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学者姓名：陈琼
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Abstract ：
Online and offline blended teaching mode, the future trend of higher education, has recently been widely used in colleges around the globe. In the article, we conducted a study on students' learning behavior analysis and student performance prediction based on the data about students' behavior logs in three consecutive years of blended teaching in a college's "Java Language Programming" course. Firstly, the data from diverse platforms such as MOOC, Rain Classroom, PTA, and cnBlog are integrated and preprocessed. Secondly, a novel multiclass classification framework, combining the genetic algorithm (GA) and the error correcting output codes (ECOC) method, is developed to predict the grade levels of students. In the framework, GA is designed to realize both the feature selection and binary classifier selection to fit the ECOC models. Finally, key factors affecting grades are identified in line with the optimal subset of features selected by GA, which can be analyzed for teaching significance. The results show that the multiclass classification algorithm designed in this article can effectively predict grades compared with other algorithms. In addition, the selected subset of features corresponding to learning behaviors is pedagogically instructive.
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| GB/T 7714 | Xie, Shu-Tong , He, Zong-Bao , Chen, Qiong et al. Predicting Learning Behavior Using Log Data in Blended Teaching [J]. | SCIENTIFIC PROGRAMMING , 2021 , 2021 . |
| MLA | Xie, Shu-Tong et al. "Predicting Learning Behavior Using Log Data in Blended Teaching" . | SCIENTIFIC PROGRAMMING 2021 (2021) . |
| APA | Xie, Shu-Tong , He, Zong-Bao , Chen, Qiong , Chen, Rong-Xin , Kong, Qing-Zhao , Song, Cun-Ying . Predicting Learning Behavior Using Log Data in Blended Teaching . | SCIENTIFIC PROGRAMMING , 2021 , 2021 . |
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Abstract ：
In recent years, online and offline teaching activities have been combined by the Small Private Online Course (SPOC) teaching activities, which can achieve a better teaching result. Therefore, colleges around the world have widely carried out SPOC-based blending teaching. Particularly in this year's epidemic, the online education platform has accumulated lots of education data. In this paper, we collected the student behavior log data during the blending teaching process of the "College Information Technology Fundamentals" course of three colleges to conduct student learning behavior analysis and learning outcome prediction. Firstly, data collection and preprocessing are carried out; cluster analysis is performed by using k-means algorithms. Four typical learning behavior patterns have been obtained from previous research, and these patterns were analyzed in terms of teaching videos, quizzes, and platform visits. Secondly, a multiclass classification framework, which combines a feature selection method based on genetic algorithm (GA) with the error correcting output code (ECOC) method, is designed for training the classification model to achieve the prediction of grade levels of students. The experimental results show that the multiclass classification method proposed in this paper can effectively predict the grade of performance, with an average accuracy rate of over 75%. The research results help to implement personalized teaching for students with different grades and learning patterns.
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| GB/T 7714 | Xie, Shu-tong , Chen, Qiong , Liu, Kun-hong et al. Learning Behavior Analysis Using Clustering and Evolutionary Error Correcting Output Code Algorithms in Small Private Online Courses [J]. | SCIENTIFIC PROGRAMMING , 2021 , 2021 . |
| MLA | Xie, Shu-tong et al. "Learning Behavior Analysis Using Clustering and Evolutionary Error Correcting Output Code Algorithms in Small Private Online Courses" . | SCIENTIFIC PROGRAMMING 2021 (2021) . |
| APA | Xie, Shu-tong , Chen, Qiong , Liu, Kun-hong , Kong, Qing-zhao , Cao, Xiu-juan . Learning Behavior Analysis Using Clustering and Evolutionary Error Correcting Output Code Algorithms in Small Private Online Courses . | SCIENTIFIC PROGRAMMING , 2021 , 2021 . |
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The teaching mode combining Massive Open Online Course (MOOC) with flipped classroom has been emerged in recent years since the arrangement can enhance obviously students’ learning outcome. In this paper, we proposed an ensemble approach based on genetic algorithm (GA) for feature selection (EA-GA) for MOOC data analysis, focusing on the prediction of students’ learning outcome. The work is based on the implementation of an online course from a college. The tracking data is collected from both the online MOOC platform and the offline classroom. After combining all data together, a GA based ensemble system is designed to predict students’ academic performances. Some other machining learning algorithms are also derived for performance comparison of different algorithms. Simulation results showed the proposed the EA-GA preforms better than other algorithms to predict well the students’ learning score. The 'shared features' found by EA-GA from massive features are helpful to discriminate at-risk students and excellent students for different teaching intervention purpose. © Springer International Publishing AG, part of Springer Nature 2018.
