Identifying students who may experience difficulty in an introductory computer science course

ACM-SE 28 Pub Date : 1990-04-01 DOI:10.1145/98949.98988

Ted W. Goodman

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引用次数: 4

Abstract

A high percentage of the students in introductory computer science (CS1) courses either drop out or fail to perform at an acceptable level. In past years when students were flocking to computer science, student failure rales were not viewed as a problem. Now that student interest in computer science has declined significantly, departments are faced with the problem of how to increase retention rates without compromising the quality of the course. This can be partially accomplished by identifying those students who may experience difficulty and then closely monitoring and assisting these students. This study is concerned with the question: Is it possible to develop a simple and reasonably effective procedure for identifying those students who may experience difficulty in the CS1 course? Prior studies have attempted to predict success In CS1 courses with mixed results. Of these studies, the one most closely related to the present study was conducted by Fowler and Glorefeld [4]. In this study, college GPA, number of math courses, SAT math score, and age were used to develop a model which correctly classified 81 per cent of the students in CS1 courses into two categories, those who received a grade of A or B, and those who received a grade of C, D, or F. Thirty students from two sections of a CS1 course using Pascal at a comprehensive university participated in the present study during the 1988 Fall semester. The final grade was used as a measure of success in the course and was used to classify the students into two groups according to the degree of difficulty that they experienced in the course. Those who received a final grade of Cor lower were placed in a "high difficulty" group while those who received a final grade of C or higher were placed in a "low difficulty" group. The predictors consisted of classification (CLASS), reason for taking the course (UHY), and four tests covering mathematics background (MATH), logical

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识别在计算机科学入门课程中可能遇到困难的学生

在计算机科学入门(CS1)课程中，有很高比例的学生要么辍学，要么表现不佳。在过去的几年里，当学生们涌向计算机科学时，学生的不及格率并没有被视为一个问题。现在学生对计算机科学的兴趣明显下降，院系面临着如何在不影响课程质量的情况下提高留校率的问题。这可以通过识别那些可能遇到困难的学生，然后密切监测和帮助这些学生来部分完成。本研究关注的问题是:是否有可能开发一种简单而合理有效的程序来识别那些在CS1课程中可能遇到困难的学生?先前的研究试图预测CS1课程的成功，结果好坏参半。在这些研究中，与本研究最密切相关的是Fowler和Glorefeld[4]。在这项研究中，大学GPA、数学课程数量、SAT数学成绩和年龄被用来开发一个模型，该模型正确地将81%的CS1课程的学生分为两类，一类是获得a或B的学生，另一类是获得C、D或f的学生。1988年秋季学期，一所综合性大学使用Pascal的CS1课程的两个部分的30名学生参加了本研究。最终成绩被用作课程成功的衡量标准，并根据学生在课程中经历的困难程度将他们分为两组。那些最终得分低于Cor的人被分到“高难度”组，而那些最终得分为C或更高的人被分到“低难度”组。预测因子包括分类(CLASS)、选课原因(UHY)和四项测试，包括数学背景(MATH)、逻辑(logic)

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ACM-SE 28

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