Assessing the effects of gamification in the classroom: A longitudinal study on intrinsic motivation,social comparison,satisfaction, effort,and academic performance

Gamification, the application of game elements to non-game settings, continues to grow in popularity as a method to increase student engagement in the classroom. We tested students across two courses, measuring their motivation, social comparison, effort, satisfaction, learner empowerment, and academic performance at four points during a 16-week semester. One course received a gamified curriculum, featuring a leaderboard and badges, whereas the other course received the same curriculum without the gamified elements. Our results found that students in the gamified course showed less motivation, satisfaction, and empowerment over time than those in the non-gamified class. The effect of course type on students' final exam scores was mediated by students' levels of intrinsic motivation, with students in the gamified course showing less motivation and lower final exam scores than the non-gamified class. This suggests that some care should be taken when applying certain gamification mechanics to educational settings.     

虚拟人运动中受扰的平衡保持算法

为了更加真实且实时地模拟运动中虚拟人恢复平衡的反应动作,提出一种针对受到外界作用力扰动的虚拟人平衡保持算法.首先通过虚拟人质心位移和速度来判断平衡性;然后借助生物力学的研究成果设计了具有人体特性的虚拟人运动受扰后的平衡保持方法,并用动力学进行模拟,驱动虚拟人完成平衡恢复.实验结果表明:该算法计算效率高,符合人体的生物力学特性,并且具有良好的交互性与较好的视觉效果,适用于虚拟人动画合成.     

Android IoT Lifelog System and Its Application to Motion Inference

In social science, health care, digital therapeutics, etc., smartphone data have played important roles to infer users’ daily lives. However, smartphone data collection systems could not be used effectively and widely because they did not exploit any Internet of Things (IoT) standards (e.g., oneM2M) and class labeling methods for machine learning (ML) services. Therefore, in this paper, we propose a novel Android IoT lifelog system complying with oneM2M standards to collect various lifelog data in smartphones and provide two manual and automated class labeling methods for inference of users’ daily lives. The proposed system consists of an Android IoT client application, an oneM2M-compliant IoT server, and an ML server whose high-level functional architecture was carefully designed to be open, accessible, and internationally recognized in accordance with the oneM2M standards. In particular, we explain implementation details of activity diagrams for the Android IoT client application, the primary component of the proposed system. Experimental results verified that this application could work with the oneM2M-compliant IoT server normally and provide corresponding class labels properly. As an application of the proposed system, we also propose motion inference based on three multi-class ML classifiers (i.e., k nearest neighbors, Naive Bayes, and support vector machine) which were created by using only motion and location data (i.e., acceleration force, gyroscope rate of rotation, and speed) and motion class labels (i.e., driving, cycling, running, walking, and stilling). When compared with confusion matrices of the ML classifiers, the k nearest neighbors classifier outperformed the other two overall. Furthermore, we evaluated its output quality by analyzing the receiver operating characteristic (ROC) curves with area under the curve (AUC) values. The AUC values of the ROC curves for all motion classes were more than 0.9, and the macro-average and micro-average ROC curves achieved very high AUC values of 0.96 and 0.99, respectively.     

基于多流网络一致性的视频显著性检测

现有的视频显著性检测算法通常采用双流结构提取视频的时空线索,其中运动信息作为双流结构的一个分支,在显著物体发生剧烈或慢速移动时存在运动估计准确率低的问题,并且不合理的训练数据或方案使得权重偏向单个分支结构。提出一种基于多流网络一致性的视频显著性检测算法MSNC。设计并使用一种新的三重网络结构提取预选目标区域的颜色信息、时序信息和先验特征,通过先验特征补偿运动流的缺陷,并提高运动线索的利用率。采用多流一致性融合模型优化三流分支,得到不同特征的最佳融合方案。同时通过循环训练策略平衡三重网络的权重,以避免网络过度拟合单流分支,从而有效地提高运动估计和定位的准确率。在Davis数据集上的实验结果表明,相比PCSA、SSAV、MGA等算法,该算法的鲁棒性更优,其maxF和S-Measure值分别达到0.893和0.912,MAE仅为0.021。     

Developments in quantitative dimensional synthesis (1970-present): four-bar motion generation

Dimensional synthesis is a type of inverse problem in linkage kinematics where the objective is to calculate the linkage dimensions required to achieve prescribed linkage output motion. Motion generation is a particular category of dimensional synthesis where the objective is to calculate the linkage dimensions required to achieve a group of prescribed link positions. In motion generation for a four-bar linkage, positions are prescribed for the coupler link. While early motion generation methods were primarily qualitative, ongoing advancements in computing hardware and software continue to make quantitative motion generation more practical. By providing overviews of works representative of developments in quantitative four-bar motion generation since 1970, this work is essentially an overview that spans over 40 years of developments in quantitative four-bar motion generation.     

