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本文对大学物理在线教学的设计进行了探索和实践,我校的大学物理在线课程依托雨课堂、智慧树等教学平台,构建了基于自主学习、以人才培养为核心线上教学模式.通过课前预习、课中翻转、课后巩固三阶段,实现了育人同向化、教学精准化、难度阶梯化、研讨开放化、思维独立化、互动全员化的培养过程,通过平台数据、督学和学生反馈、教师互评进行反思和提升,形成基于自主学习的“一核三阶六化三省”的在线智慧教学,实践证明这种教学设计推动了将课内和课外分开的教学格局转变成依托网络信息技术手段的课堂内外一体化建设. 相似文献
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2020年春季学期各高等院校均采用在线教学保证停学不停课,在线课程的教学和考试暴露了一些新的问题,教师面临着全新的挑战.文章通过问卷调查,统计考察了学生对在线考试的看法,以及考试中作弊的预期和常见作弊方式,结合开展在线教育较早的欧美研究结果探讨了预防和减少作弊的方法和思路.提出应在激发学生学习兴趣的基础上,建立学术诚信意识,降低作弊冲动.在疫情后的教学活动中,结合线上线下教学各自的优势,改变考核与评分方式,对学生整个学习过程进行考核,全面反应学生学习成果. 相似文献
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大学物理实验作为理工科学生的一门重要基础课,对学生的动手能力、创新能力的培养起着极其重要的作用。如何有效激发学生的创新活力,是各高校大学物理教学团队一直关注的重要课题。本文介绍了通过加强演示实验环节、建立学生助课制、与相关课程融合教学等新思路,有效提高了大学物理实验课程的教学效果。 相似文献
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为了适应新冠肺炎疫情时期线上教学要求,保证大学物理课程教学的顺利实施,本学期开始我校就对特殊时期的线上教学模式进行了探索.通过调研不同教学平台线上教学特点,最终决定采用钉钉和雨课堂作为主要的授课软件,学校网络教学平台作为教学资源上传平台,保证学生有系统的线上文件库.因为雨课堂的数据记录与分析功能,雨课堂被选为单元测试及课堂测试平台.通过在各平台实施“课前预习、课中直播讨论学习、课后复习”的全过程教学模式,不仅有效的开展了线上教学,还能采集到学生的学习数据,有效掌握学生的学习情况,为大学物理线上教学提供保障. 相似文献
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以大学物理课程为例,从前期准备、教学实践、课程考核3方面进行混合式教学的探索与实践.实践表明混合式教学突破了传统课堂的时空局限,极大地扩展了传统课堂的教学内容和深度,完善了网络教学,使学生实践了真正意义上的自主学习,实现了理想的师生互动关系. 相似文献
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培养兴趣,引导创新——《信息光学》理论教学改革实践 总被引:2,自引:0,他引:2
重点研究了针对《信息光学》理论性强的课程和学生在学习过程中兴趣下降以及一知半解等问题,着重从提高学生理论学习兴趣,引导学生创新为出发点,通过改进PPT课件,吸引学生注意,采用深入浅出、举一反三、师生互动、启发式教学方法、理论与实际相结合、教学科研相结合等多种方法和手段,帮助学生建立正确的物理概念,促进学生对数学公式所代表的物理意义的理解,达到改善理论教学效果的目的。同时结合创新性实验建设,培养创新型和复合型人才。 相似文献
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基于新概念量子物理的原子物理课程改革研究 总被引:1,自引:0,他引:1
借助"渗透式"教学理念及"螺旋式"上升的学习理念,将传统原子物理课内容改变为新概念量子物理,并使之与后续理论物理中的量子力学课程平滑衔接.由实施后的效果分析可知,新课程有助于学生提高学习量子物理的兴趣,逐渐加深对量子基本理论的理解,同时学生对选用的新教材具有很好的适应性. 相似文献
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为改变当前课堂教学效果差的现状,本文提出一种适用于应用型本科院校大学物理教学的课堂教学模式.该模式以学生的学为中心,以问题为驱动,形式上借鉴了对分课堂,并利用了线上教学.教学实践表明,该模式提高了学生学习的积极性,提高了课堂教学质量. 相似文献
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Multi-label learning is dedicated to learning functions so that each sample is labeled with a true label set. With the increase of data knowledge, the feature dimensionality is increasing. However, high-dimensional information may contain noisy data, making the process of multi-label learning difficult. Feature selection is a technical approach that can effectively reduce the data dimension. In the study of feature selection, the multi-objective optimization algorithm has shown an excellent global optimization performance. The Pareto relationship can handle contradictory objectives in the multi-objective problem well. Therefore, a Shapley value-fused feature selection algorithm for multi-label learning (SHAPFS-ML) is proposed. The method takes multi-label criteria as the optimization objectives and the proposed crossover and mutation operators based on Shapley value are conducive to identifying relevant, redundant and irrelevant features. The comparison of experimental results on real-world datasets reveals that SHAPFS-ML is an effective feature selection method for multi-label classification, which can reduce the classification algorithm’s computational complexity and improve the classification accuracy. 相似文献
