在线学习者社会临场感自动编码的最优路径探索  

The Optimal Path Exploration of Online Learner’s Social Presence Automatic Coding

作　　者：吴怡君 冯晓英[1] WU Yijun;FENG Xiaoying(Key Laboratory of Learning Design and Analysis,Beijing Normal University,Beijing 100875,China)

机构地区：[1]北京师范大学学习设计与学习分析重点实验室,北京100875

出　　处：《开放学习研究》2020年第6期7-13,45,共8页Journal of Open Learning

摘　　要：随着在线学习、混合式学习的普及,为学习者提供更加及时的学习支持,就需要准确地判断在线学习者的学习状态,探究社区理论中的临场感水平是一个好的衡量指标。本研究旨在用自然语言处理的思路探索临场感自动编码的最优路径。研究以社会临场感为例,应用中文自然语言处理中文本分类问题的机器学习算法,探索了六条不同的临场感自动编码的路径。通过对不同路径进行比较,选取其中的最优路径,构建了在线学习者社会临场感编码模型组。经验证,该模型组能够较好地测量社会临场感水平,帮助教师准确判断学习者的学习状态,更好地开展在线教学。With the increasing popularity of online learning and hybrid learning,how to accurately judge the learning status of online learners is becoming more and more important.The level of presence in the community of Inquiry theory is a good measure to judge.This study aimed to explore the optimal path of automatic coding of presence by using the idea of natural language processing.Taking the social presence as an example,this study applied the machine learning algorithm in classification problem of Chinese natural language processing,and explored six different automatic coding paths of presence.By comparing different paths,the optimal path was selected and the online learner social presence coding model group was constructed.It has been verified that the model group can better measure the level of social presence and further help teachers accurately judge the learner’s learning state to improve online teaching.

关 键 词：在线学习 临场感 中文自然语言处理 最优路径 

分 类 号：G43[文化科学—教育学]