Posts tagged ‘cognitive science’
Really interesting blog post, dissecting the mistakes made in a very popular TED talk.
Sir Ken’s ideas aren’t just impractical; they are undesirable. Here’s the trouble with his arguments:
1. Talent, creativity and intelligence are not innate, but come through practice.
2. Learning styles and multiple intelligences don’t exist.
3. Literacy and numeracy are the basis for creativity.
4. Misbehaviour is a bigger problem in our schools than conformity.
5. Academic achievement is vital but unequal, partly because…
6. Rich kids get rich cultural knowledge, poor kids don’t.
I don’t completely agree with all of Pragmatic Education’s arguments.
- Intelligence may not be malleable. You can learn more knowledge, and that can come from practice. It’s not clear that fluid intelligence is improved with practice.
- Learning styles don’t seem to exist. Multiple intelligences? I don’t think that the answer is as clear there.
- Creativity comes from knowing things. Literacy and numeracy are great ways of coming to know things. It’s a bit strong to say that creativity comes from literacy and numeracy.
- There are lots of reasons why rich kids are unequal to poor kids (see the issue about poverty and cognitive function.) Cultural knowledge is just part of it.
But 90% — I think he gets what’s wrong with Sir Ken’s arguments.
I’d love to see this new system from MIT compared to Lewis Johnson’s Proust. Proust also found semantic bugs in students’ code. Lewis (and Elliot Soloway and Jim Spohrer) collected hundreds of bugs when students were working on the Rainfall Problem, then looked for those bugs in students’ programs. Proust caught about 85% of students’ semantic errors. That last 15% covered so many different bugs that it wasn’t worthwhile to encode the semantic check rules — each rule would only fire once, ever. My guess is that Proust, which knew what problem that the students were working on, would do better than the MIT homework checker, because it can only encode general mistakes.
The new system does depend on a catalogue of the types of errors that student programmers tend to make. One such error is to begin counting from zero on one pass through a series of data items and from one in another; another is to forget to add the condition of equality to a comparison — as in, “If a is greater than or equal to b, do x.”
The first step for the researchers’ automated-grading algorithm is to identify all the spots in a student’s program where any of the common errors might have occurred. At each of those spots, the possible error establishes a range of variations in the program’s output: one output if counting begins at zero, for instance, another if it begins at one. Every possible combination of variations represents a different candidate for the corrected version of the student’s program.
I like David Brooks’s opinion pieces quite a bit, and particularly his pieces where he draws on research. The piece linked below touches on an issue that I’ve been wondering about. All this neuroscience data about what part of the brain lights up when — what does it really tell us about how the mind works? Does it actually tell us anything about learning? Brooks’ opinion: Not yet.
These two forms of extremism are refuted by the same reality. The brain is not the mind. It is probably impossible to look at a map of brain activity and predict or even understand the emotions, reactions, hopes and desires of the mind.
I usually really like Annie Murphy Paul’s articles, but this one didn’t work for me. Below are her reasons why TED talk videos work well in learning, with my comments interspersed.
• They gratify our preference for visual learning. Effective presentations treat our visual sense as being integral to learning. This elevation of the image—and the eschewal of text-heavy Power Point presentations—comports well with cognitive scientists’ findings that we understand and remember pictures much better than mere words.
Cognitive scientists like Richard Mayer have found that diagrams and pictures can enhance learning — absolutely. But his work combined diagrams with words (e.g., best combination with diagrams: audio narration, not visual text). This quote seems to suggest that pictures are better than words. For most of STEM, that’s not true. We may have an affinity for visual, but that doesn’t mean that it works better for learning complex material.
• They engage the power of social learning. The robust conversation that videos can inspire, both online and off, recognizes a central principle of adult education: We learn best from other people. In the discussions, debates, and occasional arguments about the content of the talks they see, video-watchers are deepening their own knowledge and understanding.
