计算机网络课程的自顶向下教学

为了更加符合人们的自然思维习惯,打破传统的学习顺序,提出计算机网络课程不再按照体系结构由下而上进行教学,而是采用自顶向下的教学方式,并从概述到每一层的教学设计都进行具体阐述和说明。

关于硕士课程教学咨询的经验（英文）

教学咨询是一项高层次的教学任务,需要充分的准备,而软件工程教育专业化需要涉及这样的高级主题。本文提供了为期三年的硕士课程教学咨询的教学结果,并提出一些见解来帮助深化高校专业课程教学专业化水平。

2007年全国独立学院计算机教育论文征稿启事

香农:信息论及数字通信之父

基于CIPP的计算机组成原理翻转课堂教学评价指标体系构建与应用

针对基于SPOC的计算机组成原理翻转课堂传统教学评价中存在的问题，提出基于CIPP的计算机组成原理课程多元评价指标体系，介绍评价指标体系的构建模型与结构，通过评价应用结果分析，给出翻转课堂教学模式的改进策略。

The MOOC/SPOC Based “1+M+N” Multi-University Collaborative Teaching and Learning Mode:Practice and Experience

Since 2012, the MOOCs, the massive open online courses, have brought big influences on the higher education in the world. How to use MOOCs to help universities rather than bother them to improve their education level and quality becomes an important issue. In China, many universities have explored the new modes and approaches for MOOC/SPOC-based teaching and learning. Especially, the China MOOC Association on Computing Education(CMOOC association), established in 2014, has done a set of successful practice and achieved fruitful experiences on MOOC courses development and computer education reform. Based on the practical experiences, a MOOC/SPOC based "1+M+N" multi-university collaborative teaching and learning mode is presented, which is adapted to the real situation of Chinese university education. In the paper, the practices and experiences of CMOOC association are introduced, the MOOC/SPOC based "1+M+N" multi-university collaborative teaching and learning mode and its approaches are described. Finally, the suggestions for MOOCs development and applications are also presented.

教育领域反馈文本情感分析方法及应用研究

对教育领域反馈文本情感分析的作用进行分析,并对相关研究现状进行介绍,以中国大学MOOC评论文本数据集为基础,介绍基于裁切语言模型与注意力机制的情感分析方法及在北京航空航天大学计算机导论与伦理学国家精品课线下教学课堂中的应用情况,验证线上教育领域情感分析技术迁移至线下教育领域的可行性,为情感分析技术在线下教育领域的应用提供实践参考。

创新是中国高校计算机教育的基石

简要回顾中国计算机教育界创新教学计划(教程)的概况,介绍全国高等学校计算机教育研究会的创新工作;提出创新中国高校计算机教育的个人设想,最后展望创新前景。

Towards Sensor-free Academic Emotion Prediction in Programming Environment

The transition from traditional learning to practice-oriented programming learning will bring learners discomfort. The discomfort quickly breeds negative emotions when encountering programming difficulties, which leads the learner to lose interest in programming or even give up. Emotion plays a crucial role in learning. Educational psychology research shows that positive emotion can promote learning performance, increase learning interest and cultivate creative thinking. Accurate recognition and interpretation of programming learners' emotions can give them feedback in time, and adjust teaching strategies accurately and individually, which is of considerable significance to improve effects of programming learning and education. The existing methods of sensor-free emotion prediction include emotion prediction based on keyboard dynamic, mouse interaction data and interaction logs, respectively. However, none of the three studies considered the temporal characteristics of emotion, resulting in low recognition accuracy. For the first time, this paper proposes an emotion prediction model based on time series and context information. Then, we establish a Bi-recurrent neural network, obtain the time sequence characteristics of data automatically, and explore the application of deep learning in the field of Academic Emotion prediction. The results show that the classification ability of this model is much better than that of the original LSTM(Long-Short Term Memory), GRU(Gate Recurrent Unit) and RNN(Re-current Neural Network), and this model has better generalization ability.

“Visual Basic程序设计”课堂教学模式改革

本文分析了"Visual Basic程序设计课程"的教学模式改革实践,探讨了直观教学法、推理教学法、演示教学法、案例教学法在教学中的应用。实践证明,这些方法对于提高教学质量起到了积极的作用。