It is a common conception that CS1 is a very difficult course and that failure rates are high. However, until now there has only been anecdotal evidence for this claim. This article reports on a survey among institutions around the world on failure rates in introductory programming courses. The article describes the design of the survey and the results. The number of institutions answering the call for data was unfortunately rather low, so it is difficult to make firm conclusions. It is our hope that this article can be the starting point for a systematic collection of data in order to find solid proof of the actual failure and pass rates of CS1.

It is a common conception that CS1 is a very difficult course and that failure rates are high. However, until now there has only been anecdotal evidence for this claim. This article reports on a survey among institutions around the world on failure rates in introductory programming courses. The article describes the design of the survey and the results. The number of institutions answering the call for data was unfortunately rather low, so it is difficult to make firm conclusions. It is our hope that this article can be the starting point for a systematic collection of data in order to find solid proof of the actual failure and pass rates of CS1.

We present a brief overview of a model for the human cognitive architecture and three learning theories based on this model: cognitive load theory, cognitive apprenticeship, and worked examples (a key area of cognitive skill acquisition). Based on this brief overview we argue how an introductory object-oriented programming course is designed according to results of cognitive science and educational psychology in general and cognitive load theory and cognitive skill acquisition in particular; the principal techniques applied are: worked examples, scaffolding, faded guidance, cognitive apprenticeship, and emphasis of patterns to aid schema creation and improve learning. As part of the presentation of the course, we provide a characterization of model-driven programming ⎯the approach we have adopted in the introductory programming course. The result is an introductory programming course emphasizing a pattern-based approach to programming and schema acquisition in order to improve learning.

Computer science educators generally agree that abstract thinking is a crucial component for learning computer science in general and programming in particular. We report on a study to confirm the hypothesis that general abstraction ability has a positive impact on programming ability. Abstraction ability is operationalized as stages of cognitive development (for which validated tests exist). Programming ability is operationalized as grade in the final assessment of a model-based objects-first CS1. The validity of the operationalizations is discussed. Surprisingly, our study shows that there is no correlation between stage of cognitive development (abstraction ability) and final grade in CS1 (programming ability). Possible explanations are identified.

In order to improve the course design of a CS1 model-driven programming course we study potential indicators of success for such a course. We explain our specific interpretation of objects-first.Of eight potential indicators of success, we have found only two to be significant at a 95% confidence interval: math grade from high school and course work. The two significant indicators explain 24.2% of the variation of the exam grade. The result concerning math grade contradicts earlier findings.We discuss four aspects of our research: the explanation power of the potential success indicators, the impact of our findings on teaching, limits of what to conclude from the available data, and the variety of the notion "objects-first".Because of the variety of interpretations of "objects-first", the present research is necessary as a supplement to earlier research in order to make generalizable results on the success factors for objectsfirst programming.