ABSTRACT

Interactive learning environments can generate massive learning behavior data and the support of learning behavior big data can ensure the completeness of data analysis and robustness of relationship verification. In this study, learning behaviors are divided into training set and testing set, BP neural network and recurrent Elman network are integrated. Through the training set, the recursive multi-layer feedback neural network model based on the fusion of context features is constructed. The recognition algorithm is designed. From relative standard deviation (RSD) and prediction accuracy (P.A), the model and algorithm designed in this study are more suitable for the recognition and prediction of learning behavior data. Furthermore, the key topological path and interaction process equation of learning behaviors are designed, three kinds of intervention optimization strategies are constructed to serve the daily teaching processes of three courses. After two semesters of learning behavior feature recognition and relationship analysis, learners’ interest, assessment pass rate, or excellence rate have improved significantly. So the intervention optimization strategies of learning behaviors based on the analysis results of models and algorithms can influence the feature distribution and behavior trend.

摘要

交互式学习环境可以产生海量的学习行为数据，学习行为大数据的支持可以保证数据分析的完整性和关系验证的鲁棒性。本研究将学习行为分为训练集和测试集，融合BP神经网络和循环Elman网络。通过训练集，构建了基于上下文特征融合的递归多层反馈神经网络模型。设计了识别算法。从相对标准差（RSD）和预测精度（PA）来看，本研究设计的模型和算法更适合学习行为数据的识别和预测。进一步设计了学习行为的关键拓扑路径和交互过程方程，构建了三种干预优化策略，服务于三门课程的日常教学过程。经过两个学期的学习行为特征识别和关系分析，学习者的兴趣、考核通过率或优秀率都有明显提高。因此，基于模型和算法分析结果的学习行为干预优化策略可以影响特征分布和行为趋势。