| 面向深度学习的动态知识图谱建构模型及评测 |
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| 引用本文： | 姜强,药文静,赵蔚,李松.面向深度学习的动态知识图谱建构模型及评测[J].电化教育研究,2020(3):85-92. |
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| 作者姓名： | 姜强 药文静 赵蔚 李松 |
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| 作者单位： | 东北师范大学信息科学与技术学院;国家开放大学教育教学部 |
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| 基金项目： | 国家社会科学基金教育学一般课题“基于大数据的在线学习精准预警与干预机制研究”(课题编号:BCA170074)。 |
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| 摘 要： | 深度学习回应时代诉求,指向“核心素养”改革,回答了“培养什么样的人”的问题,回归了学习本质。知识图谱有助于促进学生深入思考,提高问题解决能力、批判性思维和创新能力,实现深度学习。但以往知识建构存在组织静态、孤立的局限,基于ARCS动机模型和知识建构理论,从协同知识建构、动机策略和学习环境建构面向深度学习的动态知识图谱模型,具有动态生成、及时反馈、交互共享等特点,突出学生的主体性、能动性,增强学习体验。以大学生为研究对象,利用文本挖掘、滞后序列分析等方法评测动态知识图谱建构模型。结果表明,实验组学生在学习成绩、注意力程度等方面均优于控制组,尤其对中低水平动机学习者产生积极影响,显著提高了学生在完成任务过程中的感知注意力、自信心和满意度。动态知识图谱建构发展思路可从重塑任务前计划、社会认知开放性、意义协商及生成性教学等方面寻找突破口,催生深层次认知能力与高阶思维。
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| 关 键 词： | 深度学习 动态知识图谱 协同知识建构 ARCS模型 文本挖掘 滞后序列分析 |
A Model to Construct Dynamic Knowledge Map for Deep Learning and Its Evaluation |
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| JIANG Qiang,YAO Wenjing,ZHAO Wei,LI Song.A Model to Construct Dynamic Knowledge Map for Deep Learning and Its Evaluation[J].E-education Research,2020(3):85-92. |
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| Authors: | JIANG Qiang YAO Wenjing ZHAO Wei LI Song |
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| Institution: | (School of Information Science and Technology,Northeast Normal University,Changchun Jilin 130117;Education Department,National Open University,Beijing 100039) |
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| Abstract: | Deep learning,which responds to the epoch demands and the reform of"core literacy",answers the question of"what kind of people to cultivate"and returns to the essence of learning.Knowledge map can help students to think deeply,improve their problem solving ability,critical thinking and creative ability as well,and realize deep learning.However,the previous knowledge construction had the limitation of static and isolated organization.Based on ARCS motivation model and knowledge construction theory,a model to construct the dynamic knowledge map for deep learning is built from collaborative knowledge construction,motivational strategies and learning environment,which is characterized by dynamic generation,timely feedback and interactive sharing,so as to highlight students'subjectivity and initiative and enhance their learning experience.Taking college students as the research object,this paper analyzes the effect of the model by means of text mining and lag sequence analysis.The results show that the experimental group is superior to the control group in terms of academic performance,attention,etc.Particularly,this model has a positive effect on learners with middle-level or low-level motivation,and their perceived attention,confidence and satisfaction in completing tasks have been significantly improved.The construction and development of dynamic knowledge map can be explored from the aspects of remolding pre-task planning,social cognitive openness,meaning negotiation and generative teaching,so as to promote deep cognitive ability and higher-order thinking. |
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| Keywords: | Deep Learning Dynamic Knowledge Map Collaborative Knowledge Construction ARCS Model Text Mining Lag Sequence Analysis |
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