Difference between revisions of "Coordinative Learning"

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*[[Co-training of Chinese characters| Co-training of Chinese characters (Liu, Perfetti, Dunlap, Zi, Mitchell)]]
 
*[[Co-training of Chinese characters| Co-training of Chinese characters (Liu, Perfetti, Dunlap, Zi, Mitchell)]]
 
*[[Learning Chinese pronunciation from a “talking head”| Learning Chinese pronunciation from a “talking head” (Liu, Massaro, Dunlap, Wu, Chen,Chan, Perfetti)]] [Was in Fluency]
 
*[[Learning Chinese pronunciation from a “talking head”| Learning Chinese pronunciation from a “talking head” (Liu, Massaro, Dunlap, Wu, Chen,Chan, Perfetti)]] [Was in Fluency]
*[[HandwritingEquationSolving | Handwriting in algebra learning (Anthony, Yang & Koedinger)]]
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*[[Effect of adding simple worked examples to problem-solving in algebra learning]] (Anthony, Yang & Koedinger)
  
 
Examples and Explanations
 
Examples and Explanations

Revision as of 11:32, 26 February 2007

The PSLC Coordinative Learning cluster

Abstract

The studies in the Coordinative Learning cluster tend to focus on varying a) the types of information available to learning or b) the instructional methods that they employ. In particular, the studies focus on the impact of having learners coordinate two or more types. Given that the student has multiple sources/methods available, two factors that might impact learning are:

  • What is the relationship between the content in the two sources or the content generated by the two methods? Our hypothesis is that the two sources or methods facilitate robust learning when a knowledge component is difficult to understand or absent in one and is present or easier to understand in the other.
  • When and how does the student coordinate between the two sources or methods? Our hypothesis is that students should be encouraged to compare the two, perhaps by putting them close together in space or time.

At the micro-level, the overall hypothesis is that robust learning occurs when the learning event space has target paths whose sense making difficulties complement each other (as expressed in the first bullet above) and the students make path choices that take advantage of these complementary paths (as in the second bullet, above). This hypothesis is just a specialization of the general PSLC hypothesis to this cluster.

Glossary

Coordinative Learning glossary.

Research question

When and how does coordinating multiple sources of information or lines of reasoning increase robust learning?


Two sub-groups of coordinative learning studies are exploring these more specific questions:

1) Visualizations and Multi-modal sources

When does adding visualizations or other multi-modal input enhance robust learning and how do we best support students in coordinating these sources?

2) Examples and Explanations

When and how should example study by combined and coordinated with problem solving to increase robust learning? When and how should explicit explanations be added or requested of students before, during, or after example study and problem solving practice?

Independent variables

  • Content of the sources (e.g., pictures, diagrams, written text, audio, animation) or the encouraged lines of reasoning (e.g., example study, self-explanation, conceptual task, procedural task) and combinations

Dependent variables

Normal post-test and measures of robust learning.

Hypotheses

When students are given sources/methods whose sense making difficulties are complementary and they are engaged in coordinating the sources/methods, then their learning will be more robust than it would otherwise be.

Explanation

There are both sense making and foundational skill building explanations. From the sense making perspective, if the sources/methods yield complementary content and the student is engaged in coordinating them, then the student is more likely to successfully understand the instruction because if a student fails to understand one of the sources/methods, he can use the second to make sense of the first. From a foundational skill building perspective, attending to both sources/methods simultaneously associates features from both with the learned knowledge components, thus potentially increasing feature validity and hence robust learning.

Descendents

Visualizations and Multi-modal sources

Examples and Explanations

Annotated Bibliography

Forthcoming