Wait a minute — isn’t she just saying that TED talks give us something to talk about? TED talks are not themselves inherently social. Isn’t a book discussed in a book club just as effective for “engaging the power of social learning”? What makes TED talks so “social”?
• They enable self-directed, “just-in-time” learning. Because video viewers choose which talks to watch and when to watch them, they’re able to tailor their education to their own needs. Knowledge is easiest to absorb at the moment when we’re ready to apply it.
This was the quote that inspired this blog post. It’s an open question, but here’s my hypothesis. Nobody watches a TED talk for “just-in-time” learning. People watch TED talk for entertainment. ”I am about to go to my school board meeting — I think I’ll watch Sir Ken Robinson to figure out what to say!” ”I need to be able to guess birthdays — isn’t there a TED talk on that?” There are videos that really work for “just-in-time” learning. TED talks aren’t like that.
• They encourage viewers to build on what they already know. Adults are not blank slates: They bring to learning a lifetime of previously acquired information and experience. Effective video instruction build on top of this knowledge, adding and elaborating without dumbing down.
It’s absolutely true that effective instruction builds on top of existing knowledge, which is something that the best teachers know how to do — to figure out what students know and care about, and relate knowledge to that. How does a fixed video build on what viewers (all hundreds of thousands of them) actually know? No, I don’t see how TED talks do that.
Way to go, Wendy! My Georgia Tech colleague did really well at a recent AAAS forum on MOOCs. The tone between the three speakers is striking. Anant Agarwal says “Hype is a good thing!” Kevin Wehrbach says that a MOOC is “an extraordinary teaching and learning experience.” Then Wendy Newstetter lets loose with concerns supported with citations and hard research questions.
In any learning environment, students should gain “transferable knowledge” that can be applied in many contexts, said Newstetter, citing a 2012 National Academies’ report on Education for Life and Work. Specifically, she said, researcher James Pellegrino has identified an array of cognitive, interpersonal and intrapersonal skills that all students need in order to succeed. How can the array of new online learning models help students achieve those goals?
Newstetter proposed a series of questions that should be answered by research. Educators need to know, for example, under what conditions technology-mediated experiences can result in enhanced learning competencies, she said. Do MOOCs effectively encourage students to develop perseverance, self-regulation and other such skills? Is knowledge gained in a MOOC “transferable,” so that what students learn can help them solve problems in other contexts? How can MOOCs be enhanced to promote interpersonal skills, and what intrapersonal attributes are needed for optimal learning in MOOCs?
Some observers have suggested that MOOCs tend to work best for more affluent students, Newstetter noted. She mentioned the 2013 William D. Carey lecture, presented at the AAAS Forum by Freeman Hrabowski III, president of the University of Maryland, Baltimore County, who focused on strategies for helping underrepresented minorities succeed in science fields. “What he described was high-contact, intensive mentoring,” she pointed out.
This is a compelling vision. Set aside MOOCs or not — how could we use a team-based approach in building postsecondary education, so that we have the best of texts, tools, in-class experiences, videos, and individualized tutoring and advising? If we want higher-quality, we can’t expect one teacher to perform all roles for increasing numbers of students.
The real threat to traditional higher education embraces a more radical vision that removes faculty from the organizational center and uses cognitive science to organize the learning around the learner. Such models exist now.
Consider, for example the implications of Carnegie Mellon’s Open Learning Initiative. More than 10 years ago, Herb Simon, the Carnegie Mellon University professor and Nobel laureate, declared, “Improvement in postsecondary education will require converting teaching from a solo sport to a community-based research activity.” The Open Learning Initiative (OLI) is an outgrowth of that vision and has been striving to realize it for more than a decade.
Call for Participation
2nd Annual Learning Science Workshop
Research and Innovation for Enhancing Achievement and Equity
Carnegie Mellon University
Applications Due May 5, 2013
*No Cost To Attend*
LearnLab, an NSF Science of Learning Center (SLC) at Carnegie Mellon and the University of Pittsburgh, has an exciting summer research opportunity available to early career researchers in the fields of psychology, education, computer science, human-computer interfaces and language technologies.
