基于多粒度粗糙集的大学生学习能力研究

Research on College Students’ Learning Ability Based on Multi-Granularity Rough

摘要: 学习能力是在校大学生的核心竞争力。大学生学习能力培养是高等教育改革与发展的使命之一，是提高大学生综合竞争力的重要途径。然而，影响大学生学习能力的因素众多并且复杂，从哪些方面提高在校学生学习能力成为一个难题。为了辅助高校更好地提高教学效果，帮助学生提高自身的学习能力，设计了大学生学习能力调查问卷并向在校大学生发放，从而整理获得基础数据，然后在设计的粗糙集属性约简算法的基础之上，利用多粒度粗糙集理论的悲观粒度约简算法和乐观粒度约简算法，从多粒度的角度从众多因素中挖掘出影响大学生学习能力的关键因素，从而辅助学校优化现有的教学体制和制定相关学习策略，有针对性地帮助大学生提高自身的学习能力，提高学生的核心竞争力。

Abstract: Learning ability is the core competitiveness of college students. The cultivation of college students’ learning ability is one of the missions of the reform and development of higher education and an important way to improve the comprehensive competitiveness of college students. However, there are many factors that affect students’ learning ability, which is more complex. How to improve students’ learning ability in school has become a problem. On the one hand, teachers and schools can’t make targeted improvement in teaching methods and improve teaching level; on the other hand, students can’t take targeted improvement to improve their learning ability. In order to help colleges and universities to improve their teaching achievements and learning ability, based on the survey data of college students’ learning ability questionnaire, using the pessimistic granularity reduction algorithm and optimistic granularity reduction algorithm of multi-granularity rough set theory, the key factors affecting college students’ learning ability are mined out from the perspective of multi granularity, so as to assist schools to optimize existing teaching system and make relevant learning strategies to improve the core competitiveness of students.