作者机构：
[Sun, Jianwen; Liu, Sannyuya; Liu, Zhi; Kang, Lingyun; Su, Zhu] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.;[Rudian, Sylvio] Humboldt Univ, Dept Comp Sci, Weizenbaum Inst Networked Soc, Berlin, Germany.
会议名称：
International Joint Conference on Information, Media and Engineering (ICIME)
会议时间：
DEC 12-14, 2018
会议地点：
Osaka Univ, Int Joint Lab Knowledge & Media Dynam Educ Fields, Osaka, JAPAN
会议主办单位：
Osaka Univ, Int Joint Lab Knowledge & Media Dynam Educ Fields
关键词：
social network analysis;emotion density;network structure
摘要：
In recent years, a growing number of educational researchers are keen to utilize social network analysis (SNA) and emotion detection for exploring collective learning states. Students' emotions and interaction characteristics before the exam typically suggest some significant traces of learning states. In this study, data from the discussion forum of "Chinese legal history" course in a university learning platform was used to investigate evolutionary trends of students' network characteristics and emotion densities (EDs) in the last four weeks before the final exam, as well as visualized the distribution of the high-EDs (including positivity, negativity and confusion) students in the weekly network. Empirical analyses suggested that, as the exam approaches, learners' network structure and emotional densities are constantly changing. After experiencing a smooth change in the first two weeks, the average degree centrality reached a peak in the third week, and confusion emotional density far exceeded positive and negative emotional density in the last week, which may help in identifying the potential academic losers and providing timely interventions.
期刊：
Journal of Physics: Conference Series,2018年1113(1) ISSN：1742-6588
通讯作者：
Su, Zhu(suz@mail.ccnu.edu.cn)
作者机构：
[Sun, Jianwen; Kang, Lingyun; Su, Zhu; Liu, Sannyuya; Liu, Zhi] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China;[Sun, Jianwen; Su, Zhu; Liu, Sannyuya; Liu, Zhi] National Engineering Laboratory for Technology of Big Data Applications in Education, Central China Normal University, Wuhan, China
通讯机构：
National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
作者机构：
[Sun, Jianwen; Yang, Zongkai; Cheng, Hercy N. H.; Liu, Zhi; Liu, Sanya] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Sci Hall 419,152 Luoyu Rd, Wuhan 430079, Peoples R China.
通讯机构：
[Cheng, Hercy N. H.] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Sci Hall 419,152 Luoyu Rd, Wuhan 430079, Peoples R China.
关键词：
Small private online courses;SPOC;blended learning;online-learning behaviors;sequential analysis
摘要：
Currently, with the increasing advancement of interactive learning technologies in MOOCs, a large number of student-generated comments (SGCs) have been substantially produced with two primary emotions (positive and negative). The emotional orientations are typically related with specific learning topics or aspects discussed, which is of value to offer abundant academic feedbacks for teachers and developers. Especially, the negative emotion and topics can be exploited to get an in-depth insight of the problems and barriers encountered by learners in online learning. However, it is challenging to capture relevant details from unstructured SGCs. In this paper, we propose a generative probabilistic model that extends Sentence-LDA (SLDA), namely Emotion Topic Joint Probabilistic Model (ETJM), to explore negative opinions in terms of pairs of <emotion, topic> which we call emo-topic. The model first automatically extracts the sentences with the high negative emotion density (NED), and then incorporates emotion and topic together to explore negative emotional feedbacks towards topics. The experimental results show that learners extended some negative comments towards the issues about learning content, online assignments and certificates of courses. The summarization of these issues can be given back to teachers to regulate and improve the teaching methods, strategies and design of learning contents.
作者：
Chen, Yangjun*;Liao, Calvin C. Y.;Liu, Sannyuya;Cheng, Hercy N. H.;Jia, Liansheng;...
作者机构：
[Sun, Jianwen; Jia, Liansheng; Liu, Sannyuya; Cheng, Hercy N. H.; Chen, Yangjun; Liao, Calvin C. Y.] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
会议名称：
6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)
会议时间：
JUL 09-13, 2017
会议地点：
Hamamatsu, JAPAN
会议主办单位：
[Chen, Yangjun;Liao, Calvin C. Y.;Liu, Sannyuya;Cheng, Hercy N. H.;Jia, Liansheng;Sun, Jianwen] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
关键词：
pupils' Chinese compositions;linguistic feature;stepwise multiple linear regression;Support Vector Machine
摘要：
The traditional evaluation of composition is human evaluation which is time-consuming, laborious and easily affected by subjective. In recent years, the automatic essay scoring (AES) has become a hot issue in natural language processing, but few research focus on Chinese AES. Hence, this study designed a Chinese AES system and collected 4566 compositions from first grade to sixth grade students. We also extracted 43 linguistic features based on Chinese characteristic, and analysis these compositions based on three model by stepwise multiple regression technique and support vector machine. Results showed that the accuracy of classification is among 70-80%.
作者：
Sanya Liu;Zhenfan Hu;Xian Peng;Zhi Liu;Hercy N. H. Cheng;...
期刊：
International Journal of Distance Education Technologies,2017年15(1):15-27 ISSN：1539-3100
作者机构：
[Jianwen Sun; Hercy N. H. Cheng; Zhenfan Hu; Sanya Liu; Zhi Liu; Xian Peng] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
关键词：
Behavioral Pattern;Cloud Classroom;Learning Analytics;Massive Open Online Courses;Sequential Analysis
作者：
Tian, Xinyun*;Han, Xiaoxue;Cheng, Hercy N. H.;Chang, Wang-Chen;Liao, Calvin C. Y.;...