The workshop is targeted to senior graduate students, post-docs and early career faculty. The workshop seeks broad participation, including members of underrepresented groups as defined by NSF (African American, Hispanic, Native American) who may be considering a research or faculty position in the learning sciences.
This two-day workshop immediately precedes the LearnLab Summer School (www.learnlab.org/opportunities/summer/). Our research theme is theresearch and innovation for enhancing achievement and equity, including these five areas:
* Enhancing Achievement through Educational Technology and Data Mining. Using domain modeling, and large datasets to discover when learning occurs and to provide scaffolding for struggling students. See http://www.learnlab.org/research/wiki/index.php/Computational_Modeling_and_Data_Mining.
* 21st Century Skills, Dispositions, and Opportunities. Re-examining the goals of education and assessment and considering transformative changes in how and where learning occurs.
* Opening Classroom Discourse. Studying how classroom talk contributes to domain learning and supports equity of learning opportunity. See LearnLab’s Social-Communicative Factors thrustwww.learnlab.org/research/wiki/index.php/Social_and_Communicative_Factors_in_Learning.
* Course-Situated Research. Running principle-testing experiments while navigating the complex waters of real-world classrooms. Seewww.learnlab.org/research/wiki/index.php/In_vivo_experiment.
* Motivation Interventions for Learning. Implementing theory based motivational interventions to target at risk populations to improve robust student learning. Seehttp://www.learnlab.org/research/wiki/index.php/Metacognition_and_Motivation
The substantive focus of the workshop is the use of current research and innovations to enhance achievement and equity at all levels of learning. Activities will include demonstrations of the diverse set of ongoing learning sciences research projects at LearnLab, and poster presentations or talks by participants. Participants will also meet with LearnLab faculty in research groups and various informal settings. We will provide information about becoming a part of the Carnegie Mellon or University of Pittsburgh learning science community.
In addition to these substantive themes, the workshop will provide participants with opportunities for professional development and the chance to gain a better understanding of the academic career ladder. These include mentoring that focuses on skills, strategies and “insider information” for career paths. Sessions will include keynote speakers and LearnLab senior faculty discussing professional development topics of interest to the attendees. These may include the tenure and promotion process, launching a research program, professionalism, proposal writing, among other topics. There is no cost to attend this workshop
We are very pleased to announce that the workshop will have two distinguished keynote speakers:
Nora S. Newcombe, Ph.D. is the James H. Glackin Distinguished Faculty Fellow and Professor of Psychology at Temple University. Dr. Newcombe is the PI of the Spatial Intelligence and Learning Center (SILC), headquartered at Temple and involving Northwestern, the University of Chicago and the University of Pennsylvania as primary partners. Dr. Newcombe was educated at Antioch College, where she graduated with a major in psychology in 1972; and at Harvard University, where she received her Ph.D. in Psychology and Social Relations in 1976. She taught previously at Penn State University.
A nationally recognized expert on cognitive development, Dr. Newcombe’s research has focused on spatial development and the development of episodic and autobiographical memory. Her work has been federally funded by NICHD and the National Science Foundation for over 30 years. She is the author of numerous scholarly chapters and articles on aspects of cognitive development, and the author or editor of five books, including Making Space: The Development of Spatial Representation and Reasoning (with Janellen Huttenlocher) published by the MIT Press in 2000.
Tammy Clegg, Ph.D. is an assistant professor in the College of Education with a joint appointment in the College of Information Studies at the University of Maryland. She received her PhD in Computer Science at Georgia Tech in 2010 and her Bachelor of Science in Computer Science from North Carolina State University in 2002. From 2010-2012 Tamara was a postdoctoral fellow at the University of Maryland with the Computing Innovations Fellows program. Her work focuses on developing technology to support life-relevant learning environments where children engage in science in the context of achieving goals relevant to their lives. Kitchen Chemistry is the first life-relevant learning environment she designed along with colleagues at Georgia Tech. In Kitchen Chemistry, middle-school children learn and use science inquiry to make and perfect dishes. Clegg uses participatory design with children to design these new technologies. Her work currently includes creating new life-relevant learning environments (e.g., Sports Physics, Backyard Biology) to understand how identity development happens across these environments. From this analysis, she aims to draw out design guidelines for life-relevant learning activities and technology in various contexts (e.g., sports, gardening).
