基于细粒度学习情感本体的学习效果评估方法 ——以算法设计与分析课程为例

计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 58-62.

基于细粒度学习情感本体的学习效果评估方法 ——以算法设计与分析课程为例

张春霞,牛振东,施重阳,商建云

北京理工大学计算机学院北京100081

出版日期:2018-06-20 发布日期:2018-08-03
作者简介:张春霞(1974-),女,博士,副教授,主要研究方向为大数据搜索与挖掘、知识工程等,E-mail:cxzhang@bit.edu.cn(通信作者);牛振东(1968-),男,博士,教授,主要研究方向为智能信息处理等,E-mail:zniu@bit.edu.cn;施重阳(1980-),男,博士,讲师,主要研究方向为数据挖掘和信息检索等,E-mail:cy_shi@bit.edu.cn;商建云(1965-),女,博士,高级工程师,主要研究方向为大数据搜索与挖掘等,E-mail:shangjia@bit.edu.cn。
基金资助:
北京理工大学学位与研究生教育发展研究课题(YJYJG2015-B16),北京理工大学研究生学术型精品课程课题(XSKC2016020),北京理工大学研究生教学团队建设课题(YJXTD-2015-A03)资助

Learning Effect Evaluation Method Based on Fine-granularity Learning Emotion Ontology
——Taking Algorithm Design and Analysis Course as Example

ZHANG Chun-xia,NIU Zhen-dong,SHI Chong-yang, SHANG Jian-yun

School of Computer Science,Beijing Institute of Technology,Beijing 100081,China

Online:2018-06-20 Published:2018-08-03

摘要/Abstract

摘要： 教育目标包括认知领域目标、动作技能领域目标和情感领域目标。情感领域目标教育已受到越来越多教育者和众多领域学者的关注和研究。学习者的情感在传统教育和网络教育中都起着十分重要的作用,影响着学习者的学习主动性、积极性、创造性以及学习效果。基于多年承担本科生和硕士生的算法相关课程的教学实践,构建了细粒度学习情感本体,提出了基于细粒度学习情感本体的学习效果评估方法。细粒度学习情感本体的特点是引入了课程知识点之间的多种语义关系,构建了基于知识点的教师情感反馈行为分类。学习效果评估方法的特点是构建了基于细粒度学习情感本体中知识点关系路径的学习情感演化模型,并应用该模型来评估学习效果。

关键词: 算法课程, 细粒度学习情感本体, 学习情感演化模型, 学习效果评估

Abstract: Education goals include cognitive domain goal,motor skill domain goal and emotional domain goal.Education of emotional domain goal has received attentions and research of more and more pedagogues and scholars of many domains.Emotions of learners play an important role in traditional education and network education,and they affect lear-ning initiative,enthusiasm,creativity and learning effects of learners.According to authors’ teaching practices of algorithm related courses of undergraduates and master graduates for many years,this paper built a fine-granularity learning emotion ontology,and proposed a learning effect evaluation method based on fine-granularity learning emotion ontology.The characteristics of the fine-granularity learning emotion ontology are that it introduces multiple semantic relations among knowledge points of courses,and constructs a classification of emotion feedback actions of teachers.The traits of the learning effect evaluation method are that it builds the evolutional model of learning emotion based on relational paths of knowledge points in the fine-granularity learning emotion ontology,and this model can be used to evaluate learning effects of learners.

Key words: Algorithm course, Evolutional model of learning emotion, Fine-granularity learning emotion ontology, Learning effect evaluation

中图分类号:

TP182

张春霞,牛振东,施重阳,商建云. 基于细粒度学习情感本体的学习效果评估方法 ——以算法设计与分析课程为例[J]. 计算机科学, 2018, 45(6A): 58-62. https://doi.org/

ZHANG Chun-xia,NIU Zhen-dong,SHI Chong-yang, SHANG Jian-yun. Learning Effect Evaluation Method Based on Fine-granularity Learning Emotion Ontology
——Taking Algorithm Design and Analysis Course as Example[J]. Computer Science, 2018, 45(6A): 58-62. https://doi.org/

参考文献

[1]耿显亚.影响高校数学学习的情感因素分析.教育教学论坛,2014,6(18):102-103.
[2]BLOOM B S,ENGELHART M D,FURST E J,et al.Taxonomy of Educational Objectives:The Classification of Educational Goals.Handbook I:Cognitive Domain [M].New York:Longman,1956.
[3]刘万友.网络教育和传统教育之比较.科技信息,2007,25(14):164.
[4]邓妍祯,莫建萍.积极情感因素在网络自主学习中的作用及培养途径分析.中国电力教育,2014,30(32):52-53.
[5]邓铁辉.教师教学正面情感的提高与差生学习负面情感的消除.湖南科技学院学报,2015,36(7):146-147.
[6]沈映珊,汤庸.社交学习网络中基于学习认知的情感交互研究.现代教育技术,2015,25(9):90-96. [7]陶小梅,牛秦洲. 基于情感学习本体的教学反馈策略生成算法的研究[J]. 计算机工程与科学,2015,37(2):320-328.
[8]盛群力,褚献华.布卢姆认知目标分类修订的二维框架.课程教材教法,2004,24(9):90-96. [9]唐晓波,朱娟,杨丰华.基于情感本体和KNN算法的在线评论情感分类研究.信息系统,2016,39(6):110-114. [10]ANDERSON L W,KRATHWOHL D R,AIRASIAN P W, et al.A Taxonomy for Learning,Teaching and Assessing:A Revision of Bloom’s Taxonomy of Educational Objectives .New York:Longman,2001.
[11]EKMAN P.Chapter 3:Basic Emotions.DALGLEISH T and POWER MJ.Handbook of Cognition and Emotion[M].Chichester:John Wiley & Sons,2000.
[12]ECONOMIDESA A.Emotional Feedback in CAT[J].International Journal of Instructional Technology & Distance Lear-ning,2006,3(2):11-20.
[13]EKMAN P.An Argument for Basic Emotions[J].Cognition and Emotion,1992,6(3/4):169-200.

