摘要
"互联网+教育"时代的来临使得e-Learning学习模式被广泛接受,如何为e-Learning学习者提供个性化学习支持服务成为学界关注的焦点。自适应学习路径能够根据学习者特征,为其推荐个性化的学习资源与学习活动序列,是实现个性化学习的重要手段。为提升自适应学习路径构建的智能化程度,提出了包含学习者模型库、学习过程数据库、自适应学习路径构建引擎等核心功能模块的人工智能支持下的自适应学习路径构建模型。在该模型的实现过程中,首先,从认知风格及知识水平两个维度对学习者特征进行向量化描述和相似度计算;而后,提取相似学习者群体的历史学习路径和测试成绩构建学习路径图谱;最后,采用改进的蚁群算法从学习路径图谱中挖掘出最优学习路径推荐给目标学习者。实验结果表明:该方法可以从繁复的学习资源和活动中生成简洁、精准的自适应学习路径,既能有效解决学习者的学习迷航与认知过载问题,还能促进学习资源的高效利用;通过该方法构建的自适应学习路径可有效提升学习者的学习效率、学习成绩和学习满意度,有利于学习者对知识的主动建构、内化及迁移。
With the advent of"Internet+Education"era,e-learning has been widely accepted,and how to provide personalized learning support services for e-learning learners has become the focus of the academic attention.The adaptive learning path can recommend personalized learning resources and learning activities sequences to learners according to their characteristics,which is an important means to achieve personalized learning.In order to construct adaptive learning path intelligently,an adaptive learning path construction model supported by artificial intelligence is proposed,which includes learners’model library,learning process database,adaptive learning path construction engine and other core functional modules.In the implementation of the model,firstly,the cognitive style and knowledge level are used to describe learners’characteristics and do similarity calculation;then,the similar learners’historical learning paths and test scores are extracted to construct the learning path map;finally,an improved ant colony algorithm is used to mine the optimal learning path from the learning path map for the target learners.The experimental results show that the proposed model can generate a concise and accurate adaptive learning path from the complex learning resources and activities,which can not only effectively solve the problems of learning confusion and cognitive overload,but also promote the efficient use of learning resources.Moreover,the adaptive learning path constructed by this model can greatly improve learners’learning efficiency,learning performance and learning satisfaction,which facilitates learners to construct,internalize and transfer knowledge.
作者
孔维梁
韩淑云
张昭理
KONG Weiliang;HAN Shuyun;ZHANG Zhaoli
出处
《现代远程教育研究》
CSSCI
北大核心
2020年第3期94-103,共10页
Modern Distance Education Research
基金
教育部人文社会科学研究项目“网络学习共同体意见领袖的形成机制及优化策略研究”(19YJC880049)
河南师范大学博士启动课题“面向学科领域的自适应学习模型构建及应用研究”(qd14191)。
关键词
人工智能
自适应学习路径
个性化学习
学习风格
知识水平
改进蚁群算法
Artificial Intelligence
Adaptive Learning Path
Personalized Learning
Learning Style
Knowledge Level
Improved Ant Colony Algorithm