LearnLab is funded by the National Science Foundation (award number SBE-0836012). Our center leverages cognitive theory and computational modeling to identify the instructional conditions that cause robust student learning. Our researchers study robust learning by conducting in vivo experiments in math, science and language courses. We also support collaborative primary and secondary analysis of learning data through our open data repository LearnLab DataShop, which provides data import and export features as well as advanced visualization, statistical, and data mining tools.
To learn more about our cognitive science theoretical framework, read our Knowledge-Learning-Instruction Framework.
The results of our research are collected in our theoretical wiki which currently has over 400 pages. It also includes a list of principles of learning which are supported by learning science research. The wiki is open and freely editable, and we invite you to learn more and contribute.
Applicants should email their CV, this demographic form, a proposed presentation title and abstract, and a brief statement describing their research interests to Jo Bodnar (firstname.lastname@example.org) by May 5, 2013. Please use the subject Application for LearnLab Summer Workshop 2013. Upon acceptance, we will let you know if you have been selected for a talk or poster presentation.
There is no registration fee for this workshop. However, attendance is limited so early applications are encouraged. Scholarships for travel are available. Scholarships will be awarded based on your application, including your research interests, future plans, and optional recommendation letter.
I’m visiting Indiana University this week, and giving two talks. If any readers are in the Bloomington area, I hope you can stop by!
9:30 am Jan 29
Title: Improving Success in Learning Computer Science Using Lessons from Learning Sciences
Abstract: Learning computer science is difficult, with multiple international studies demonstrating little progress. We still understand too little about the cognitive difficulties of learning programming, but we do know that we can improve success by drawing on lessons from across learning sciences. In this talk, I will describe three examples, where we improve success in learning computer science through application of lessons and models from the learning sciences. We increased the retention of non-CS majors in a required CS course by increasing the relevance of the course (informed by Eccles’ model of achievement-related choices), though we are limited in how far we can go because legitimate peripheral participation is less relevant. We improved opportunities to learn in a collaborative forum by drawing on lessons from anchored instruction, but were eventually defeated by student perceptions of culture. We have improved learning and transfer of knowledge about programming by using subgoal labeling to promote self-explanations.
Abstract: My colleagues and I have been studying how to teach computer science, to CS majors, to non-CS undergraduates, and to adult professionals. In this talk, I’ll talk about some of what we’ve learned, organized around three lessons. Lesson #1: We typically teach computer science too abstractly, and by teaching it in a context (e.g., media, robots, Nintendo GameBoys, Photoshop), we can dramatically improve success (retention and learning) for both traditional and non-traditional CS learners. Lesson #2: Collaboration can create opportunities for learning, but classroom culture (e.g., competition) trumps technology (Wikis). Lesson #3: Our greatest challenge in computer science education is improving teaching, and that will require changes in high schools, in public policy, and in universities.
Mylène is describing in the below blog post about how she’s helping her students develop a set of cognitive skills (including a growth mindset) to help them build models. What I found fascinating in her post were the implicit points, obvious to her, about what the students didn’t know. One student said, “I wish someone had told me this a long time ago.” What are the cognitive skills necessary to enable people to build models, or program? Causal thinking is absolutely critical, of course. What else is necessary that we haven’t identified? We need to check if students have those skills, or if we need to teach them explicitly.
Last year I found out in February that my students couldn’t consistently distinguish between a cause and a definition, and trying to promote that distinction while they were overloaded with circuit theory was just too much. So this year I created a unit called “Thinking Like a Technician,” in which I introduced the thinking skills we would use in the context of everyday examples.
