The Structure and Interpretation of the Computer Science Curriculum


The Structure and Interpretation of the Computer Science Curriculum is a 14-page paper published in 2004 by Matthias Felleisen, Robert Bruce Findler, Matthew Flatt and Shriram Krishnamurthi comparing and contrasting the pedagogical focus of How to Design Programs with that of Structure and Interpretation of Computer Programs.
The paper introduces the pedagogical landscape surrounding the publication of SICP. The paper starts with a history and critique of SICP, followed by a description of the goal of the computing curriculum. It then describes the principles of teaching behind HtDP; in particular, the difference between implicit vs. explicit teaching of design principles. It then continues on to describe the role of Scheme and the importance of an ideal programming environment, and concludes with an extensive evaluation of content and student/faculty reaction to experience with SICP vs. HtDP.
One of the major focuses of the paper is the emphasis on the difference in required domain knowledge between SICP and HtDP. A chart in the paper compares major exercises in SICP and HtDP, and the related text describes how the exercises in the former require considerably more sophisticated domain knowledge than those of HtDP. The paper continues on to explain why this difference in required domain knowledge has resulted in certain students having confused domain knowledge with program design knowledge.
The paper claims the following four major efforts that the authors of HtDP have made to address perceived issues with SICP:
1) HtDP addresses explicitly, rather than implicitly, how programs should be constructed.
2) To make programming easier, the book guides students through five different knowledge levels corresponding to data definition levels of complexity.
3) The book's exercises focus on program design guidelines, rather than domain knowledge.
4) The book assumes less domain knowledge than that of SICP.
The paper then distinguishes between structural recursion, where the related data definition happens to be self-referential, requiring usually a straightforward design process, and generative recursion, where new problem data is generated in the middle of the problem-solving process and the problem solving method is re-used, often requiring ad hoc mathematical insight, and stresses how this distinction makes their approach scalable to the object-oriented world.
Finally, the paper concludes with a description of responses from various faculty and students after having used HtDP in the classroom.