A biased attempt at measuring failure rates in introductory programming

Do students fail intro CS at higher rates than in comparable classes (e.g., intro Physics, or Calculus, or History)?  We’ve been trying to answer that question for years.  I studied that question here at Georgia Tech (see my Media Computation retrospective paper at last year’s ICER).  Jens Bennedsen and Michael Caspersen answered that question with a big international survey (see paper here).  They recognized the limitations of their study — it was surveying on the SIGCSE member’s list and similar email lists (i.e., to teachers biased toward being informed about the latest in computing education), and they got few responses.

This last year’s ITiCSE best paper awardee tried to measure failure rates again (see link below), by studying published accounts of pass rates.  While they got a larger sample size this way, it’s even more limited than the Bennedsen and Caspersen study:

1. Nobody publishes a paper saying, “Hey, we’ve had lousy retention rates for 10 years running!”  Analyzing publications means that you’re biasing your sample to teachers and researchers who are trying to improve those retention rates, and they’re probably publishing positive results.  You’re not really getting the large numbers of classes whose results aren’t published and whose teachers aren’t on the SIGCSE members list.
2. I recognized many of the papers in the meta-analysis.  I was co-author on several of them.  The same class retention data appeared in several of those papers.  There was no funny business going on.  We reported on retention data from our baseline classes.  We then tried a variety of interventions, e.g., with Media Computation and with Robotics.  The baseline then appears in both papers.  The authors say that they made sure that that didn’t double count any classes that appeared in two papers, but I can’t see how they could possibly tell.
3. Finally, the authors do not explicitly cite the papers used in their meta-analysis.  Instead, they’re included on a separate page (see here).  SIGCSE shouldn’t publish papers that do this.  Meta-analyses should be given enough pages to list all their sources, or they shouldn’t be published.   Including them on a separate page makes it much harder to check the work, to see what data got used in the analysis.  Second, they are referencing work that won’t appear in any reverse citation indices or in the authors’ H-index calculations.  I know some of the authors of those papers who are up for promotion or tenure decisions this coming year.  Those authors are having impact through this secondary publication, but they are receiving no credit for it.

This paper is exploring an important question, and does make a contribution.  But it’s a much more limited study than what has come before.

Whilst working on an upcoming meta-analysis that synthesized fifty years of research on predictors of programming performance, we made an interesting discovery. Despite several studies citing a motivation for research as the high failure rates of introductory programming courses, to date, the majority of available evidence on this phenomenon is at best anecdotal in nature, and only a single study by Bennedsen and Caspersen has attempted to determine a worldwide pass rate of introductory programming courses.In this paper, we answer the call for further substantial evidence on the CS1 failure rate phenomenon, by performing a systematic review of introductory programming literature, and a statistical analysis on pass rate data extracted from relevant articles. Pass rates describing the outcomes of 161 CS1 courses that ran in 15 different countries, across 51 institutions were extracted and analysed. An almost identical mean worldwide pass rate of 67.7% was found. Moderator analysis revealed significant, but perhaps not substantial differences in pass rates based upon: grade level, country, and class size. However, pass rates were found not to have significantly differed over time, or based upon the programming language taught in the course. This paper serves as a motivation for researchers of introductory programming education, and provides much needed quantitative evidence on the potential difficulties and failure rates of this course.

via Failure rates in introductory programming revisited.