Fascinating question! Bilingual people have some additional executive control. Does learning a programming language give a similar benefit in executive control? The study described below is suggestive but not conclusive. If we could find evidence for it, it would be another benefit of learning to program.
If computer programming languages are languages, then people who spoke one language and could programme to a high standard should be bilingual. Research has suggested that bilingual people perform faster than monolingual people at tasks requiring executive control – that is, tasks involving the ability to pay attention to important information and ignore irrelevant information (for a review of the “robust” evidence for this, see Hilchey & Klein, 2011). So, I set out to find out whether computer programmers were better at these tasks too. It is thought that the bilingual advantage is the result of the effort involved in keeping two languages separate in the brain and deciding which one to use. I noticed that novice computer programmers have difficulty in controlling “transfer” from English to programming languages (e.g. expecting the command “while” to imply continuous checking; see Soloway and Spohrer, 1989), so it seemed plausible that something similar might occur through the learning of programming languages.
Richard Hake relates a story from Alan Schoenfeld:
One of the problems on the NAEP [National Assessment of Educational Progress] secondary mathematics exam, which was administered to a stratified sample of 45,000 students nationwide, was the following: An army bus holds 36 soldiers. If 1128 soldiers are being bused to their training site, how many buses are needed?
Seventy percent of the students who took the exam set up the correct long division and performed it correctly. However, the following are the answers those students gave to the question of ‘how many buses are needed?’: 29% said…31 remainder 12; 18% said…31; 23% said…32, which is correct. (30% did not do the computation correctly).
It’s frightening enough that fewer than one-fourth of the students got the right answer. More frightening is that almost one out of three students said that the number of buses needed is ‘31 remainder 12’.
The problem that Hake and Schoenfeld are both pointing out is that we teach mathematics (and much else in our curriculum) completely divorced from the contexts in which the mathematics make sense. The children taking the NAEP knew how to do the mathematics, but not why, and not nearly enough about how the mathematics helps to solve a problem. They knew mathematics, but now what it was for.
Hake relates this story in an article about Louis Paul Benezet, an educator who ran a radical experiment in the 1930′s. Benezet saw how mindlessly young children were performing mathematics, so he made a dramatic change: Almost entirely remove mathematics from grades 1-5. Start teaching mathematics in grade 6, with a focus on problem-solving (e.g., start from estimation, so that you have a sense of when an answer is reasonable). Sixth graders can understand the problems for which one should use mathematics. The point is not to introduce the solution, until students understood the problem. Remarkably, the experimental 6th graders completely caught up in just four months to the 6th graders who had had mathematics all five previous years.
The experiment was radical then, and as far as I know, has not been replicated — even though evaluations suggest it worked well. It runs against our intuition about curriculum. Mathematics is important, right? We should do more of it, and as early as possible. How could you remove any of it? Benezet argued that, instead, young children should do more reading and writing, saving the mathematics for when it made sense.
Hake uses Benezet (and the evaluation of Benezet’s approach by Berman) to argue for a similar radical approach to physics education — teaching some things to kids to build up intuition, but with a focus on using physics to solve problems, and introducing the problems only when the students can understand them. There are lessons here for computing education, too.
- First, problems and contexts always come first! Teaching a FOR loop and arrays before teaching a problem in which they are useful just leads to rote learning, brittle knowledge which can’t be applied anywhere, let alone transferred.
- Second, the answer to the question “What should be removed from our overly-packed curriculum to squeeze computer science in?” may be “Get rid of the overly-packed curriculum.” There may be things that we’re teaching at the wrong time, in the wrong way, which really is just a waste of everyone’s time.
- Finally, just how young should we be teaching programming? Several people sent me the link to the report about Estonia teaching all first graders to program (quoted and linked below). Sure, you can teach first graders to program — but will they understand why they’re programming? What problems will first graders recognize as problems for which programming is a solution?
I do applaud the national will in Estonia to value computing education, but I do wonder if teaching programming so young leads to rote learning and the idea that “31 remainder 12″ is a reasonable number of buses.