相关文章 15

[1]	王杰, 李晓楠, 李冠宇. 基于自适应注意力机制的知识图谱补全算法 Adaptive Attention-based Knowledge Graph Completion 计算机科学, 2022, 49(7): 204-211. https://doi.org/10.11896/jsjkx.210400129
[2]	方连花, 林玉梅, 吴伟志. 随机多尺度序决策系统的最优尺度选择 Optimal Scale Selection in Random Multi-scale Ordered Decision Systems 计算机科学, 2022, 49(6): 172-179. https://doi.org/10.11896/jsjkx.220200067
[3]	许思雨, 秦克云. 基于剩余格的模糊粗糙集的拓扑性质 Topological Properties of Fuzzy Rough Sets Based on Residuated Lattices 计算机科学, 2022, 49(6A): 140-143. https://doi.org/10.11896/jsjkx.210200123
[4]	蔡晓娟, 谭文安. 一种改进的融合相似度和信任度的协同过滤算法 Improved Collaborative Filtering Algorithm Combining Similarity and Trust 计算机科学, 2022, 49(6A): 238-241. https://doi.org/10.11896/jsjkx.210400088
[5]	李妍妍, 秦克云. 广义粗糙近似算子的拓扑性质 On Topological Properties of Generalized Rough Approximation Operators 计算机科学, 2022, 49(3): 263-268. https://doi.org/10.11896/jsjkx.210100204
[6]	赵杨, 倪志伟, 朱旭辉, 刘浩, 冉家敏. 基于改进狮群进化算法的面向空间众包平台的多工作者多任务路径规划方法 Multi-worker and Multi-task Path Planning Based on Improved Lion Evolutionary Algorithm forSpatial Crowdsourcing Platform 计算机科学, 2021, 48(11A): 30-38. https://doi.org/10.11896/jsjkx.201200085
[7]	徐进. 面向工业装配的知识图谱构建与应用研究 Construction and Application of Knowledge Graph for Industrial Assembly 计算机科学, 2021, 48(6A): 285-288. https://doi.org/10.11896/jsjkx.200600116
[8]	丁玲, 向阳. 基于分层次多粒度语义融合的中文事件检测 Chinese Event Detection with Hierarchical and Multi-granularity Semantic Fusion 计算机科学, 2021, 48(5): 202-208. https://doi.org/10.11896/jsjkx.200800038
[9]	鲁巡, 李妍妍, 秦克云. 三种近似算子之间的关系 Relationship Among Three Types of Rough Approximation Pairs 计算机科学, 2021, 48(4): 49-53. https://doi.org/10.11896/jsjkx.200900089
[10]	胡平, 秦克云. 基于模糊等价的毕达哥拉斯模糊集相似度构造方法 Similarity Construction Method for Pythagorean Fuzzy Set Based on Fuzzy Equivalence 计算机科学, 2021, 48(1): 152-156. https://doi.org/10.11896/jsjkx.191100102
[11]	郭庆春,马建敏. 对偶区间集概念格上区间集协调集的判定方法 Judgment Methods of Interval-set Consistent Sets of Dual Interval-set Concept Lattices 计算机科学, 2020, 47(3): 98-102. https://doi.org/10.11896/jsjkx.190500098
[12]	冯晨娇,梁吉业,宋鹏,王智强. 基于极端评分行为的相似度计算 New Similarity Measure Based on Extremely Rating Behavior 计算机科学, 2020, 47(2): 31-36. https://doi.org/10.11896/jsjkx.190500130
[13]	石晓玲, 陈芷, 杨立功, 沈伟. 基于自适应样本权重的矩阵分解推荐算法 Matrix Factorization Recommendation Algorithm Based on Adaptive Weighted Samples 计算机科学, 2019, 46(6A): 488-492.
[14]	陈德江, 王君, 张浩为. 基于直觉模糊多属性决策的动态威胁评估模型 Dynamic Threat Assessment Model Based on Intuitionistic Fuzzy Multiple Attribute Decision Making 计算机科学, 2019, 46(4): 183-188. https://doi.org/10.11896/j.issn.1002-137X.2019.04.029
[15]	林洪,秦克云. 决策形式背景基于三支决策规则的属性约简 Attribute Reduction for Decision Formal Contexts Based on Threek-way Decision Rules 计算机科学, 2019, 46(3): 248-252. https://doi.org/10.11896/j.issn.1002-137X.2019.03.037

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed