Priming Computer Science Students for Success

随着对计算机科学毕业生的需求激增，“引导计算机科学学生走向成功”通过运用精通学习和指导来解决计算机科学的高流失率问题，针对课程中两个公认的绊脚石：计算机科学入门课程（CS1）和四门必修的数学课程。该项目将为在拉马波学院学习计算机科学的两组8名学生提供为期五年的奖学金。参与的学者将有机会参与本科生研究、编程竞赛、同伴辅导、与技术相关的校园俱乐部和校友指导。Ramapo将从当地的城市学区招收学生，这些学区为低收入学生提供服务，同时也将招收长期运行的上进数学科学项目的学生。招聘团队包括两名女教师，她们是培养女学生对计算机科学兴趣的宝贵榜样。CS1学者的精通学习建立在“问题”和“epplets”之前的成功基础上，这两个软件辅导套件可以在网络上使用，随时随地促进学习。这些套房已被证明可以将女性的自我效能感提高到男性的水平。导师会自动为学生的解决方案打分，并提供逐步的反馈，帮助学生学习。设计为课后在线使用，每个学生花尽可能多的时间在练习上，直到他们掌握了指定的概念。参加CS1的学者受益于认知学徒式教学。当使用这种方法时，教师首先模拟与概念相关的问题解决过程，然后使用苏格拉底式提问引导学生在监督下走向正确的解决方案，并逐渐淡出支持，同时要求学生越来越依赖自己的知识和经验。在数学课程中遇到学业困难的S-STEM学生将与一名数学教师导师配对，该导师每周将花一小时的时间与学生一起使用认知学徒模式解决问题。该项目将产生关于精通学习和认知学徒式方法在减少计算机科学专业人员流失方面的有效性的知识。研究结果将在ACM SIGCSE， icticse， CCSCNE和IEEE FIE等国家和地区会议上发表。

As the demand for computer science graduates skyrockets, "Priming Computer Science Students for Success" tackles the large attrition rate in computer science by applying mastery learning and mentoring to target two universally acknowledged stumbling blocks in the curriculum: the introductory computer science course (CS1) and four required mathematics courses. The project will provide four-year scholarships to two cohorts of eight students studying computer science at Ramapo College over five years. Participating scholars will have the opportunity to participate in undergraduate research, programming contests, peer-tutoring, technology-related campus clubs, and alumni mentoring. Ramapo will recruit students from local urban school districts serving low-income students as well as a long-running Upward Bound Math Science program. The recruitment team includes two female faculty, valuable role models for cultivating interest in computer science among female students.Mastery learning for the CS1 scholars builds upon the prior success of "problets" and "epplets," two software tutoring suites available for use over the web that facilitate learning anytime anywhere. These suites have been shown to raise the level of self-efficacy of women to that of men. The tutors automatically grade student solutions and provide step-by-step feedback that helps students learn. Designed to be used online after class, individual students spend as much time as needed with the exercises until they have mastered the assigned concepts. Scholars enrolled in CS1 benefit from cognitive-apprenticeship-style teaching. When using this approach, the instructor first models the problem-solving process related to a concept, then uses Socratic questioning to steer students toward the correct solution under supervision, and gradually fades support while requiring students to rely increasingly on their own knowledge and experience. S-STEM scholars experiencing academic distress in mathematics courses will be paired with a mathematics faculty mentor who will spend an hour each week solving problems with the student using a cognitive-apprenticeship model. The project will generate knowledge on the effectiveness of mastery learning and the cognitive-apprenticeship-style approach in reducing attrition in the computer science major. Results will be presented at national and regional conferences such as ACM SIGCSE, ITiCSE, and CCSCNE, and IEEE FIE.