计算机科学
学习分析
个性化学习
机器学习
教育技术
分析
灵活性(工程)
路径分析(统计学)
体验式学习
同步学习
学习风格
人工智能
协作学习
多媒体
合作学习
教学方法
数学教育
开放式学习
数据科学
知识管理
心理学
数学
统计
作者
Lumy Joseph,Sajimon Abraham,Biju P Mani,N. Rajesh
标识
DOI:10.1177/07356331211057816
摘要
A fixed learning path for all learners is a major drawback of virtual learning systems. An online learning path recommendation system has the advantage of offering flexibility to select appropriate learning content. Learning Analytics Intervention (LAI) provides several educational benefits, particularly for low-performing students. Researchers employed an LAI approach in this work to recommend personalised learning paths to students pursuing online courses depending on their learning styles. It was accomplished by developing a Learning Path Recommendation Model (LPRM) based on the Felder–Silverman Learning Style Model (FSLSM) and evaluating its efficacy. The data were analysed with the help of a dataset from the Moodle Research Repository, and different learning paths were recommended using a sequence matching algorithm. The effectiveness of this approach was tested in two groups of learners using the independent two-sample t-test, a statistical testing tool. The experimental evaluation revealed that learners who followed the suggested learning path performed better than those who followed the learning path without any recommendations. This enhanced learning performance exemplifies the effects of learning analytics intervention .
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