AI-based College Course Selection Recommendation System: Performance Prediction and Curriculum Suggestion

Yu-Hsuan Wu, Eric Hsiao-Kuang Wu
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引用次数: 3

Abstract

Recent advances of AI applications in various of industries have led to remarkable performance and efficiency. Driven by the great success of datasets and experience sharing, people are exploring more precious datasets with diverse features and longer time range. The promising reasoning information of well-curated student grade datasets is expected to assist young students to find the best of themselves and then improve their learning outcome and study experience. Through data and experience sharing, young students can have a better understanding of their learning condition and possible learning outcomes. Existing course selection systems in Taiwan which offer limited basic enrolling functions fail to provide performance prediction and course arrangement guidance based on their own learning condition. Students now selecting courses with unawareness of their expecting performance. A personalized guide for students on course selection is crucial for how they structure professional knowledge and arrange study schedule. In this paper, we first analyzed what factors can be used on defining learning curve, and discovered the difference between students with different properties and background. Second, we developed a recommendation system based on great amount of grade datasets of past students, and the system can give students suggestions on how to assign their credits based on their own learning curve and students that had similar learning curve. The result of our research demonstrates the feasibility of a new approach on applying big data and AI technology on learning analysis and course selection.
基于人工智能的大学选课推荐系统:成绩预测与课程建议
近年来,人工智能在各个行业的应用取得了长足的进步,取得了令人瞩目的性能和效率。在数据集和经验共享取得巨大成功的推动下,人们正在探索更多具有不同特征和更长的时间范围的珍贵数据集。经过精心策划的学生成绩数据集的有希望的推理信息,有望帮助年轻学生找到最好的自己,从而提高他们的学习成果和学习体验。通过数据和经验分享,年轻学生可以更好地了解自己的学习状况和可能的学习成果。台湾现有的选课系统仅提供有限的基本招生功能,未能提供基于自身学习状况的成绩预测和课程安排指导。现在的学生在选课时没有意识到自己的预期成绩。个性化的选课指导对学生如何构建专业知识和安排学习计划至关重要。在本文中,我们首先分析了哪些因素可以用来定义学习曲线,并发现了不同性质和背景的学生之间的差异。其次,我们基于大量过去学生的成绩数据集开发了一个推荐系统,系统可以根据学生自己的学习曲线和有相似学习曲线的学生给出如何分配学分的建议。我们的研究结果证明了将大数据和人工智能技术应用于学习分析和课程选择的新方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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