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Data mining design for improving STEM student retention
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The persistent difficulty of retaining college students through graduation has become a global problem. The purpose of this quantitative, descriptive, and retrospective study was to apply data mining methods, tools, and algorithms to analyze enrollment data for issues affecting STEM students' retention at an historically black college (HBCU). The data source was a Johnson C. Smith University (JCSU) database containing demographic data, background data, commitment behavior, and social data. Students' enrollment data rom JCSU were a useful data source to identify students who would most probably...
The persistent difficulty of retaining college students through graduation has become a global problem. The purpose of this quantitative, descriptive, and retrospective study was to apply data mining methods, tools, and algorithms to analyze enrollment data for issues affecting STEM students' retention at an historically black college (HBCU). The data source was a Johnson C. Smith University (JCSU) database containing demographic data, background data, commitment behavior, and social data. Students' enrollment data rom JCSU were a useful data source to identify students who would most probably leave the institution. Results showed data mining approaches explained and predicted retention of all STEM students at JCSU.