Posts tagged ‘cognitive science’
Premise 1: Teaching is a human endeavor that does not and cannot improve over time.
Premise 2: Human beings are fantastic learners.
Premise 3: Humans don’t learn well in the teaching-focused classroom.
Conclusion: We won’t meet the needs for more and better higher education until professors become designers of learning experiences and not teachers.
Interesting argument linked above, but wrong.
- Premise 1: Teaching does improve with time. Gerhard Fischer published a wonderful piece many years ago that showed how skiing instruction has improved over time, and that the approaches used can be understood in terms of cognitive science.
- Premise 2: Humans are fantastic learners, but as Kirschner, Sweller, and Clark showed, humans learn much better with direct instruction.
- Premise 3: No, no one learns well in a teaching-focused classroom. However, many teachers help their students learn better in a student-centered classrooms.
- The Conclusion doesn’t follow from the premises at all.
I don’t agree that learning a foreign language is as useful as learning a programming language, especially in terms of increased communication capability (so I wouldn’t see it as equivalent to a foreign language requirement). I see learning a foreign language as far more important and useful. It is interesting to think about cognitive effects of learning programming that might be similar to the cognitive effects of learning another human language.
Learning a language increases perception. Multilingual students are better at observing their surroundings. They can focus on important information and exclude information that is less relevant. They’re also better at spotting misleading data. Likewise, programming necessitates being able to focus on what works while eliminating bugs. Foreign language instruction today emphasizes practical communication — what students can do with the language. Similarly, coding is practical, empowering and critical to the daily life of everyone living in the 21st century.
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.