We’re reading today that Estonia is implementing a new education program that will have 100 percent of publicly educated students learning to write code.
Called ProgeTiiger, the new initiative aims to turn children from avid consumers of technology (which they naturally are; try giving a 5-year-old an iPad sometime) into developers of technology (which they are not; see downward-spiraling computer science university degree program enrollment stats).
ProgreTiiger education will start with students in the first grade, which starts around the age of 7 or 8 for Estonians. The compsci education will continue through a student’s final years of public school, around age 16. Teachers are being trained on the new skills, and private sector IT companies are also getting involved, which makes sense, given that these entities will likely end up being the long-term beneficiaries of a technologically literate populace.
I taught educational technology in the Spring, and it gave me a chance to re-read classic texts (I still love Cognitive Apprenticeship) and reflect on some of the key principles of learning sciences. One of these is that all learning is built on existing knowledge — Piagetian assimilation and accommodation are still the main two learning mechanisms that we know. That’s why culture matters, and past experience matters.
The piece linked below from NYTimes highlights how different that prior experience can be, even with students attending the same classroom, and how those different experiences lead to different learning outcomes.
I wonder about the implications for CS Ed. What are the key experiences that lead students to have the prior knowledge to succeed in CS1? If a student has never built a spreadsheet with formulas, then that student may not have the same understanding of specifying instructions for another agent and for using a formal notation to be interpreted by machine, compared to a student who has. A student who has never used Photoshop or looked at a color chooser may have a harder time understanding hierarchy of data representations (e.g., red, green, and blue numbers inside a pixel, which is arranged in two dimensions to make up a picture). Studies in the past have looked at background experiences like how much mathematics a student has had. With the pervasiveness of computing technology today, we might be able to look at more “near transfer” kinds of activities.
When a new shipment of books arrives, Rhonda Levy, the principal, frets. Reading with comprehension assumes a shared prior knowledge, and cars are not the only gap at P.S. 142. Many of the children have never been to a zoo or to New Jersey. Some think the emergency room of New York Downtown Hospital is the doctor’s office.
The solution of the education establishment is to push young children to decode and read sooner, but Ms. Levy is taking a different tack. Working with Renée Dinnerstein, an early childhood specialist, she has made real life experiences the center of academic lessons, in hopes of improving reading and math skills by broadening children’s frames of reference.
The TechCrunch article actually cites research (see below), a paper by Cindy Hmelo. Cindy’s paper is actually on problem-based learning, but it does describe scaffolding — as defined in a Hmelo & Guzdial paper from 1996! How about that!
What I see in the Khan Academy offering is one of the kinds of scaffolding that Cindy and I talked about. Scaffolding is an idea (first defined by Wood, Bruner, and Ross) which does involve letting students explore, but under the guidance of a tutor. A teacher in scaffolding doesn’t “point out novel ways of accomplishing the task.” Instead, the teacher models the process for the student, coaches the student while they’re doing it, and gets the student to explain what they’re doing. A key part of scaffolding is that it fades — the student gets different kinds of support at different times, and the support decreases as the student gets more expert. I built a form of adaptable scaffolding in my 1993 dissertation project, Emile, which supported students building physics simulations in HyperTalk. Yes, students using Emile could click on variables and fill in their values without directly editing the code, but there was also process guidance (“First, identify your goals; next, find your components in the Library”) and prompts to get students to reflect on what they’re doing. And the scaffolding could be turned on or off, depending on student expertise.
I wouldn’t really call what Khan Academy has “scaffolding,” at least, not the way that Cindy and I defined it, nor in a way that I find compatible with Wood, Bruner, and Ross’s original definition. There’s not really a tutor or a teacher. There are videos as I learned from this blog post, and later found for myself. The intro video (currently available on the main Khan Academy page) says that students should just “intuit” how the code works. Really? There’s a lot more of this belief that students should just teach themselves what code does. The “scaffolding” in Khan Academy has no kind of process modeling or guidance, nothing to explain to students what they’re doing or why, nothing to encourage them to explain it to themselves.
