作者机构：
[Zhou, Jin] Cent China Normal Univ CCNU, Sch Educ Informat Technol, Wuhan, Peoples R China.;[Ye, Jun-min] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.
通讯机构：
[Zhou, Jin] C;Cent China Normal Univ CCNU, Sch Educ Informat Technol, Wuhan, Peoples R China.
作者机构：
[Luo Da-Xiong; Shu, Chen; Song, Xu; Min, Ye Jun] Cent China Normal Univ, Sch Comp, Wuhan 430070, Hubei, Peoples R China.;[Feng, Wang Zhi] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan 430070, Hubei, Peoples R China.
通讯机构：
[Song, Xu] C;Cent China Normal Univ, Sch Comp, Wuhan 430070, Hubei, Peoples R China.
会议名称：
9th International Conference of Information and Communication Technology [ICICT]
会议时间：
JAN 11-13, 2019
会议地点：
Nanning, PEOPLES R CHINA
会议主办单位：
[Song, Xu;Min, Ye Jun;Luo Da-Xiong;Shu, Chen] Cent China Normal Univ, Sch Comp, Wuhan 430070, Hubei, Peoples R China.^[Feng, Wang Zhi] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan 430070, Hubei, Peoples R China.
摘要：
The short text in the online learning community is an important source of data in learning analysis. Therefore, the quality of the short text has a significant impact on the study of learning analysis. Due to the large amount of text data in the learning community, manual detection and repair will cost too much. This paper proposes a text detection and repair framework based on an online learning community. It aims to automatically detect and repair various types of semantic errors and grammatical errors that exist in online learning community short texts. The framework utilizes existing text error detection and repair algorithms and integrates them effectively to form a comprehensive detection and repair algorithm. In this paper, the validity of the framework is verified through experiments on the constructed data set. The experimental results show that the framework has high accuracy in automatically detecting and repairing text errors. (C) 2019 The Authors. Published by Elsevier Ltd.
摘要：
对话流所隐含的信息包括了学习者对所学课程内容的掌握程度和关注点,分析这些对话流对预测学习者的成绩,以支持教师提前对潜在成绩不良的学生进行及时干预有着重要意义.提出了一种基于对话流的学习者成绩等级预测算法ARPDF(Achievement Rank Prediction based on Dialogue Flow),首先采集对话流,通过对话流划分、对话状态矩阵生成实现了对该对话流的分析以获取到学习小组的对话状态矩阵;在此基础上,通过基于LSTM的预测模型获得学习小组学习者的成绩等级.在本文所提方法的基础上进行了实验,其结果表明了该算法是有效的.
作者机构：
[Ye Jun-min; Xu Song; Luo Da-Xiong; Huang Peng-Wei; Xu Chen] School of Computer, Central China Normal University, Wu Han, 430000, China;[Wang Zhi-Feng] School of Educational Information Technology, Central China Normal University, Wu Han, 430000, China
通讯机构：
School of Computer, Central China Normal University, Wu Han, China
摘要：
Aiming at the problem of "learning defiance" and "information overload" brought by educating big data to learners, this paper proposes an online learning community personalized learning path recommendation algorithm based on ant colony algorithm: in terms of computing pheromone, it combines individuality. Based on the characteristics of the learning path, a learning path scoring method based on multi-factor fuzzy evaluation is proposed to quantify the learning path evaluation as a score to solve the problem that it is difficult for the subjective score to accurately represent the pheromone concentration; in terms of pheromone updating rules, The introduction of pheromone restriction intervals avoids the problems associated with excessive or small learning path pheromone concentration in global updating; in the calculation of the selection probability of local search, the positive and negative feedback effects of pheromones can be better used. Search for a local optimal solution. The related experiments show that this algorithm can effectively solve the recommendation of the personalized learning path of the online learning community.
摘要：
In the online learning environment, identifying learners' behaviors in the learning process can help them improve their learning effect autonomously. Firstly, we use K-Means algorithm to cluster the learner's help-seeking behavior data to get the classification label of the learner's help-seeking behavior. Secondly, we use the t-distributed Stochastic Neighbor Embedding(T-sne) algorithm to reduce the dimension of the data to visualize the clustering result. Finally, the learner's help-seeking behavior data and the help-seeking behavior classification labels are used as training data to train the Naive Bayesian model so as to automatically obtain the help-seeking behavior classification for the data generated by the new learner. Via the analysis and processing of the help-seeking behavior data using the method proposed in this paper, it shows that this method can effectively find online learners' help-seeking behavior classifications.