作者机构：
[Sun, Jianwen; Han, Xiaoxue; Cheng, Hercy N. H.; Liu, Sanya; Tian, Xinyun; Zhu, Xiaoliang] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.;[Chang, Wang-Chen] Natl Cent Univ, Grad Inst Learning & Instruct, Taoyuan, Taiwan.
会议名称：
6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)
会议时间：
JUL 09-13, 2017
会议地点：
Hamamatsu, JAPAN
会议主办单位：
[Tian, Xinyun;Han, Xiaoxue;Cheng, Hercy N. H.;Sun, Jianwen;Zhu, Xiaoliang;Liu, Sanya] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.^[Chang, Wang-Chen] Natl Cent Univ, Grad Inst Learning & Instruct, Taoyuan, Taiwan.
关键词：
Reading ability;test quality;item response theory
摘要：
In order to realize the individualized teaching of Chinese language in primary schools, this research has developed an online Chinese reading assessment for primary schools, which aims to record the students' test process and analyze the development level of students' Chinese reading ability. In this paper, the item response theory (IRT) is applied to the quality analysis of the assessment in terms of measurement attributes (difficulty, discrimination, guessing), item characteristic curve, item information function and test information function. Additionally, this paper further explores the relationship between the various parameters of the items. The results show that the IRT can effectively guide the construction of the reading assessment scale, improve the discrimination degree, reduce the guessing degree, and effectively improve the quality of the item. This paper also proposes a method to find out the unreasonable options and modify items locally through project parameters and option analysis. It is expected that researchers and educators can modify the item more efficiently by quality analysis.
作者：
Jia, Liansheng*;Cheng, Hercy N. H.;Liu, Sannyuya;Chang, Wang-Chen;Chen, Yangjun;...
作者机构：
[Sun, Jianwen; Jia, Liansheng; Liu, Sannyuya; Cheng, Hercy N. H.; Chen, Yangjun] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.;[Chang, Wang-Chen] Natl Cent Univ, Grad Inst Learning & Instruct, Taoyuan, Taiwan.
会议名称：
6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)
会议时间：
JUL 09-13, 2017
会议地点：
Hamamatsu, JAPAN
会议主办单位：
[Jia, Liansheng;Cheng, Hercy N. H.;Liu, Sannyuya;Chen, Yangjun;Sun, Jianwen] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.^[Chang, Wang-Chen] Natl Cent Univ, Grad Inst Learning & Instruct, Taoyuan, Taiwan.
摘要：
It is helpful for students and teachers to identify students' reading abilities and testing strategies based on their behavioral records of reading tests in an online Chinese reading assessment system. In this study, a K-means clustering algorithm is used to divide students into three potential clusters, and the behavioral sequence diagram of each cluster is drawn by means of the lag sequential analysis. By comparing the characteristics and differences of clusters, this paper draws the following main conclusions: (1) For better reading performance, increasing the time of reading articles is more beneficial than directly searching for the answers in the articles according to questions and options; (2) Students with high reading abilities spend longer time on reading articles and inspecting items, but rarely alter options; (3) Students with low reading abilities, who spend longer testing time and have more behaviors of clicking on articles and items, are not focused enough on current questions; (4) Those students with low reading abilities, who spend shorter testing time, rarely have inspection behaviors. Finally, this paper puts forward some suggestions based on the reading ability and testing strategy of each cluster to improve students' reading literacy and instruct teacher's reading teaching activities.
期刊：
International Journal of Innovative Computing, Information and Control,2016年12(6):2099-2110 ISSN：1349-4198
通讯作者：
Liu, Sanya(lsy5918@gmail.com)
作者机构：
[Sun, Jianwen; Liu, Sanya; Liu, Zhi; Peng, Xian; Gan, Wenbin] National Engineering Research Center for E-Learning, Central China Normal University, No. 152, Luoyu Road, Wuhan, 430079, China
通讯机构：
National Engineering Research Center for E-Learning, Central China Normal University, No. 152, Luoyu Road, Wuhan, China
作者：
Cheng, Hercy N. H.*;Sun, Jian-Wen（孙建文）;Liu, Zhi;Liu, San-Ya
作者机构：
[Sun, Jian-Wen; Liu, San-Ya; Cheng, Hercy N. H.; Liu, Zhi] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
会议名称：
24th International Conference on Computers in Education (ICCE) - Think Global Act Local
会议时间：
NOV 28-DEC 02, 2016
会议地点：
Indian Inst Technol Bombay, Mumbai, INDIA
会议主办单位：
Indian Inst Technol Bombay
关键词：
Testing behavioral patterns;k-mean clustering;online Chinese reading assessment
摘要：
Understanding students' testing behaviors may help researchers design better computer-based assessment. For this reason, this study aims at characterizing students' behavioral patterns in online reading test by k-means clustering. The clustering algorithm adopts eight indicators: reading time, answering time, the number of choosing articles, the number of choosing questions, the number of selecting options, the number of marking questions, the number of revisiting a test and the final testing scores. The result identifies five clusters of student testers: slow readers, fast readers, question markers, fast responders, and re-readers.
