Some Readings on Cognitive Science (for 2/13/12)

Meltzoff, A. N., Kuhl, P. K., Movellan, J., & Sejnowski, T. J. (2009). Foundations for a new science of learning. Science, 325(5938), 284-288. doi:10.1126/science.1175626

This article discusses the connection between learning theory, psychology and machine learning.  The take away highlights (particularly for a language teacher) are about the importance of social interaction in learning.  It cites many studies that pertain to early childhood development, but there are interesting and important things that apply to all age groups.  For example, children who are simple exposed to sounds in a foreign language via TV or radio learn much less than children who interact with humans in the language.  Researchers are developing robots that can interact in more social ways, and reported findings of an interactive robot increasing learning when compared to tv.  (seems like that is what we are seeing with iOS apps for kids and language).  The authors talk about three traits of human learning: imitation, shared attention and empathy and social emotions.  It would be interesting to examine language course based on those three elements.  

Hoadley, C., & Van Haneghan, J. P. .(2012) The learning sciences: Where they came from and what it means for instructional designers. In. Reiser, R. A., & Dempsey, J. V. (Eds.), Trends and issues in instructional design and technology (3rd ed.). Boston, MA: Pearson. [Note: online preprint]

This book chapter gives a brief historical background of learning theory from behaviorism to cognitivism and into more recently developed theories such as those in the realm of communities of practice and anchored instruction.  This is a really helpful resource to use when looking at theory as it applies to instructional design and learning science.  This sums up the sources that support the notion of deep, enduring and transferable and meaningful learning.

Winn, W. (2004). Cognitive perspectives in psychology. In D. H. Jonassen (Ed.), Handbook of research on educational communications and technology (2nd ed., pp. 79-112). Mahwah, NJ: Lawrence Erlbaum. [From AECT, free access for members]

This chapter explored the current reality of cognitive science as it relates to learning.  Winn groups these new developments into four categories: systems theoretical frameworks, biological frameworks, approaches based on cognitive neuroscience, and neural networks.   Then there is an extensive historical background.  Woah.  Wondering about chunking, embeddeness, embodiment or adaption?  It is all there.

Also, the introduction provides a sharp criticism of constructivism as having the dangerous potential to value anything that is student produced as inherently of intellectual value.  It makes me think of crappy projects that my students turn in but fit into a decent grade based on the rubric—not sure I agree with the critique, but interesting to read. 

The final section "Cognitive Theory and Educational Technology" is hugely useful for understanding the basis of the field of educational technology and also why some things appear to be "stuck" or harder to make changes.  It describes the legacy of behaviorism in instructional design and the weakness that is born out of the need for specificity given the multitude of combinations and compounding variables in this line of work.  It is hard to generalize anything!  It is a good summary to use as a reference when thinking about methodologies as it points out many of the real-life complications in instructional design. 

Willingham, D. T. (2009). Why don't students like school? Because the mind is not designed for thinking. American Educator, 33(1), 4-13.

This magazine article is written for teachers and administrators.  It presents some learning about cognitive science from the perspective that thinking is hard, and people will avoid doing it.  This statement sets the stage for an explanation about what are the optimal conditions for encouraging, rewarding and fostering thinking in our students.  The author emphasizes an understanding of prior knowledge, the value of success and variety, as things to keep in mind when hoping to craft an environment in which students learn, and like it. 

 Willingham, D. T. (2009). Three problems in the marriage of neuroscience and education. Cortex, 45(4), 544-545. doi:10.1016/j.cortex.2008.05.009

This article begins by emphasizing the differenced between natural sciences (biology, neuroscience, etc. . . ) and artificial sciences (economics, education etc. . . ).  As he describes, artificial sciences are driven by goals, whereas natural sciences seek to explain or describe a given phenomenon. Their ability to inform each other is limited. 

A summary of his thoughts:

Any marriage will be more successful when each partner has realistic expectations of the other. Educators should expect that neuroscience (1) will not be prescriptive; (2) will re- main silent on educational goals that are incompatible neuroscientific analysis (e.g., aesthetic training), and will omit levels of analysis beyond the individual, and; (3) neuroscience will prove helpful on targeted questions at a more- fine grained level of analysis such as how people read, learn, and attend, but these data will only be useful in the context of well-developed behavioral theory.

Riener, C., & Willingham, D. (2010). The myth of learning styles. Change: The Magazine of Higher Learning, 42(5), 32-35. doi: 10.1080/00091383.2010.503139

This article describes the lack of evidence for the success of instructional design based on learning styles.  The authors do support the notion that people will have difference preferences for how they learn, but it goes on to say that there is no evidence to suggest that catering to these preferences improves learning.  The article also includes an interesting discussion about why this myth has persisted.  First, it feels good; it values all people and their different strengths—who could argue against that?  Also, it has been impacted by confirmation bias and is now considered common knowledge.  The bottom line is that knowing your students, and understanding their prior knowledge and interests can help them learn, but there is not scientific basis for learning styles and differentiation on their behalf as helping learning.  It feels funny to read this and feel a little defensive—I so want to support each student that I almost find myself wanting to suppress this article.  But then at the end they offer Mindset, by Carol Dweck as a reference, and I am back to wanting to pass it out.  We don't need to use fuzzy science to encourage supporting our students as individuals—there is plenty of real science to support and justify those desires.