络生存周期,本文将博弈论引入WSN功率控制中,综合考虑节点的剩余能量、发射功率等因素构建效用函数,将剩余能量较大的节点作为下一跳节点,连接其他节点并承载较多的传输任务.节点在通信过程中以功率博弈算法作为策略方案,不断调整各自的策略,提高信息传输的准确率,并证明该算法纳什均衡的存在.仿真结果表明,与传统分布式功率控制算法相比,该算法通过控制节点传输功率实现了降低能耗的目的,能更快迭代出最优发射功率,并得到较大的信干比.     

电力系统控制与决策中的博弈问题 ――工程博弈论初探

由于在处理多主体、多目标与复杂决策时序等方面具有的独特优势,博弈论从其诞生至今半个多世纪以来,已经对经济学、社会学、军事学、政治学以及工程科学等诸多领域产生深远的影响,成为分析研究策略相互作用的主流理论和控制与决策领域不可或缺的辅助设计工具.由于存在多个决策主体以及竞争与合作等多种利益关系,工程系统优化决策问题往往可归结为某种特殊形式的博弈问题.本文从电力系统中的控制与决策问题出发,阐述了工程博弈论的建模思想和主要方法,并以笔者团队近年来在相关领域的科研工作为主线,对工程博弈论在电力系统中的应用进行总结与展望.     

基于合并与分裂机制的云任务联盟博弈调度

为降低任务执行代价,提高云资源提供者收益,提出一种云计算环境下基于合并与分裂机制的任务调度联盟博弈优化算法。建立基于资源联盟的任务执行模型,定义资源提供者选择建立联盟的依据;给出联盟完成云任务后总收益在其联盟成员间的分配方式,重点阐述如何通过联盟合并与分裂机制产生稳定的联盟结构,分析结盟算法的性能。通过仿真实验,与同类型算法进行性能比较,比较结果表明,联盟博弈算法可以产生稳定的联盟结构,为联盟中的个体成员带来最大化收益。     

基于进化博弈论的工业无线传感网能耗攻防研究

无线传感器节点由于部署、维护简单易行被广泛应用在工业网络中,并形成了工业无线传感器网络(Industrial Wireless Sensor Network,IWSN).针对在工业控制网络中的能量耗尽攻击的内部机理进行了详尽的分析,提出了基于进化博弈理论的能量耗尽攻防模型,将各个博弈方的收益函数定义为信誉值的函数,从而通过收益函数来约束博弈方的行为,并在理论上证明了该博弈的进化稳定策略的存在性.选取了策略选择概率作为算法的评价指标,设计并实施了大量仿真实验对算法进行验证.实验结果表明无论博弈双方的初始策略集概率分布如何,最终能达到有效防止能耗攻击的目的.