Balancing cognition and motivation in computing education: Herbert Simon and evidence-based education

January 6, 2017 at 7:00 am 6 comments

Education is a balancing act between optimally efficient instruction and motivating students. It’s not the same thing to meet the needs of the head and of the heart.

Shuchi Grover tweeted this interesting piece (quoted below) that reviews an article by Herb Simon (and John Anderson and Lynne Reder) which I hadn’t previously heard of.  The reviewer sees Herb Simon as taking a stand against discovery-based, situated, and constructivist learning, and in favor of direct instruction. When I read the article, I saw a more subtle message.  I do recommend reading the review piece linked below.

He [Herbert Simon] rejects discovery learning, and praises teacher instruction

When, for whatever reason, students cannot construct the knowledge for themselves, they need some instruction. The argument that knowledge must be constructed is very similar to the earlier arguments that discovery learning is superior to direct instruction. In point of fact, there is very little positive evidence for discovery learning and it is often inferior (e.g., Charney, Reder & Kusbit, 1990). Discovery learning, even when successful in acquiring the desired construct, may take a great deal of valuable time that could have been spent practicing this construct if it had been instructed. Because most of the learning in discovery learning only takes place after the construct has been found, when the search is lengthy or unsuccessful, motivation commonly flags.

Source: Herbert Simon and evidence-based education | The Wing to Heaven

Some cognitive scientists have been railing against the constructivist and situated approaches to learning for years. Probably the most important paper representing the cognitivist perspective is the Kirschner, Sweller, and Clark paper, “Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching.”  I talked about the Kirschner, Sweller, and Clark paper in this blog post with its implication for how we teach computer science.

The conclusion is pretty straightforward: Direct instruction is far more efficient than making the students work it out for themselves. Students struggling to figure something out for themselves does not lead to deeper learning or more transfer than simply telling students what they ought to do. Drill and practice is important. Learning in authentic, complex situations is unnecessary and often undesirable because failure increases with complexity.

The Anderson, Reder, and Simon article does something important that the famous Kirschner, Sweller, and Clark paper doesn’t — it talks about motivation. The words “motivation” and “interests” don’t appear anywhere in the Kirschner, Sweller, and Clark paper. Important attitudes about learning (like Carol Dweck’s fixed and growth mindsets, or Angela Duckworth’s grit) are not even considered.

In contrast, Anderson, Reder, and Simon understand that motivation is a critical part of learning.

Motivational questions lie outside our present discussion, but are at least as complex as the cognitive issues. In particular, there is no simple relation between level of motivation, on the one hand, and the complexity or realism of the context in which the learning takes place, on the other. To cite a simple example, learning by doing in the real-life domain of application is sometimes claimed to be the optimum procedure. Certainly, this is not true, when the tasks are life-threatening for novices (e.g., firefighting), when relevant learning opportunities are infrequent and unpredictable (e.g., learning to fly a plane in bad weather), or when the novice suffers social embarrassment from using inadequate skills in a real-life context (e.g., using a foreign language at a low level of skill). The interaction of motivation with cognition has been described in information-processing terms by Simon (1967, 1994). But an adequate discussion of these issues would call for a separate paper as long as this one.

There are, of course, reasons sometimes to practice skills in their complex setting. Some of the reasons are motivational and some reflect the special skills that are unique to the complex situation. The student who wishes to play violin in an orchestra would have a hard time making progress if all practice were attempted in the orchestra context. On the other hand, if the student never practiced as a member of an orchestra, critical skills unique to the orchestra would not be acquired. The same arguments can be made in the sports context, and motivational arguments can also be made for complex practice in both contexts. A child may not see the point of isolated exercises, but will when they are embedded in the real-world task. Children are motivated to practice sports skills because of the prospect of playing in full-scale games. However, they often spend much more time practicing component skills than full-scale games. It seems important both to motivation and to learning to practice one’s skills from time to time in full context, but this is not a reason to make this the principal mechanism of learning.

As a constructionist-oriented learning scientist, I’d go further with the benefits of a motivating context (which is a subset of what they’re calling a “complex setting”). When you “figure it out for yourself,” you have a different relationship to the domain. You learn about process, as well as content, as in learning what it means to be a scientist or how a programmer thinks. When you are engaged in the context, practice is no longer onerous but an important part of developing expertise — still arduous, but with meaning. Yasmin Kafai and Quinn Burke talk about changing students’ relationship with technology. Computer science shouldn’t just be about learning knowledge, but developing a new sense of empowerment with technology.

I’ve been wondering about what (I think) is an open research question about cognitivist vs. situationist approaches on lifelong learning. I bet you’re more likely to continue learning in a domain when you are a motivated and engaged learner. An efficiently taught but unmotivated learner is less likely to continue learning in the discipline, I conjecture.

While they underestimate the motivational aspect of learning, Anderson, Reder, and Simon are right about the weaknesses of an authentic context. We can’t just throw students into complex situations. Many students will fail, and those that succeed won’t be learning any better. They will learn slower.

Anderson, Reder, and Simon spend much of their paper critiquing Lave & Wenger’s Situated Learning. I draw on situated learning in my work (e.g., see post here) and reference it frequently in my book on Learner-Centered Computing Education, but I agree with their critique. Lave & Wenger are insightful about the motivation part, but miss on the cognitive part. Situated learning, in particular, provides insight into how learning is a process of developing identity. Lave & Wenger value apprenticeship as an educational method too highly. Apprenticeship has lots of weaknesses: inefficient, inequitable, and difficulty to scale.

