利用关联规则挖掘构建智能教务系统
Building A Smart Academic Advising System Using Association Rule Mining
摘要：
在学术环境中，学生咨询被认为是导师和学生提高学生学习成绩的最重要活动。在学生人数众多的大学中，咨询是一项耗时的活动，可能需要顾问和大学管理部门做出相当大的努力来指导学生成功有效地完成注册。目前的系统是传统的，在很大程度上取决于顾问的努力，以找到最好的课程选择，以提高学生的表现。需要一个智能系统，可以建议大量的学生每学期。在本文中，我们提出了一个智能系统，使用关联规则挖掘，以帮助学生和顾问在选择和优先级的课程。该系统通过建议满足学生当前需求的课程来帮助学生提高成绩，同时提高他们的学业成绩。该系统使用关联规则挖掘，以找到已注册的学生在许多以前的学期课程之间的关联。该系统成功地生成了一个关联规则列表，指导特定的学生选择由类似的学生注册的课程。
英文摘要：
In an academic environment, student advising is considered a paramount activity for both advisors and student to improve the academic performance of students. In universities of large numbers of students, advising is a time-consuming activity that may take a considerable effort of advisors and university administration in guiding students to complete their registration successfully and efficiently. Current systems are traditional and depend greatly on the effort of the advisor to find the best selection of courses to improve students performance. There is a need for a smart system that can advise a large number of students every semester. In this paper, we propose a smart system that uses association rule mining to help both students and advisors in selecting and prioritizing courses. The system helps students to improve their performance by suggesting courses that meet their current needs and at the same time improve their academic performance. The system uses association rule mining to find associations between courses that have been registered by students in many previous semesters. The system successfully generates a list of association rules that guide a particular student to select courses registered by similar students.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
