基于人类动力学的在线学习行为规律分析及建模研究  

A Study on Law Analysis and Modeling of Online Learning Behaviors Based on Human Dynamics

作　　者：李月 姜强 方慧 赵蔚 LI Yue;JIANG Qiang;FANG Hui;ZHAO Wei(School of Information Science and Technology,Northeast Normal University,Changchun Jilin 130117;Northeast Yucai School,Shenyang Liaoning 110179)

机构地区：[1]东北师范大学信息科学与技术学院,吉林长春130117 [2]东北育才学校,辽宁沈阳110179

出　　处：《电化教育研究》2023年第1期77-83,共7页E-education Research

基　　金：2020年度国家自然科学基金面上项目“网络学习空间中的学习风险预警模型和干预机制研究”(项目编号:62077012)。

摘　　要：探究在线学习行为,有助于明晰在线学习本质、改进在线学习过程。已有研究主要聚焦于在线学习行为的外显表征分析,还应关注在线学习行为发生的内隐规律与动因。文章采用人类动力学研究方法,挖掘在线学习行为时间上的规律性,构建教学活动和学习兴趣双重驱动的在线学习行为动力学模型。研究表明学习者群体与个体的在线学习行为规律都具有一定的周期性、阵发性等基本特征,并且在时间间隔分布方面具有显著的重尾特征。模型在解释学习行为发生动因方面具体表现为,学习兴趣的衰减与学习行为发生概率呈负相关,而教学活动的影响与学习行为发生概率呈正相关。在促进在线学习行为中,需考虑任务与协作驱动的教学设计、智能技术支持的资源推荐、基于学习规律的个性化干预等策略,以推动破解在线教育质量难题。Exploring online learning behaviors helps to clarify the nature of online learning and improve the online learning process. While existing studies have mainly focused on the analysis of external representation of online learning behaviors, and studies should also pay attention to the internal laws and motivation of online learning behaviors. This paper adopts a human dynamics approach to excavate the temporal regularity of online learning behaviors and constructs a dynamics model of online learning behavior driven by both teaching activities and learning interests. The research shows that the online learning behavior law of both groups of learners and individuals has certain basic characteristics such as periodicity and paroxysm, and has significant heavy-tailed characteristics in the distribution of time intervals. In terms of explaining the motivation of learning behavior, the model shows that the decline of learning interest is negatively related to the probability of learning behavior, while the impact of teaching activities is positively related to the probability of learning behavior. In promoting the online learning behavior, strategies such as task-and collaboration-driven instructional design, intelligent technology-supported resource recommendations, and personalized intervention based on learning rules need to be considered to promote the solution of online education quality problems.

关 键 词：在线学习 学习行为 行为规律 人类动力学 建模 

分 类 号：G434[文化科学—教育学]