What guaranteed
evidence does a
working program
provide us
about student
understanding?

derstanding. We posit that the need exists for computing instructors to design assessments more directly targeting understanding, not just doing, computing. And, of course, to adopt teaching approaches that support student development of these skills.

 

grounds, suggest that we are failing to transfer our ways of thinking to a broad audience.

 

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similarities Between Physics and computing education

Moreover, there are important parallels between physics and computing instruction. Similar to physics problem solving, the most engaged part of many computing courses—whether that be operating systems or introductory programming—is when students develop programs embodying the concepts of the course. Many instructors strongly value code writing including out-of-class programming, laboratory assignments, and/or program writing on exams as an assessment of deep understanding of computing concepts.

Is it possible that students “plug and chug” in computing, not really understanding the concepts as we would like them to? That is, are we absolutely certain that, in the process of writing a program that exercises concept X, students fully grasp the behavior of X and the various appropriate decisions regarding X we assume occurred in producing a working program? In short, what guaranteed evidence does a working program provide us about student understanding? We propose it can only assure us that our students can produce a working program— other inferences on our part are mostly that—just inferences.

With the FCI, physics faculty were shown, through a “new” type of assessment question, that their existing assessment approaches did not give them an accurate view of student un-

increasing Deeper understanding

How can one foster deep understanding in the standard educational environment? Fortunately, Mazur did not throw up his hands at his student’s baffling question. He developed a teaching method called Peer Instruction (PI) that has been used in numerous science and mathematics courses. The cornerstone of PI involves students attempting to explain to each other how they understand core physics concepts via a series of deceptively simple-looking problems. The emphasis is not on getting to a right answer via a mechanical process; instead, the right answer is apparent once the students use the appropriate core concepts in their attempts to articulate their understanding of the problem and their solution to it.

In a variety of studies, this approach has been shown to improve learning twofold over the standard lecture format. 1, 2 These dramatic learning effects, as well as PI’s documented use across a variety of disciplines, should make computing educators take note: to foster the desired level of understanding, it is the teaching method that Mazur addressed. Often the computing community seems focused on what to teach, not how to teach it. While content is important, we need to focus more on theoretically and experimentally grounded methods, such as Peer Instruction, which are designed to support the development of deep understanding.

 

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What is Peer instruction? In Peer Instruction, students gain preparatory knowledge before class (for example, through textbook reading) and complete a pre-lecture quiz to both incentivize their preparation and to give them feedback on whether they are ready to learn in a lecture format. During class, lecture is interspersed with or largely replaced by multiple choice questions (MCQs) and discussion. MCQs are designed