“娱乐性”——动画艺术本质解读

动画通过夸张、风趣幽默的表现手法打破现实世界的既定框架,契合了人们对精神解放、自由的追求,表达了人们对美好生活的向往。娱乐性是动画作品诞生的原动力,是动画片尤其是商业动画片存在、成功的本质因素。我们应该颠覆"寓教于乐"中"乐"为途径"教"为目的旧观念,卸下中国动画背负了多年的沉重的教化功能这个包袱,轻松上路,让"娱乐"带领中国动画走得更远更好。     

Providing effective feedback, monitoring and evaluation to on-line collaborative learning discussions

Learning and knowledge building have become critical competences for people in the knowledge society era. In this paper, we propose a sociolinguistic dialogue model for understanding how learning evolves and how cognitive process is constructed in on-line discussions. The knowledge extracted from this model is used to assess participation behavior, knowledge building and performance. The ultimate purpose is to provide effective feedback, evaluation and monitoring to the discussion process. Seven hundred students from the Open University of Catalonia in Spain participated in this study. Results showed that learning and knowledge building may be greatly enhanced by presenting selected knowledge to learners as for their particular skills exhibited during interaction. In addition, this valuable provision of information is used as a meta cognitive tool for tutors and moderators for monitoring and evaluating the discussion process more conveniently. This contribution presents our conceptual model for interaction management as well as key design guidelines and evaluation results. Implications of this study are remarked and further research directions are proposed.     

教育机器人仿真系统中的声音设计

现有教育机器人仿真系统在进行物理仿真时缺少对声音的支持,为弥补这一缺点,有必要设计一种声音系统,使仿真更加逼真。分析仿真过程对声音的需求状况,并结合FMOD声音引擎的特点,设计系统的实现流程,讨论其中的关键问题及解决方法。测试结果显示,该系统可以实现多种音效,能够满足教育机器人物理仿真对声音的需求,对多声源能够进行较好的管理,并且有效地节约计算机资源。     

新的鲁棒模糊C-均值聚类分割算法及其应用

针对Krinidis和公茂果等提出的系列鲁棒模糊局部C-均值聚类算法存在聚类中心迭代公式缺乏严格数学理论基础的不足,于是将其聚类目标函数及其约束条件采用拉格朗日乘子法进行严格数学推导,从而获得最优解逼近的隶属度和聚类中心迭代表达式,并通过多次循环迭代实现图像聚类分割。实验结果表明,本文所建议的鲁棒模糊局部C-均值聚类分割算法是有效的,相比现有鲁棒模糊局部C-均值聚类分割算法更适合复杂遥感等图像的分割需要。     

Ordinal regression by a gravitational model in the field of educational data mining

Educational data mining (EDM) is a research area where the goal is to develop data mining methods to examine data critically from educational environments. Traditionally, EDM has addressed the following problems: clustering, classification, regression, anomaly detection and association rule mining. In this paper, the ordinal regression (OR) paradigm, is introduced in the field of EDM. The goal of OR problems is the classification of items in an ordinal scale. For instance, the prediction of students' performance in categories (where the different grades could be ordered according to A ? B ? C ? D) is a classical example of an OR problem. The EDM community has not yet explored this paradigm (despite the importance of these problems in the field of EDM). Furthermore, an amenable and interpretable OR model based on the concept of gravitation is proposed. The model is an extension of a recently proposed gravitational model that tackles imbalanced nominal classification problems. The model is carefully adapted to the ordinal scenario and validated with four EDM datasets. The results obtained were compared with state‐of‐the‐art OR algorithms and nominal classification ones. The proposed models can be used to better understand the learning–teaching process in higher education environments.     

改进权值非局部均值超声图像去噪

目的 超声图像斑点噪声会影响诊断的准确性和可靠性。通过分析超声图像斑点噪声统计模型,结合非局部均值滤波算法,提出一种基于超声斑点噪声模型的改进权值非局部均值（NLM）滤波算法。方法 算法针对超声图像灰度信息对图像进行预处理,利用超声图像斑点噪声模型改进传统NLM算法的权值计算函数,基于图像特征确定最优采样间隔进行下采样,利用改进后的权值计算函数对图像进行NLM去噪处理。结果 分别采用人工合成与真实超声图像对本文算法性能进行测试,并与传统非局部均值滤波算法、非局部总变分（NLTV）等算法进行去噪效果比较,同时采用均方误差、峰值信噪比和平均结构相似性作为滤波算法性能的客观评价指标。本文算法能快速完成超声图像的去噪处理,峰值信噪比较其他算法可以提高0.2 dB以上,可以降低均方误差,提高平均结构相似性,缩短处理时间,并得到较好的图像质量和视觉效果。结论 根据超声图像斑点噪声模型对NLM算法的权值计算函数进行优化,使得NLM图像滤波算法能更好地适用于超声图像的去噪,基于超声斑点噪声模型的改进权值NLM算法相较于其他算法,滤波效果更佳,适合超声图像去噪。     

现代教育技术条件下教师角色转变的策略

现代教育技术条件下,教学内容,教学目标和教学方法及师生关系等都发生了全新的变化。教学模式从原来的单一模式向着多元化发展,要求教师由“经验型”向“研究型”过渡,探讨网络环境下教师角色的转变有着一定的现实意义。教师作为知识源等辅助性的外围作用在很大程度上为各种媒体技术所替代,教师工作中那些简单、机械性的劳动将更多地由计算机等媒体来完成。因此,教师角色向多重性演变。     

Mentor modeling: the internalization of modeled professional thinking in an epistemic game

Players of epistemic games – computer games that simulate professional practica – have been shown to develop epistemic frames: a profession's particular way of seeing and solving problems. This study examined the interactions between players and mentors in one epistemic game, Urban Science. Using a new method called epistemic network analysis, we explored how players develop epistemic frames through playing the game. Our results show that players imitate and internalize the professional way of thinking that the mentors model, suggesting that mentors can effectively model epistemic frames, and that epistemic network analysis is a useful way to chart the development of learning through mentoring relationships.     

Technological determinism in educational technology research: some alternative ways of thinking about the relationship between learning and technology

This paper argues that research on the educational uses of technology frequently overemphasizes the influence of technology. Research in the field is considered a form of critical perspective, and assumptions about technology are questioned. Technological determinism is introduced, and different positions on this concept are identified. These are used to discuss the ways in which work within the field might be described as technologically deterministic. Four theoretical perspectives (activity theory, communities of practice, actor–network theory, and the social construction of technology) are then briefly characterized, demonstrating that alternative positions are viable, and positioning each in relation to the earlier discussion of technological determinism. The paper concludes by arguing that research, building on such alternative conceptions of technology, is important in developing our understanding of the relationship between technology and learning, as well as identifying potential methodological implications.