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Kessissoglou NJ 《The Journal of the Acoustical Society of America》2012,131(3):2510-2514
The Bachelor of Mechanical Engineering at the University of New South Wales in Sydney, Australia is a four year degree program. In their fourth and final year, students can choose from a range of technical elective courses, one of those being in acoustics. The acoustics course entitled "Fundamentals of Noise" can also be taken as a postgraduate coursework subject as part of a Masters by Coursework degree. The course covers fundamental topics in acoustics, noise, noise measurement, and noise control. This paper outlines the topics covered in the course, the range of assessment activities conducted by the students, and the use of invited industry guest speakers, all of which aim to maximize student knowledge, interest, and performance in the course. 相似文献
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Recently, deep learning (DL) has been utilized successfully in different fields, achieving remarkable results. Thus, there is a noticeable focus on DL approaches to automate software engineering (SE) tasks such as maintenance, requirement extraction, and classification. An advanced utilization of DL is the ensemble approach, which aims to reduce error rates and learning time and improve performance. In this research, three ensemble approaches were applied: accuracy as a weight ensemble, mean ensemble, and accuracy per class as a weight ensemble with a combination of four different DL models—long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), a gated recurrent unit (GRU), and a convolutional neural network (CNN)—in order to classify the software requirement (SR) specification, the binary classification of SRs into functional requirement (FRs) or non-functional requirements (NFRs), and the multi-label classification of both FRs and NFRs into further experimental classes. The models were trained and tested on the PROMISE dataset. A one-phase classification system was developed to classify SRs directly into one of the 17 multi-classes of FRs and NFRs. In addition, a two-phase classification system was developed to classify SRs first into FRs or NFRs and to pass the output to the second phase of multi-class classification to 17 classes. The experimental results demonstrated that the proposed classification systems can lead to a competitive classification performance compared to the state-of-the-art methods. The two-phase classification system proved its robustness against the one-phase classification system, as it obtained a 95.7% accuracy in the binary classification phase and a 93.4% accuracy in the second phase of NFR and FR multi-class classification. 相似文献
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针对当前高等教育界的大型开放式网络课程(MOOCs)热潮,笔者对该校部分学员做了一次问卷调查,结果显示MOOCs在中国的发展仍处于萌芽的阶段,大部分学员对MOOCs知之甚少,但绝大多数学员对MOOCs这一新兴的教学模式很感兴趣,表示愿意尝试.大部分学习者表示选择MOOCs教学方式,更愿意在学校进行学习;MOOCs最大的优势是灵活、自由的学习方式.针对此调查,笔者对MOOCs的发展提出了4点建议,即制作MOOCs不必一拥而上、MOOCs制作要具有针对性、MOOCs的证书要与课程学分挂钩、推行"混合式"授课模式. 相似文献