ABSTRACT

Learning interaction activities are the key part of tracking and evaluating learning behaviors, that plays an important role in data-driven autonomous learning and optimized learning in interactive learning environments. In this study, a big data set of learning behaviors with multiple learning periods is selected. According to the instance characteristics of data attributes and learning behaviors, the analysis of learning interaction activities and their relationships is mapped to the optimized design of Markov random fields. Appropriate models and multi-level algorithms are constructed to demonstrate the maximal cliques that affect the learning behaviors and mine the critical paths, then cooperative inference strategies of learning interaction activities are deduced. In order to verify the effectiveness of the random fields and analysis technology of learning interaction activities, several approximate algorithms are introduced to fully compare multiple statistical indexes; To verify the feasibility of collaborative recommendation strategies, the improvement effect of learning behaviors and learning effect is tested by applying it to the actual teaching process. Research and analysis show that the random fields of learning interaction activities and the collaborative inference strategies have advantages in data analysis, topological relationship construction and teaching practice guidance.

摘要

学习交互活动是跟踪和评估学习行为的关键部分，在交互学习环境中数据驱动的自主学习和优化学习中发挥着重要作用。在本研究中，选择了具有多个学习周期的学习行为大数据集。根据数据属性和学习行为的实例特征，将学习交互活动及其关系的分析映射到马尔可夫随机场的优化设计。构建适当的模型和多层次算法来展示影响学习行为的最大派系并挖掘关键路径，然后推导出学习交互活动的合作推理策略。为了验证随机场和学习交互活动分析技术的有效性，引入了几种近似算法来充分比较多个统计指标；为了验证协同推荐策略的可行性，将其应用于实际教学过程中来测试其学习行为和学习效果的改善效果。研究分析表明，学习交互活动的随机场和协同推理策略在数据分析、拓扑关系构建和教学实践指导等方面具有优势。将其运用到实际教学过程中来检验学习行为和学习效果的改善效果。研究分析表明，学习交互活动的随机场和协同推理策略在数据分析、拓扑关系构建和教学实践指导等方面具有优势。将其运用到实际教学过程中来检验学习行为和学习效果的改善效果。研究分析表明，学习交互活动的随机场和协同推理策略在数据分析、拓扑关系构建和教学实践指导等方面具有优势。