It is a very cool text editor. But it’s a text editor. I don’t see it as a revolution in computer science education — not yet, anyway. Now, maybe it’s way of supporting “collaborative floundering” which has been suggested to be even more powerful than scaffolding as a learning activity. Maybe they’re right, and this will be the hook to get thousands of adolescents interested in programming. (I wonder if they tested with any adolescents before they released?) Khan has a good track record for attracting attention — I look forward to seeing where this goes.
The heart of the design places a simplified, interactive text editor that sits adjacent to the code’s drawing output, which updates in real time as students explore how different variables and numbers change the size, shapes, and colors of their new creation. An optional video guides students through the lesson, step-by-step, and, most importantly, can be paused at any point so that they can tinker with the drawing as curiosity and confusion arise during the process.
This part is key: learning is contextual and idiosyncratic; students better absorb new material if they can learn at their own pace and see the result of different options in realtime.
The pedagogy fits squarely into what educators called “scaffolded problem-based learning” [PDF]; students solve real-life problems and are encouraged to explore, but are guided by a teacher along the way, who can point out novel ways of accomplishing the task. Scaffolded learning acknowledges that real-life problems always have more than one path to a solution, that students learn best by doing, and that curiosity should drive exploration. This last point is perhaps the most important, since one of the primary barriers to boosting science-related college majors is a lack of interest.
I looked up this report, expecting to see something about computation as a ’21st-century skill.’ The report is not what I expected, and probably more valuable than what I was looking for. Rather than focus on which content is most valuable (which leads us to issues like the current debate of whether we ought to teach algebra anymore), the panel emphasized “nonacademic skills,” e.g., the ability to manage your time so that you can graduate and intra-personal skills. I also appreciated how careful the panel was about transfer, mentioning that we do know how to teach for transfer within a domain, but not between domains.
Stanford University education professor Linda Darling-Hammond, who was not part of the report committee, said developing common definitions of 21st-century skills is critical to current education policy discussions, such as those going on around the Common Core State Standards. She was pleased with the report’s recommendation to focus more research and resources on nonacademic skills. “Those are the things that determine whether you make it through college, as much as your GPA or your skill level when you start college,” she said. “We have tended to de-emphasize those skills in an era in which we are focusing almost exclusively on testing, and a narrow area of testing.”
The skill that may be the trickiest to teach and test may be the one that underlies and connects skills in all three areas: a student’s ability to transfer and apply existing knowledge to a problem in a new context. “Transfer is the sort of Holy Grail in this whole thing,” Mr. Pellegrino said. “We’d like to believe we can create Renaissance men who are experts in a wide array of disciplines and can blithely transfer skills from one to the other, but it just doesn’t happen that way.”
Very interesting report from Neil Brown. Here’s the question I’d like to know: So what are students intuitions about computing as they enter the classroom? Are they suppressed or supplanted through instruction? My guess is that it’s different for computing than for science. We live our lives for many years, 24 hours a day, in the real world before we enter school. That’s a lot of time to invent science hypotheses about the world. Not so much for computing. While we may increasing live our lives in a computing world, it’s a constructed, designed world — a world in which the computer science is explicitly hidden. I bet that students only make up theories about computing in times of break down, when they have to invent a theory to explain what went wrong. How often does that happen? What theories do they develop?
The paper title here says it all: Scientiﬁc knowledge suppresses but does not supplant earlier intuitions. A consistent theme across the research described in this post is that when you are explaining science to pupils, you are not adding totally new knowledge, in the way that you might when explaining a lesser-known historical event. When you explain forces to someone, they will already have an idea about the way the world works (drop something, and it falls to the ground), so you are trying to adjust and correct their existing understanding (falling is actually due to gravity), not start from scratch. The paper suggests that the old knowledge is generally not replaced, but merely suppressed, meaning people carry their original misconceptions with them forever-after.