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Increasing URM Undergraduate Student Success through Assessment-Driven Interventions: A Multiyear Study Using Freshman-Level General Biology as a Model System

机译:通过评估驱动的干预措施提高URM本科生的成功率:一项以新生水平的通用生物学为模型系统的多年研究

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Xavier University of Louisiana leads the nation in awarding BS degrees in the biological sciences to African-American students. In this multiyear study with ~5500 participants, data-driven interventions were adopted to improve student academic performance in a freshman-level general biology course. The three hour-long exams were common and administered concurrently to all students. New exam questions were developed using Bloom’s taxonomy, and exam results were analyzed statistically with validated assessment tools. All but the comprehensive final exam were returned to students for self-evaluation and remediation. Among other approaches, course rigor was monitored by using an identical set of 60 questions on the final exam across 10 semesters. Analysis of the identical sets of 60 final exam questions revealed that overall averages increased from 72.9% (2010) to 83.5% (2015). Regression analysis demonstrated a statistically significant correlation between high-risk students and their averages on the 60 questions. Additional analysis demonstrated statistically significant improvements for at least one letter grade from midterm to final and a 20% increase in the course pass rates over time, also for the high-risk population. These results support the hypothesis that our data-driven interventions and assessment techniques are successful in improving student retention, particularly for our academically at-risk students.
机译:路易斯安那州的泽维尔大学(Xavier University of Louisiana)在授予非裔美国学生生物科学学士学位方面名列全美之首。在这项大约5500名参与者的多年研究中,采用了数据驱动的干预措施,以提高新生水平的普通生物学课程的学生学习成绩。三小时的考试很普遍,并且对所有学生同时进行。使用Bloom的分类法开发了新的考试问题,并使用经过验证的评估工具对考试结果进行了统计分析。除全面期末考试外,所有其他考试均退还给学生进行自我评估和补救。除其他方法外,还通过在10个学期的期末考试中使用相同的60个问题来监控课程的严格性。对相同的60项期末考试题进行分析后发现,总体平均分数从2010年的72.9%提高到2015年的83.5%。回归分析表明,高风险学生与60个问题的平均水平之间存在统计学上的显着相关性。额外的分析表明,对于高风险人群,从中期到期末至少一个字母的成绩在统计学上有显着改善,并且随着时间的推移,课程及格率提高20%。这些结果支持以下假设:我们的数据驱动型干预和评估技术可以成功地提高学生的保留率,尤其是对于我们处于学习风险中的学生。

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