摘要
在大数据的大背景下,学习分析成为学界研究的热点。对于学习分析的研究,美国已经提出预测学习者学习偏好的模型,并且根据预测结果对学习者进行自适应引导;国内对于学习分析实现了学习者评估,并且在评估后对学习者给予干预与预警。学习分析是使用数据和模型预测学习者收获和行为,具备处理这些信息的能力。其模型自底向上分别为数据层、机制层、结果层。学习分析首先要获取学习者的在线学习数据,而后选择适合研究对象的算法(如决策树、神经网络、支持向量机、K-Means聚类等)。拟提出将学习分析应用在高校计算机教学中实证研究模型,将试验对象分为A、B 2个对照组,根据前7周学习行为进行分析与预测,并于8-11周进行学习干预,最后检验预测结果及干预效果。基于数据挖掘的学习分析能够精准支持课程教学,在优化学习任务和教学决策方面有一定的创新性。
Learning analytics has become a focus in academia in Big Data Era. For the study of learning analysis, the United States has proposed a model to predict learners＇ learning preferences, and given adaptive guidance to learners based on the prediction results. Meanwhile, domestic research on learning analysis achieves learner evaluation and provides intervention and early warning to learners after assessment. Learning analysis is to use data and models to predict learners＇ harvest and behavior, and have the ability to process these information. The models, from bottom to top, are data layer, mechanism layer and result layer. Learning analysis should first obtain the learners＇ online learning data, and then select algorithms suitable for research objects（such as Decision Tree, Neural Network, Support Vector Machine, K-Means Clustering, etc.）. This paper proposes an empirical study model which applies learning analysis to the teaching of computer teaching in Colleges and universities. The experimental subjects are divided into two groups of A and B, which are analyzed and predicted according to the fi rst 7 weeks＇ learning behavior, and the study intervention, is carried out with 8-11 weeks. Finally, the results of prediction and the effect of intervention are tested. The study analysis based on data mining can support the course teaching accurately, and it has some innovation in optimizing the learning task and teaching decision which is worthy for further exploring.
作者
隋永博
曹旭
SUI Yongbo;CAO Xu(Changchun University of Chinese Medicine,Changchun 130117,China)
出处
《长春中医药大学学报》
2018年第5期988-991,共4页
Journal of Changchun University of Chinese Medicine
基金
吉林省高教学会高教科研课题"大数据背景下计算机基础课程在线学习分析研究"(JGJX2018C60)