Keyword ：
E-learning E-learning Feature Selection Feature Selection Forecasting Forecasting Genetic algorithms Genetic algorithms Learning algorithms Learning algorithms Students Students Teaching Teaching
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| GB/T 7714 | Li, Jia-Lian , Xie, Shu-Tong , Wang, Jun-Neng et al. Prediction and learning analysis using ensemble classifier based on GA in SPOC experiments [J]. | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 2018 , 10943 LNCS : 339-348 . |
| MLA | Li, Jia-Lian et al. "Prediction and learning analysis using ensemble classifier based on GA in SPOC experiments" . | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10943 LNCS (2018) : 339-348 . |
| APA | Li, Jia-Lian , Xie, Shu-Tong , Wang, Jun-Neng , Lin, Yu-Qing , Chen, Qiong . Prediction and learning analysis using ensemble classifier based on GA in SPOC experiments . | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 2018 , 10943 LNCS , 339-348 . |
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本文首先简要介绍基于SPOC的高校翻转课堂教学模式的定义,当前基于SPOC的翻转课堂模式在建设和发展过程中表现出的不足之处,而后对构建和完善基于SPOC的高校翻转课堂教学模式展开简要论述.
Keyword ：
SPOC SPOC 信息技术 信息技术 构建 构建 策略 策略 翻转课堂 翻转课堂 高校 高校
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| GB/T 7714 | 陈琼 . 基于SPOC的高校翻转课堂教学模式构建研究 [J]. | 时代教育 , 2018 , (9) : 13 . |
| MLA | 陈琼 . "基于SPOC的高校翻转课堂教学模式构建研究" . | 时代教育 9 (2018) : 13 . |
| APA | 陈琼 . 基于SPOC的高校翻转课堂教学模式构建研究 . | 时代教育 , 2018 , (9) , 13 . |
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Abstract ：
教育质量是高等学校可持续发展的生命线。农林院校学生计算机应用能力的培养是一个渐进、持续的系统工程。结合大学生计算机应用能力教学改革,针对农林院校大学生就业难问题,农林院校学生计算机应用能力的培养思路与途径应该以就业需求为导向,明确育人目标,统筹规划,修订符合教学规律的培养方案,优化课程体系,加强教学团队建设,创新教学方法,强化实践教学,学教并重,将计算机应用能力培养贯穿于高等教育的全过程,提升农林院校学生信息素养与就业竞争力。
Keyword ：
信息素养 信息素养 农林院校 农林院校 就业需求 就业需求 教育质量 教育质量 计算机应用能力 计算机应用能力
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| GB/T 7714 | 纪祥敏 , 景林 , 陈秋妹 et al. 农林院校大学生计算机应用能力培养途径 [J]. | 沈阳农业大学学报(社会科学版) , 2013 , 15 (03) : 299-302 . |
| MLA | 纪祥敏 et al. "农林院校大学生计算机应用能力培养途径" . | 沈阳农业大学学报(社会科学版) 15 . 03 (2013) : 299-302 . |
| APA | 纪祥敏 , 景林 , 陈秋妹 , 林大辉 , 陈琼 . 农林院校大学生计算机应用能力培养途径 . | 沈阳农业大学学报(社会科学版) , 2013 , 15 (03) , 299-302 . |
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Abstract ：
分析紫外光照射下木荷产生自由基的规律和表面化学组成及结构的变化。利用电子自旋共振波谱(ESR)和X射线光电子能谱(XPS)技术分别测量紫外光辐照后木荷颗粒的自由基波谱和X射线光电子能谱。结果表明,木荷自由基的光谱分裂因子g=2.003 3,自由基的强度随着辐照时间按Y=1-e-biPt规律增加,紫外光辐照60 min后,木荷表面氧、碳原子比稍有增加,C—C、C—H和C—O含量增加,C=O含量减少,—O—C=O含量增加为原来的2倍左右,说明木荷表面生成了一些含氧官能团或碳的氧化态增高。
Keyword ：
X射线光电子能谱 X射线光电子能谱 木荷 木荷 电子自旋共振波谱 电子自旋共振波谱 紫外光 紫外光
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| GB/T 7714 | 张文辉 , 陈琼 . 紫外光下木荷的ESR和XPS分析 [J]. | 福建林学院学报 , 2012 , 32 (03) : 274-279 . |
| MLA | 张文辉 et al. "紫外光下木荷的ESR和XPS分析" . | 福建林学院学报 32 . 03 (2012) : 274-279 . |
