I drew on Cognitive Apprenticeship a lot in my dissertation — so much so that Carl Berger asked me at my proposal, “Are you testing Cognitive Apprenticeship as a model?” I had no idea how to respond, and 25 years later, I still don’t. How do you test a conceptual framework?
Cognitive apprenticeship, like situated learning, starts from the assumption that apprenticeship is a particularly effective form of education. Then it asks, “How do you offer an apprenticeship around invisible tasks?”
What I like about the essay linked below is that it places cognitive apprenticeship in a broader context. Apprenticeship isn’t always the best option (as discussed in the post about the Herb Simon paper).
Active listeners or readers, who test their understanding and pursue the issues that are raised in their minds, learn things that apprenticeship can never teach. To the degree that readers or listeners are passive, however, they will not learn as much as they would by apprenticeship, because apprenticeship forces them to use their knowledge. Moreover, few people learn to be active readers and listeners on their own, and that is where cognitive apprenticeship is critical–observing the processes by which an expert listener or reader thinks and practicing these skills under the guidance of the expert can teach students to learn on their own more skillfully.
I wonder if this result explains why the second semester students in Briana’s studies (see previous blog post) didn’t have the “W” effect. If you do enough code, you move down the power law of practice, and now you can attend to things like context and generating subgoal labels.
Different subjects start the experiment with different amounts of ability and past experience. Before starting, subjects took a multiple choice test of their knowledge. If we take the results of this test as a proxy for the ability/knowledge at the start of the experiment, then the power law equation becomes (a similar modification can be made to the exponential equation):
That is, the test score is treated as equivalent to performing some number of rounds of implementation). A power law is a better fit than exponential to this data (code+data); the fit captures the general shape, but misses lots of what look like important details.
Insightful new report from ACCESS-CA on who is taking AP CS in California and on the challenges (quoted below):
Despite the strong outlook for the technology economy in California, there are major challenges in meeting the growing demand for skilled technology workers and preparing Californians to participate in the workforce of the future:
The lack of computer science standards, courses, and teachers and the lack of alignment between computing pathways and workforce needs. Roughly 65% of high schools in California offer no computing classes and the state has yet to develop a statewide plan for computing education.
The lack of diversity in the computing education pipeline and within the technology sector, particularly given the rapidly-increasing diversity of California’s population. 60% of California’s student population is Latinx or African American, yet these students comprise just 16% of students taking AP CS A and 15% of the technology workforce
California is now starting a process of developing computer science standards for K-12, explicitly using the new K-12 CS Framework. California is huge and has a huge influence on the rest of the country’s education policy and practice. This will likely be one of the most important outcomes of the K-12 CS Framework process.
Computer Science Content Standards Development
The CDE, Instructional Quality Commission, and State Board of Education (SBE) are commencing the process for developing new California computer science content standards. Per California Education Code. Section 60605.4, “on or before July 31, 2019, the Instructional Quality Commission shall consider developing and recommending to the SBE computer science content standards for kindergarten and grades 1 to 12, inclusive, pursuant to recommendations developed by a group of computer science experts.” Information and updates concerning the development of computer science content standards for California public schools will be posted here.
Interesting essay from Neil Brown who decided to try to resurrect some of the best of CS Education research software from the past. As I mentioned in a previous blog post, I have found that Logo code from the past doesn’t run as-is on modern Logo implementations. I was just talking to a colleague about how great it would be to be able to run Boxer and HyperCard again. (Yes, I have a license for Livecode, but it’s not the same interface as HyperCard.) Etoys still runs on everything, but it’s a rare exception. It’s important to make progress that we build on the past, and not simply re-invent it, forget it, or mis-remember it.
I did have one or two successes, such as getting a version of the GENIE editor running in an emulator. And it was a revelation that greatly pushed forward my understanding of old structured editors. By modern standards, they were awful. The papers’ descriptions didn’t make clear how tedious and fiddly the navigation was, how unhelpful the editor was, how awkward it was to deal with errors. Running the software was an absolutely crucial step to comparing our work to theirs. It allowed me to understand the design and critique the editor’s operation for myself, rather than relying on the authors’ incomplete descriptions of their own software.For all the other editors which I couldn’t run, there are these reviewers asking the perfectly valid question in research: “How does your work relate to previous work X?” And the honest answer is: I don’t know. Perhaps nobody can know any more — the paper wasn’t very detailed and the software is lost in time. This is no way to do research.
From Ruthe Farmer in White House OSTP. It’s great that we’re going to get more data about CS Education in the United States. Should it be at the federal level, when decisions about K-12 in the US are at the state level? I’d like to get data collected at a level that impacts decision-making. How do we get states to track CS education? Will the federal government’s effort be a prompt to get the states to track who takes CS classes, where they’re offered, and where they’re not?
Computer science has been added to the proposed 2017-18 Dept of Ed Civil Rights Data Collection. The proposed new collection instruments are open for public comment through 2/28/17.
You can view the documents here:
(you will find the proposed data collection instruments on pages 29-31 of the doc titled A-2_CRDC_Data_Groups_12_23_16)
You can add comments here:
Comments from the public are critical to inclusion of this new data request, as the overall push is to lessen the reporting load for schools. However, we felt it was necessary to add computer science as a separately tracked subject to obtain a better picture of total enrollment nationally.
Please share this opportunity to comment with your networks.
Ruthe A. Farmer | Senior Policy Advisor for Tech Inclusion
Office of Science & Technology Policy
Executive Office of the President
Balancing cognition and motivation in computing education: Herbert Simon and evidence-based education
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.
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:
- Female and under-represented minority students don’t learn differently than the white and Asian males who dominate computing. But they’re much less interested in learning computer science. “Computer science isn’t that difficult but wanting to learn it is.”
- Many students (especially female students) enter computer science class with low self-efficacy (see ICER 2016 award-winning paper). If they don’t believe they can do it, it’s not surprising that they don’t.
- We have too few computer science teachers because it is hard to motivate teachers to teach computer science.
- Students want authentic, complex learning situations, which is why we tend to value CS1 programming languages that are popular in industry. They don’t want practice and abstraction. Anderson, Reder, and Simon point out that more complex learning situations lead to more failure and lagging 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.