人工智能时代的课堂教学革新

人工智能时代的课堂教学革新要更加重视完整理解"学习"的含义,确保通过课堂教学促使学习完整地发生,教师要做好选择、组织和整合这三项工作.要理直气壮地提出"学教统一",调动师生两个方面的积极性.教学的空间和时间会有较大的变化,将更加适合学生个性化和差异化发展的需要.课程内容也将实现翻转,在学习任务的类别上,要重视"为什么"...     

浅议图书馆服务的创新与提高

计算机技术和网络技术的飞速发展,给传统图书馆的生存带来冲击.图书馆想要拥有更多的读者,就必须采用先进的装备和技术,用先进的理念进行管理,用创新的意识提高服务质量.     

A rule induction framework for the determination of representative learning design in skilled performance

Representative learning design provides a framework for the extent to which practice simulates key elements of a performance setting. Improving both the measurement and analysis of representative learning design would allow for the refinement of sports training environments that seek to replicate competition conditions and provide additional context to the evaluation of athlete performance. Using rule induction, this study aimed to develop working models for the determination of high frequency, representative events in Australian Rules football kicking. A sample of 9005 kicks from the 2015 Australian Football League season were categorised and analysed according to the following constraints: type of pressure, kick distance, possession source, time in possession, velocity and kick target. The Apriori algorithm was used to develop two models. The first consisted of 10 rules containing the most commonly occurring constraint sets occurring during the kick in AF, with support values ranging from 0.15 to 0.22. None of the rules contained more than three constraints and confidence values ranged from 0.63 to 0.84. The second model considered ineffective and effective kick outcomes and displayed 70% classification accuracy. This research provides a measurement approach to determine the degree of representativeness of sports practice and is directly applicable to various team sports.     

Transforming in the visiting assistant professor role: collaborative self-study research in physical education teacher education

Transitioning into faculty roles can be a challenging experience. Responsibilities related to teaching, research, and service can be difficult to balance, and mismatch between personal preferences for work focus and instructional expectations can be problematic. This is particularly true of visiting assistant professors who want to work toward tenure-track positions, but whose current job focus is on teaching. Through transformative learning theory and employing the methodologies of self-study, we sought to understand the experiences of Kevin, the second author and a first-year visiting assistant professor. We draw data from Kevin's reflective journal and discussions between Kevin and his critical friend, Jim. Themes included (a) becoming an academic, (b) a hard-wired disposition toward work, and (c) influence of the VAP on emotional and personal health. Results are discussed through the lens of transformative learning theory and focus on Kevin's growth during his year as a visiting assistant professor.     

我国校园足球可持续发展的若干问题研究

发展校园足球是推进我国校园体育活动的重要力量,亦是提升我国足球运动水平的必然选择。虽然在相关政策的支持下,我国校园足球发展迅速,但依然面临经费短缺、师资不足、发展不均衡、学训矛盾突出、体制机制不完善、社会认可度不高、医疗保障体系不健全等问题。指出,学校应密切联系俱乐部、企业,多渠道获取发展资源;从内部培养与外部引进两方面充实师资力量;坚持全面发展原则,妥善解决学训矛盾,提升学生综合素质;加强足球文化建设,营造良好氛围,提升足球参与者的荣誉感;强化校园足球风险防范意识,加强风险管理。旨在促进我国校园足球健康可持续发展。     

足球踢球动作技能学习中背景干扰效应的实验研究

目的：探讨初学者在足球踢球动作技能学习过程中是否存在背景干扰效应,并分析原因。方法：以足球脚背内侧踢定位球动作技能作为学习任务,选取某大学足球专修班的33名学生为研究对象,分成对照组和两个实验组,进行10周的体育教学现场实验,对一周后保持测试的达标和技评成绩进行单因素方差分析。结果：三组保持测试的达标和技评成绩均值,在0.05水平上差异不具有显著性。结论：实验结果未能出现背景干扰效应,未能验证本研究提出的研究假设和背景干扰效应假说的最主要原因是,足球脚背内侧踢定位球的复杂性任务特征。     

基于多状态跳转模型的场景独立音频事件检测方法

针对不同类型事件设计多状态跳转模型,结合两种深度神经网络实现对传统音频事件检测框架的改进。实验表明,在DCASE2017任务2的开发集数据上,改进后的DNN-HMM系统相比于基线系统取得F值8.9%的相对提升和错误率19%的绝对下降;基于多状态跳转模型聚类的卷积神经网络模型（SC-CNN）,相比于基线系统取得F值18%的相对提升和错误率30%的绝对下降。     

偏远地区高校学生大学英语自主学习能力培养探究

大学英语自主学习是大学生英语学习的一种很有效的学习方式,而自主学习能力的培养是大学英语教学的培养目标之一,它对缓解大学英语教学的诸多矛盾起到重要作用。以自主学习理论为指导,立足偏远地区高校的实际情况,探究偏远地区高校学生大学英语自主学习能力培养的可行性,对提高该类高校学生大学英语学习成绩将起到促进作用。     

学术文本词汇功能识别 ——在关键词自动抽取中的应用

传统的关键词自动抽取常以候选词的出现频次、位置等非语义信息构建特征,并未考虑关键词在学术文献中承担的特定语义角色,即词汇功能。通过对现有数据统计,本文发现作者标注关键词中约有67.99%是研究问题或研究方法词。因此,本文将关键词的词汇功能分为三类:“研究问题”“研究方法”和“其他”,在传统的词频特征以及位置特征基础上,融合词汇功能特征,使用计算机领域的学术文献基于分类和排序两种思想进行关键词抽取实验。实验结果表明,融合词汇功能后,关键词抽取效果得到明显提升。相较于基准实验,二分类模型的准确率Acc和F值分别相对提升24.63%和25.19%,达到了0.840和0.666;排序模型的MAP、NDCG@5和P@5分别相对提升168.32%、189.50%和148.30%,提升至0.813、0.828和0.447,证明了学术文献词汇功能特征在关键词自动抽取中具有重要作用。     

基于表示学习的双层知识网络链路预测

当前,针对知识网络的链路预测主要是基于网络拓扑结构的相似性,很少考虑作者的研究领域,导致信息利用不充分等问题,因此本文提出了双层知识网络的链路预测框架hypernet2vec。双层知识网络,即作者合著关系网络和学术领域关系网络,利用网络表示学习,分别将两层网络中的节点映射到低维的向量空间,再输入到专门设计的卷积神经网络中计算并进行链路预测。与经典的链路预测指标如RA指标、LP指标和LRW指标等相比,hypernet2vec模型预测的AUC(area under curve)值取得了显著的提升,平均提升幅度达11.17%。文章还从情报产生层面和复杂系统层面,对模型发生作用的深层机理进行了探讨。