CER: Factors Influencing College Success in Information Technology (FICSIT)

哈佛大学将进行一项回溯性队列研究来分析大学生？在控制人口统计学差异的同时，教育背景和以前的IT相关经验是学生在大学学习计算机科学时表现的预测因素。这项研究将揭示高中教师在科学和数学教学中使用技术的决定以及越来越多的高中计算机科学课程与全国12000名大学计算机科学学生的表现之间的关系，这些学生将从随机选择的两年制和四年制学院和大学中招募。这项研究有能力揭示和评估最有希望的教育实践和干预措施的使用，包括校内计算教学和课后计划、比赛和俱乐部，并评估高中时期的经历与大学计算机科学入门课程的演变情况之间的匹配的影响。这些知识只能帮助缓解过早放弃攻读潜在计算机科学学位的高失败率，尤其是对女性和代表性不足的少数族裔来说，他们历史上比其他同学更早、更多地选择退出这一领域。大学和高中的计算机科学教师也可以从了解有助于学生有效准备的因素中受益。大学第一门计算机科学课程。

Harvard University will undertake a retrospective cohort study to analyze college students? education background and previous IT-related experiences for predictors of performance when students study Computer Science in college, while controlling for demographic differences. The study will reveal how decisions concerning the use of technology made by high school teachers in the teaching of science and mathematics and the growing number of high school computer science courses relate to the performance of 12,000 college computer science students nationwide, who will be recruited from randomly selected 2-year and 4-year colleges and universities. This study has the capacity to both reveal and evaluate the use of the most promising educational practices and interventions, including both in-school computing instruction and after-school programs, competitions, and clubs, and to assess the effects of a match between experiences during the high school years with the evolving landscape of introductory college computer science courses. Such knowledge can only help to ameliorate the high failure rates that prematurely end the pursuit of a potential computer science degree, especially for females and under-represented minorities who historically opt out of this field earlier and in greater numbers than other classmates. Both college and high school computer science instructors can also benefit from understanding the factors contributing to effective preparation for students? first college computer science course.