摘要
在线学习为众多学习者提供了开放灵活的学习机会,却存在着学习者学习积极性不高、学习成绩不理想的问题。已有的在线学习效果预测工作着重从静态角度探究学习行为对成绩的影响,忽略了学习行为随时间的演化规律,缺少对行为背后学习模式和学习动机的深入探讨,而这两者正是影响学习效果的重要因素。为此,提出一种基于学习行为演化的学习模式识别及效果预测方法来建模学习行为与动机对学习效果的影响。首先,依据学习者的付出-收获量化学习效率,按时间构建学习效率动态演化序列;然后,使用高斯混合模型聚类真实学习数据并结合实际学习场景,识别4种典型学习模式;在此基础上,设计学习模式及动机预测模型,结合双向长短期记忆网络,构建学习效果预测模型。利用8门真实课程学习的公开数据,对每一种学习模式学习者的付出、收获演变规律进行细致分析。大量对比实验结果表明所提方法在多个性能指标上提升了6.9%~29.2%。本研究有助于在线学习者、教学者和平台准确理解学习者的学习状态,从而提升在线学习效果。
Online learning provides learners with open and flexible learning opportunities,but suffers low learning engagement and unsatisfactory academic performance.Existing works on academic performance prediction mainly study how behaviors will impact performance from a static perspective,and neglect learners’behavior evolution over time and lack a deep understanding of learning patterns and learners’motivations,which are the key factors in learning performance.Therefore,a method of perfor-mance prediction based on learners’learning pattern and motivation is proposed to model the effects of learners’patterns and motivations on their performances.First,we quantify learning efficiency based on learners’efforts and gains and construct the dynamic evolution sequence of learning efficiency with time.Then,we cluster learners’behavior and identify four typical learning patterns combined with the actual learning scenarios.Based on this,learning pattern recognition and motivation prediction mode-ling are designed.The final performance prediction model is constructed by combining them with the bi-directional long-and short-term memory networks.Furthermore,we conduct a detailed and in-depth data analysis on each type of learning pattern’s efforts and gains in eight online courses.Comparative experiments show that the proposed model performs better on several metrics,with improvements ranging from 6.9%to 29.2%.Our work will help online learners,teachers,and platforms accurately understand learners’learning states and improve online learning performance.
作者
黄春利
刘桂梅
姜文君
李肯立
张吉
任德盛
HUANG Chunli;LIU Guimei;JIANG Wenjun;LI Kenli;ZHANG Ji;TAK-SHING Peter Yum(College of Computer Science and Electronic Engineering,Hunan University,Changsha 410082,China;University of Southern Queensland,Queensland 4350,Australia;Department of Information Engineering,The Chinese University of Hong Kong,Hong Kong 999077,China)
出处
《计算机科学》
CSCD
北大核心
2024年第10期67-78,共12页
Computer Science
基金
国家自然科学基金(62172149)。
关键词
在线学习
行为演化
学习模式识别
学习动机预测
学习效果预测
Online learning
Behavior evolution
Learning pattern recognition
Learning motivation prediction
Learning perfor-mance prediction
