In a comment exchange on the Cost of College post Trying to teach the enigmatic and increasingly popular skill of critical thinking, CSProfMom and I were discussing the relationship between critical thinking and the various modes of thinking used in CS and engineering. I’ll pull out some of the highlights of the discussion to set the stage for this post, but I’m leaving some things out, so go read the original post and comments.
Grace, the author of the post, presented some of the definitions of critical thinking she had found, and CSProfMom replied with
I do not like these definitions of critical thinking because they are only based on verbal reasoning. Mathematical and computational problem solving are utterly ignored; yet I think more critical thinking goes on in those areas than in fields like literary analysis.
Grace defended the definitions, and CSProfMom responded with CMU’s definition of computational thinking:
Computational thinking means creating and making use of different levels of abstraction, to understand and solve problems more effectively.
Computational thinking means thinking algorithmically and with the ability to apply mathematical concepts such as induction to develop more efficient, fair, and secure solutions.
Computational thinking means understanding the consequences of scale, not only for reasons of efficiency but also for economic and social reasons.
I weighed in with
I think that CSProfMom’s point is that “critical thinking” is generally defined rather narrowly and generically, and so misses the important thinking styles that are crucial to some fields. “Computational thinking” is one that is missing. One I see students not getting in most of their college classes is “engineering thinking” or “systems thinking”—dividing difficult problems into simpler subproblems with clearly defined interactions between the subproblems. Although one can argue that these specific modes of thinking are somehow subsumed in “critical thinking”, classes that purport to develop critical thinking skills don’t generally develop these important modes of thinking.
CSProfMom responded with
I think there is a lot of overlap between “computational thinking”, “mathematical thinking”, and “systems thinking”. Abstraction and decomposition are key skills in all three. Your description “dividing difficult problems into simpler subproblems with clearly defined interactions” is absolutely critical in computer science. Maybe computational thinking is simply systems thinking + algorithms?
In any case, because the “critical thinking” term does not include this idea of systems thinking, we see students arrive into our engineering/CS programs utterly unable to think in this manner. I believe that is a major reason why we see the terrible attrition rates in these programs.
The rest of this post will be an expansion on the comment I left in response to this.
There are several different terms floating around in our discussion, and I’d like to pull them out for closer examination:
- critical thinking
- This seems to be a subset of the medieval trivium (grammar, logic, and rhetoric), leaving out the grammar and being a bit light on the rhetoric. It doesn’t even cover modern mathematical logic, but only the simplest Aristotelian logic. The Wikipedia article on the trivium even points to the critical thinking article, which collects nine conflicting definitions of critical thinking, none of which include the other concepts that I list below, except in the vaguest ways.
- mathematical thinking
- Mathematical thinking is about setting up formal systems of rules and examining their properties very closely. Proofs are a major component of mathematical thinking, which has a much more formal and unforgiving definition of proof than other fields. Computation has created a lot of new formal systems to study, and has been a fruitful area recently for mathematicians, just as physics was in previous centuries. Theoretical computer science is basically a branch of mathematics, involving primarily mathematical thinking.
- scientific thinking
- Scientific thinking studies the real world, constructing models of how it functions and testing the models empirically. Different branches of science differ in how much they are model-driven and how much they are data-driven. Physics is highly model-driven, with the models often coming out 40 or 50 years in advance of the data (see Higgs boson). Biology is highly data-driven often with non-quantitative verbal stories as the models. The key concept throughout science is empirical validation of predictive models.
- engineering thinking
- Engineering is about designing new things. An engineering question is more of the form “how can I make this work?” rather than the science question “how does this work?” I’ve talked about the distinction between science and engineering in one of my early blog posts, so I won’t belabor the point here. Although scientists and engineers often wander back and forth between scientific and engineering thinking, the two are really distinctly different modes of thought.
- systems thinking
- Systems thinking is an important component of engineering thinking, consisting of dividing difficult problems into simpler subproblems with clearly defined interactions between the subproblems. But systems thinking cuts across many fields, including mathematical thinking and scientific thinking.
- Computer programming is one of the best subjects to teach systems thinking in, because computer languages provide formal (though still inadequate) ways of representing the modules that encapsulate the subproblems and the interactions between them. Electrical engineers try to do the same thing with their block diagrams, but these formalize a little less of the interactions, relying on English-language descriptions that are often omitted or poorly written.
- Unfortunately, many of the lower-level computer science classes have the faculty or textbook authors do all the systems thinking for the students, so that the students never learn to do it themselves. The scaffolding put in place to help the students find good solutions is where all the systems thinking happened, and descaffolding so that students have to learn to subdivide difficult problems into easier ones is an essential, but often missing, component of teaching programming.
- The “multiple levels of abstraction” mentioned in the CMU definition of computational thinking is really about systems thinking, as each subproblem gets broken down into still smaller problems.
- algorithmic thinking
- Algorithmic thinking is coming up with very precise recipes for doing things—not just flailing around trying things, but having very specific methods that can be shown to work (and work efficiently). Algorithmic thinking is really a branch of mathematical thinking, interested in provably correct manipulations in formal rule systems. Originally it was applied to computing numerical functions, first manually and later by machine, but now has been expanded to cover many different types of data that can be represented in computers. This is the second part of the CMU definition of computational thinking.
- computational thinking
- I don’t like the CMU definition of computational thinking, as they seem to have picked up definitions of mathematical, systems, and algorithmic thinking, and missed the computational part entirely. Computational thinking, to me, involves using computation to solve problems (data analysis, algorithmic solution of symbolic problems, numerical simulation, …) and may not involve much systems thinking or algorithmic thinking—someone else may have done that for you to enable you to use a computational tool. Using Google to search for information is computational thinking, albeit at a rather low level.
- statistical thinking
- Statistical thinking is distinct from all of the above, though it is often important in scientific thinking. Statistical thinking involves reasoning about data that comes from random processes, or that can be modeled as having been corrupted by random noise. Notions of probability, sample size, statistical significance, multiple-hypothesis correction, correlation, and causation are all part of statistical thinking, which has applications to decision making in all aspects of life.
Obviously, there are overlaps and intersections between these different modes of thought (proofs done with the aid of a computer are a combination of mathematical and computational thinking, for example), but there are important differences also. For example, Engineering thinking is not just systems thinking, but includes attention to fine details in the edge conditions (a hallmark of mathematical thinking), making allowances for variations in manufacturing (statistical thinking), testing how the device works in the real world (scientific thinking), and, very often these days, figuring out how to use cheap microcontrollers to do tasks that traditionally were done with more expensive analog devices (computational thinking).
The UCSC general education requirements (see my blog post on general education) recognize mathematical reasoning, scientific inquiry, and statistical reasoning as distinct parts of general education, adding textual analysis and cross-cultural analysis to cover what is often lumped under “critical thinking”. They did not include anything that guarantees exposure to systems thinking, and they tossed in a few other things, some of which seem to me to be more politically expedient choices or fashion following than fundamental modes of thinking, but a general education system is always a compromise between different beliefs about what a university education should mean. I think they did a better job of defining the basis for their general education system than most universities manage.
There have been a lot of calls for more education in “critical thinking” lately. I’m not really happy with these calls, because teaching only a weakened version of the medieval trivium instead of more powerful modern forms of thinking does not educate students properly.