The motivational component of learning is particularly critical in computing education. Most of our hot issues are issues of motivation:

The challenge to being an effective computing educator is to be authentic and complex enough to maintain motivation, and to use scaffolding to support student success and make learning more efficient. That’s the point of Phyllis Blumenfeld et al.’s “Motivating Project-Based Learning: Sustaining the Doing, Supporting the Learning.” (I’m in the “et al,” and it’s the most cited paper I’ve ever been part of.) Project-based learning is complex and authentic, but has the weaknesses that the cognitivists describe. Blumenfeld et al. suggest using technology to help students sustain their motivation and support their learning.

Good teaching is not just a matter of choosing the most efficient forms of learning. It’s also about motivating students to persevere, to tell them the benefits that make the efforts worthwhile. It’s about feeding the heart in order to feed the head.

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6 Comments Add your own

  • 1. johnpane  |  January 6, 2017 at 8:23 am

    Nice discussion Mark! Thank you. It seems like occasional discovery learning, “When you ‘figure it out for yourself'”, may have a small role in an optimal learning environment. Getting the balance right is the trick. If you include practice and skill refinement as part of learning, discovery learning doesn’t have to the only learning taking place in a motivational context. I like how Anderson, Reder, and Simon summed it up: “It seems important both to motivation and to learning to practice one’s skills from time to time in full context, but this is not a reason to make this the principal mechanism of learning.”

  • 2. Howard  |  January 6, 2017 at 1:13 pm

    “To everything there is a season, and a time for every purpose”.
    We need students today with better and wider ranging capabilities that require many tools and contexts; many of which are beyond learning that is operationalized through standardized assessments. II read that as an underlying theme in Simons, there is a place for many different pedagogical ideas. Other articles mentioned seem to have overly narrow understandings of cognition or learning theories and also misunderstand the pragmatism that underly Dewey’s ideas on discovery learning. I suspects that such narrow views of learning also underly the problems you noted in your January 2nd post on teaching coding.

    • 3. Mark Guzdial  |  January 6, 2017 at 7:53 pm

      Howard, I believe in science. I don’t read Anderson, Reder, and Simon as having “overly narrow understandings of cognition or learning theories.” I see them as some of the most insightful cognitive scientists ever. I read Kirschner, Sweller, and Clark as being some of the most brilliant learning scientists today. Dewey was brilliant, but not a great scientist. He could have been wrong. If you’re going to argue “overly narrow understandings of cognition,” you’re going to have to show experimental evidence. It’s science. Come up with a better experiment, or accept their findings.

      • 4. Howard  |  January 9, 2017 at 12:25 pm

        I also believe in science and evidence-based practice (EBP), but the list of great experimental psychologists include John Watson and BF Skinner whose experimental work did not translate to valid educational practices as well as Edward Thorndike whose behavioral roots in psychometrics underly many of the problems with the No Child Left Behind program. In contrast, many influential scientists like Dewey, Lev Vygotsky or Jerome Bruner achieved lasting impact more through their hermeneutic works.
        If we intend to direct students toward higher cognitive abilities like collaborative problem solving or the tenets of the Agile Manifesto, we need to scaffold student performance so they can learn while doing until they can understand new knowledge within a wider social field (a pedagogical descendant of discovery learning). I favor direct instruction, but primarily as a “stage setting” pedagogy leading toward pedagogy that targets higher level cognition. I also read the paradigm underlying Kirschner’s paper In the light of new data-based personalized instruction in AI systems, advocating for direct instruction measured through behavioristic psychometrics or narrow data points like cognitive load. This can easily lead back toward the programmed instruction of Skinner and I doubt it will fare better the second time around. Experimental psychology is important, but beyond experimental results I want to reserve judgement until validity is established in educational practice. I also hope to see more acknowledgement of the reason and common sense advocated by CS Peirce who denounced the “axe welding” “demarcationist” view of science, for as recognized by Paul Fererabend, beliefs are changed less by argument than by taking new attitudes, new standards and new ways of seeing.

        • 5. Howard  |  January 9, 2017 at 4:12 pm

          PS I forgot to note that I was agreeing with Anderson, Reder and Simon who said;
          “We need to be more tentative in our recommendations for instructional methods than in our recommendations for research. Nevertheless, there is already considerable empirical support for the superiority, relative to mainstream classroom methods, of a number of procedures (like the learning-from-examples and learning-by-doing methods already mentioned) that are ready for classroom testing on a large scale”.
          A very Pragmatist idea of moving investigation to large scale practice and I would think that the mainstream classroom methods they question are compromised largely by direct instruction.

  • 6. Bennett Brown  |  January 17, 2017 at 1:06 am

    This cuts right to the heart of the tension in designing project- and problem-based curriculum. Kudos, Mark, as always.

    Reader, log in to Academia to avoid the 403 error on Mark’s link to his 1991 project-based learning (PBL) article. A survey of 2000-2011 literature on PBL can be found at .

    I think a PBL unit’s success or failure hinges on four pivots. The teacher (or implemented curriculum) must:

    1) Provide effective direct instruction to enable each student to clear a reasonable bar when responding to the problem, and pick the problem to be within proximal development of student understanding such that the direct instruction can be provided in a week or month.

    2) Precede the direct instruction sequence with a preview of the problem. Neither PLTW (who I no longer work for), nor anyone else I’m aware of, has ever measured the importance of this variable, but I know that PLTW teachers vary significantly with respect to this variable. I’d be delighted to collaborate on a study of this, and would wager odds that time previewing the problem is correlated to student interest and motivation during direct instruction.

    3) Identify a problem that is sufficiently interesting to diverse students. By providing multiple options for a problem, most problems can be made interesting wider range of students and can be differentiated to a wider range of prior expertise.

    4) Constrain the problem so students spend most of their time demonstrating the desired skills and understanding. The rubric for assessing the work should mirror the objectives. I’d wager that the portion of the students’ work time that produces evidence of desired objectives correlates with the amount of time a teacher spends on the rubric before students work on the problem.


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