Interacting factors that predict success and failure in a CS1 course

Abstract

The factors that contribute to success and failure in introductory programming courses continue to be a topic of lively debate, with recent conference panels and papers devoted to the subject (e.g. Rountree et al. 2004, Ventura et al., 2004, Gal-Ezer et al., 2003. Most work in this area has concentrated on the ability of single factors (e.g. gender, math background, etc.) to predict success, with the exception of Wilson et al. (2001), which used a general linear model to gauge the effect of combined factors. In Rountree et al. (2002) we presented the results of a survey of our introductory programming class that considered factors (such as student expectations of success, among other things) in isolation. In this paper, we reassess the data from that survey by using a decision tree classifier to identify combinations of factors that interact to predict success or failure more strongly than single, isolated factors.

Predicting Student Exam’s Scores by Analyzing Social Network Data

“…Other methods used in tackling the grade prediction problem are the factor analysis or other classification schemes with statistical analysis [5,10,14,15]. For example, Rountree et al [15] used Decision Trees analysis in order to identify combinations of factors that may assist in predicting success or failure in a CS1 class.…”

“…For example, Rountree et al [15] used Decision Trees analysis in order to identify combinations of factors that may assist in predicting success or failure in a CS1 class. In this paper, we present a method for building a social network of students in a course for the purpose of predicting their final exam grades.…”

A Case Study of the Introduction of Computer Science in NZ Schools

“…Research on teaching introductory programming had been carried out at the university of Otago since the early 2000s, which provided considerable background to inform the design of school programming standards [Robins et al 2003;Rountree et al 2004;Garner et al 2005;Robins 2010].…”

A Study of Learners’ Behaviors in Hands-On Learning Situations and Their Correlation with Academic Performance

“…Research on predictors of success in learning has been a hot topic for decades [1]. Predictors are information about learners and include work style preference, selfefficacy [2], or background and expectations [3]. However, the development of Educational Data Mining and Learning Analytics provides new capabilities to explore learners' behavior and to study its influence on their performance.…”