| APA | 张文辉 , 陈琼 . 紫外光下木荷的ESR和XPS分析 . | 福建林学院学报 , 2012 , 32 (03) , 274-279 . |
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通过对福建省内高中信息技术相关课程及福建省内外《大学信息技术基础》课程设置的调研,针对当前《大学信息技术基础》教学过程中存在的一些问题,结合自己的工作实际,提出改革教学方式、方法,更新教学内容等,提高计算机基础教学质量和教学效果,以适应创新型、应用型人才培养的社会迫切需要。
Keyword ：
大学信息技术基础 大学信息技术基础 教学改革 教学改革
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| GB/T 7714 | 刘秀玲 , 陈琼 . 《大学信息技术基础》课程教学改革的研究 [J]. | 现代计算机(专业版) , 2012 , (33) : 42-44 . |
| MLA | 刘秀玲 et al. "《大学信息技术基础》课程教学改革的研究" . | 现代计算机(专业版) 33 (2012) : 42-44 . |
| APA | 刘秀玲 , 陈琼 . 《大学信息技术基础》课程教学改革的研究 . | 现代计算机(专业版) , 2012 , (33) , 42-44 . |
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绝干的毛竹粉体在60 Coγ辐照下产生自由基的同时还发生化学组成和结构的变化。利用电子自旋共振波谱(ESR)和X射线光电子能谱(XPS)技术分别测量γ射线辐照后毛竹粉体的自由基波谱和X射线光电子能谱。经测试,毛竹自由基的光谱分裂因子g=2.003 3,自由基的强度随吸收剂量按指数规律增加;经200kGy剂量的辐照后毛竹表面的O/C原子个数比稍有增加,C—C和C—H含量增加,C—O和C O含量减少,—O—C O含量增加为原来的2.5倍,说明毛竹表面生成了一些含氧官能团或碳的氧化态增高。
Keyword ：
γ射线 γ射线 化学结构 化学结构 毛竹 毛竹 自由基 自由基
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| GB/T 7714 | 张文辉 , 陈琼 . ~(60)Co γ辐照下毛竹的自由基与化学结构的变化 [J]. | 原子能科学技术 , 2012 , 46 (09) : 1049-1054 . |
| MLA | 张文辉 et al. "~(60)Co γ辐照下毛竹的自由基与化学结构的变化" . | 原子能科学技术 46 . 09 (2012) : 1049-1054 . |
| APA | 张文辉 , 陈琼 . ~(60)Co γ辐照下毛竹的自由基与化学结构的变化 . | 原子能科学技术 , 2012 , 46 (09) , 1049-1054 . |
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分析了紫外光照射下毛竹自由基的变化规律和表面化学组成及结构的变化.利用电子自旋共振波谱和X射线光电子能谱技术,分别测量紫外光辐照后毛竹颗粒的自由基波谱和X射线光电子能谱.结果表明:毛竹自由基的光谱分裂因子g=2.003 3,自由基的强度随着辐照时间按Y=1-e-biPt规律增加;紫外光照60min后毛竹表面O/C原子比稍有增加,C-C和C-H含量增加,C-O和C=O含量减少,-O-C=O含量增加为原来的3倍左右,说明毛竹表面生成了一些含氧官能团或碳的氧化态增高.
Keyword ：
X射线光电子能谱 X射线光电子能谱 毛竹 毛竹 紫外光 紫外光 自由基 自由基
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| GB/T 7714 | 张文辉 , 陈琼 . 紫外光下毛竹的自由基与光电子能谱分析 [J]. | 光子学报 , 2012 , 41 (08) : 893-897 . |
| MLA | 张文辉 et al. "紫外光下毛竹的自由基与光电子能谱分析" . | 光子学报 41 . 08 (2012) : 893-897 . |
| APA | 张文辉 , 陈琼 . 紫外光下毛竹的自由基与光电子能谱分析 . | 光子学报 , 2012 , 41 (08) , 893-897 . |
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Abstract ：
利用电子自旋共振波谱(ESR)和X射线光电子能谱(XPS)技术分别测量γ射线辐照后木荷粉体的自由基波谱和X射线光电子能谱。分析木荷粉体在60Coγ射线辐照下自由基的变化规律、化学组成和结构变化。结果表明:木荷自由基的光谱分裂因子g=2.0033,自由基的强度随吸收剂量按指数规律I1 e bi D增加;经过200 kGy剂量的辐照后木荷表面O/C原子比稍有增加,C–C、C–H和C–O键含量增加,C=O双键含量减少,–O–C=O含量增加为原来的2.5倍,说明木材表面生成了一些含氧官能团,或碳的氧化态增高。
Keyword ：
X射线光电子能谱 X射线光电子能谱 γ射线 γ射线 木荷 木荷 电子自旋共振波谱 电子自旋共振波谱
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| GB/T 7714 | 张文辉 , 陈琼 . γ射线辐照后木荷粉自由基的ESR和XPS分析 [J]. | 辐射研究与辐射工艺学报 , 2012 , 30 (05) : 280-285 . |
| MLA | 张文辉 et al. "γ射线辐照后木荷粉自由基的ESR和XPS分析" . | 辐射研究与辐射工艺学报 30 . 05 (2012) : 280-285 . |
| APA | 张文辉 , 陈琼 . γ射线辐照后木荷粉自由基的ESR和XPS分析 . | 辐射研究与辐射工艺学报 , 2012 , 30 (05) , 280-285 . |
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