[1]郎 波,樊一娜.基于深度神经网络的个性化学习行为评价方法[J].计算机技术与发展,2019,29(07):6-10.[doi:10. 3969 / j. issn. 1673-629X. 2019. 07. 002]
　LANG Bo,FAN Yi-na.Personalized Learning Behavior Evaluation Method Based on Deep Neural Network[J].,2019,29(07):6-10.[doi:10. 3969 / j. issn. 1673-629X. 2019. 07. 002]
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基于深度神经网络的个性化学习行为评价方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年07期
页码:
6-10
栏目:
智能、算法、系统工程
出版日期:
2019-07-10

文章信息/Info

Title:
Personalized Learning Behavior Evaluation Method Based on Deep Neural Network
文章编号:
1673-629X(2019)07-0006-05
作者:
郎 波; 樊一娜
北京师范大学珠海分校,广东 珠海 519087
Author(s):
LANG Bo; FAN Yi-na
Beijing Normal University,Zhuhai,Zhuhai 519087,China
关键词:
个性化学习; 深度神经网络; 特征聚类; 行为评价
Keywords:
personalized learning; deep neural network; feature clustering; behavior evaluation
分类号:
TP391.1
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 07. 002
摘要:
人工智能技术和大数据的发展催生了各种形式和内容的在线课程,为个性化学习的普及提供了可能。 与传统的教学方式不同,个性化学习需要解决如何根据不同类型的学习者的特点对其学习行为进行准确个性化评价的问题。 文中首先利用学习者在在线学习平台上产生的大数据作为研究目标,根据学习者的学习能力层次,按照认知思维的方式建立深度神经网络对其进行聚类分组。 为降低数据冗余度,提高处理效率,采用了具有五个隐层的深度神经网络进行典型性特征的提取,从而得到更为准确的评价结果。 最后利用神经网络模型得到不同组别的学习行为聚类结果和不同层次的学习者学习五门课程知识点的评估曲线。 从实验结果来看,提出的个性化评价方法能够有效地分析出不同能力等级的学习者之间的学习差异,而且与人工专家评价的标准基本一致。
Abstract:
The development of artificial intelligence technology and big data has spawned online courses in various forms and contents, making it possible to popularize personalized learning. Different from traditional teaching methods,personalized learning needs to solve the problem of how to accurately and personally evaluate their learning behavior according to the different types of learners. First of all, the big data generated by learners on the online learning platform is employed as the research goal. According to the level of learner’s learning ability,a deep neural network is established to cluster and group them by the way of cognitive thinking. In order to reduce the data redundancy and improve the processing efficiency,a deep neural network with five hidden layers is employed to extract the typical features from original data sets,so as to obtain more accurate evaluation results. Finally,the neural network model is used to obtain the learning behavior clustering results of different groups and the evaluation curves of the five levels of knowledge points for learners at different levels. From the experimental results, the personalized evaluation method proposed can effectively analyze the learning differences between learners with different ability levels,and is basically consistent with the standards of artificial expert evaluation.

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更新日期/Last Update: 2019-07-10