Video analysis based on volumetric event detection

During the past decade, feature extraction and knowledge acquisition based on video analysis have been extensively researched and tested on many applications such as closed-circuit television(CCTV)data analysis, large-scale public event control, and other daily security monitoring and surveillance operations with various degrees of success. However, since the actual video process is a multi-phased one and encompasses extensive theories and techniques ranging from fundamental image processing, computational geometry and graphics, and machine vision, to advanced artificial intelligence, pattern analysis, and even cognitive science, there are still many important problems to resolve before it can be widely applied. Among them, video event identification and detection are two prominent ones. Comparing with the most popular frame-to-frame processing mode of most of today's approaches and systems, this project reorganizes video data as a 3D volume structure that provides the hybrid spatial and temporal information in a unified space. This paper reports an innovative technique to transform original video frames to 3D volume structures denoted by spatial and temporal features. It then highlights the volume array structure in a so-called "pre-suspicion" mechanism for a later process. The focus of this report is the development of an effective and efficient voxel-based segmentation technique suitable to the volumetric nature of video events and ready for deployment in 3D clustering operations. The paper is concluded with a performance evaluation of the devised technique and discussion on the future work for accelerating the pre-processing of the original video data.     

Stochastic differential equations driven by a Wiener process and fractional Brownian motion: Convergence in Besov space with respect to a parameter

A stochastic differential equation involving both a Wiener process and fractional Brownian motion, with nonhomogeneous coefficients and random initial condition, is considered. The coefficients and initial condition depend on a parameter. The assumptions on the coefficients and the initial condition supplying continuous dependence of the solution on a parameter, with respect to the Besov space norm, are established.     

Predictive capability of cognitive ability and cognitive style for spaceflight emergency operation performance

This study explores the effects of cognitive ability (information seeking, inference, spatial recognition, attention span, and attention allocation) and cognitive style (active-reflective, sensing-intuitive, visual-verbal, and sequential-global) on task performance of simulated spaceflight emergency operations that require judgment and operation on a Chinese spaceflight instrument board and the possible interaction effect with training experience. The performance criteria included task completion time and number of human errors. It was found that inference ability, spatial recognition ability, and attention span had significant effects on task completion time, while attention allocation ability had significant effect on the number of error. The participants with a sequential cognitive style made significantly fewer errors than those with a global cognitive style. Training experience significantly decreased task completion time. The participants with sequential cognitive style learnt faster than those with global cognitive style in the spaceflight instrument operations. With increasing training experience, the predictive capability of cognitive ability on performance decreased, whereas the predictive capability of the sequential-global cognitive style on performance increased.     

A general formulation of monotonically convergent algorithms in the control of quantum dynamics beyond the linear dipole interaction

This paper presents a general method to formulate monotonically convergent algorithms for identifying optimal control fields to manipulate quantum dynamics phenomena beyond the linear dipole interaction. The method, facilitated by a field-dependent dipole moment operator, is based on an integral equation of the first kind arising from the Heisenberg equation of motion for tracking a time-dependent, dynamical invariant observable associated with a reference control field.     

A consideration of the nature of work and the consequences for the human-oriented design of production and products

In this article, it is shown that human work can be understood as a process of creating order, and that order can be seen as a form of information. Since information can be considered as negative entropy, work is associated with energy consumption. Therefore, it is important to investigate the nature of human necessities in more detail in order to meet the desire for comfort through the efficient application of energy. Temporary increases of information cause accelerated increases in entropy. This explains the appearance of living organisms, and the historic development of increasingly complex technology. Through technical progress, repetitive human work is being replaced by automation, so that primarily creative work remains. Now the question arises of how much creative work a human can manage. In addition, one goal of automation should be the reduction of human errors, but in doing so, an optimal balance should be found between supporting the operator both during normal procedures and during unforeseen circumstances.