AI-Based Learning Style Prediction in Online Learning for Primary Education
AI-Based Learning Style Prediction in Online Learning for Primary Education
复制标题
小学教育在线学习中基于人工智能的学习风格预测
作者：
B. Pardamean;T. Suparyanto;T. W. Cenggoro;D. Sudigyo;A. Anugrahana
由于信息技术的发展，在线学习得到了广泛的应用。但是，目前还没有针对小学生的评价和应用。所有学习方面的创新努力都是为了提高教育质量，为学生创造积极的学习氛围。通过选择适合学生学习风格的学习材料，可以提高学生在教学过程中的参与度。该研究旨在开发和衡量基于人工智能（AI）的学习风格预测模型在小学生在线学习门户网站中的影响。研究对象为印尼小学四年级至六年级学生。为了实现个性化学习的原则，在线学习门户中的AI模型被设计为推荐适合学生学习风格的学习材料。我们制定了一种新的人工智能方法，使基于协作过滤的人工智能模型能够由学习风格预测驱动。通过这种人工智能算法，在线学习门户网站可以根据每个学生的学习风格提供量身定制的材料推荐。AI模型性能测试取得了令人满意的结果，在1到5的评分范围内，平均RMSE（均方根误差）为0.9035。通过对269名被试的测试前和测试后成绩的t检验分析，学生的学习成绩得到了提高。
Online learning has been widely applied due to developments in information technology. However, there has no evaluation and application for primary school students. All innovation efforts in learning are directed at improving the quality of education by creating an active learning atmosphere for students. Students’ participation in the teaching-learning process can be improved by selecting appropriate learning materials suitable to the student’s learning style. The research aims to develop and measure the impact of an Artificial-Intelligence (AI)-based learning style prediction model in an online learning portal for primary school students. The subjects were recruited from Indonesian primary school students in grades 4 to 6. To fulfill the principle of personalized learning, the AI model in the online learning portal was designed to recommend learning materials that suit students’ learning styles. We formulated a new AI approach that enables collaborative filtering-based AI models to be driven by learning style prediction.With this AI algorithm, the online learning portal can provide material recommendations tailored specifically to the learning style of each student. The AI model performance test achieved satisfactory results, with an average RMSE (Root Mean Squared Error) of 0.9035 from a rating scale of 1 to 5. Moreover, students’ learning performance was improved based on the results of t-test analysis on 269 subjects between the pre-test and post-test scores.