Archive for May 3, 2012

Blog Post #999: Research Questions in Computing Education

The 999th blog post feels like a good point to think about where we’re going.  Here’s how I define the big question of computing education research:

Computing education research is the study of how people come to understand computing, and how we can make that better.

But that’s the big question.  There are lots of research questions inside that.  Here are some of the ones that I’m intrigued by.  This is an overly-long blog post which I’m using as a place marker:  Here’s what I’m thinking about right now at the end of the first 1000 blog posts.  Skip around to the parts that you might find interesting.

What are the cognitive processes of learning to program?

Why is learning to program hard? The empirical evidence of teaching computer science suggests that it is. Failure rates worldwide of 30-50% in the first class have been reported for decades. The misconceptions and challenges that students faced in Scratch in Israel (ITICSE 2011) are quite similar to the same ones documented in Pascal at Yale in the 1980’s (Soloway et al.).

Are there cognitive challenges to learning programming that are unique among other disciplines? Perhaps so. Consider these two possibilities:

  • Agency: Writing a computer program is the task of providing instructions to another agent to execute, but a non-human agent. Miller in 1981 found that humans found it hard to describe task processes to another human, and the produced instructions required human understanding to interpret them. People do not naturally produce instructions at a level detailed enough for a computer to execute.
  • Time: A program uses a variety of notations to compress time, e.g., iteration and recursive constructs. These notations describe a process in brief which will execute repeatedly many times (perhaps millions of times). We know that these notations are among the most challenging for students to grasp.

Both agency and time notations are unique to the challenge of programming. Perhaps these factors (among others) help to explain why programming is so hard, and understanding these challenges will lead to new insight into how humans conceive of agency and time.

Where do problems/difficulties/misconceptions in learning programming come from?

Most students have no experience in programming computers before they enter their first computer science class.  So, no prior conception of assignment, memory allocation, WHILE and FOR loops, linked lists, or recursion — yet these are way up there on the list of things that are hard about learning to program.  They haven’t changed in decades, across multiple languages.  Where did those problems come from?  Do we teach them wrong?  Exactly where so that we can fix it!  Do students have some prior knowledge that is interfering?  What knowledge are students bringing to bear in learning to program?

Can we teach computing without a programming language?
Can someone learn what a computer is, how it works, and what its limitations are simply through non-programming activities?

Mathematicians did. Turing defined what a computer is, without a programming language. Instead, he defined a machine and a language.

I’m increasingly coming to believe that those are outliers — Turing and mathematicians who figure out computing without a computer are unusual, and we can’t do that at-scale.  Learning to understand computing is learning to understand a notional machine (duBoulay), to construct a mental model of how you expect the notional machine to work (Norman), and that mental model consists of decontextualized parts (deKleer and Brown).  It’s very hard to think about those parts without having names or representations of them.  It can happen, but it takes enormous cognitive effort.  It’s not going to be effective and efficient to reach our learning goals without a language.

Challenges for CS10K

The CS10K effort (to have 10,000 high school teachers capable of teaching CS:Principles in 10,000 US high schools) requires answers to some significant research questions. Some of these include:

What kind of pedagogy will fit into the lives of in-service high school teachers and other working professionals?

Computer science pedagogy today is mostly apprenticeship-based: Students get a bit of instruction (perhaps some modeling of good behavior), and then are expected to learn through doing, by programming in front of an IDE. While the apprenticeship-based model is effective, it’s inefficient if the goal is understanding about computer science, as opposed to expertise as a software engineer.

In-service high school teachers are a particularly challenging audience. Most likely, they will never be professional software engineers, and they are full-time (overworked) professions, so they have neither the motivation nor the time to engage in apprenticeship-based learning. How do we teach CS to these teachers in the small bits of time that they have available?

How do we create sufficient, high-quality on-line materials to lead to successful CS learning at a distance?

The best distance learning programs in the world (such as the Open University UK) rely significantly on text-based materials, because we know how to control costs while creating and maintaining high-quality content. CS is not best taught with printed text, since visualizations and simulations play a key role in student learning. How do we create sufficient (e.g., at reasonable cost), high-quality materials to support CS learning at a distance?

What will motivate high school teachers to take classes in computer science, to be engaged with the content, and to sustain their interest?

The existing CS teaching programs in the United States are woefully undersubscribed, e.g., Purdue’s CS methods course has never had more than one student enrolled each term that it is offered. What will drive more teachers into CS education?

What do teachers need in order to develop into successful computer science teachers?

High school teachers will not need to be professional software engineers. They do need to be able to present CS ideas, to assign and assess student work, and to mentor, e.g., to help facilitate student debugging and guide development. What are the learning objectives for CS high school teachers? How do we assess that development?

CS PCK: What is Computer Science Pedagogical Content Knowledge?
In most disciplines, there is a body of knowledge of how to teach that.  How People Learn has a whole chapter on domain-specific teaching practices, and points out that those are much more powerful for effective teaching than domain-general teaching practices.  For example, science educators explain how to support inquiry-based learning, and mathematics educators know how to build on innate understanding of number.  We call that knowledge pedagogical content knowledge.    How do we best teach computer science?  How do we help future educators develop the unique skills to teach computer science?

May 3, 2012 at 6:16 am 18 comments


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