Coordinative Learning

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

• Coordination-- a process for achieving self-supervised learning by integrating information from multiple sources or reasoning methods
• Sources--information-supplying resources that can be used for learning
• Complementary--when two (or more) sources or methods together yield more complete information on knowledge components than either alone
• Self-supervised learning-- learning that occurs by a student constructing or refining a knowledge component without having a teacher or training system providing feedback on correctness
• Co-training--a self-supervised learning method for learning from unlabeled examples and multiple sources. See papers by Blum and Mitchell.
• Unlabeled examples--examples given without feedback, label, or response
• Strategies--
• External representations--
• Input sources --
• Multi-media sources--
• Self-explanation--
• Conceptual tasks--tasks that assess understanding of the principals that govern the domain and of the interrelations between pieces of knowledge in a domain (although this knowledge doe snot need to be explicit); also thought of as assessment of understanding or of principled knowledge (See Rittle-Johnson & Siegler, 1998)
• Procedural tasks--tasks that assess knowledge of action sequences for solving problems, skills, algorithms, or strategies. (See Rittle-Johnson & Siegler, 1998)
• Contiguity--
• Instructional method--

Research question

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

Independent variables

• Content of the sources (e.g., pictures, diagrams, written text)

• Instructional activities designed to engage students in coordination (e.g., conceptual vs. procedural exercises, contiguous presentation of sources, self-explanation)

Dependent variables

Measures of normal and 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

• Visual-verbal learning in geometry (Aleven & Butcher)
• Hints during tutored problem solving – the effect of fewer hint levels with greater conceptual content (Aleven & Roll)
• Handwriting in algebra learning (Anthony, Yang & Koedinger)
• Note-taking technologies (Bauer & Koedinger)
• Knowledge component construction vs. recall (Booth, Siegler, Koedinger & Rittle-Johnson)
• Visual Representations in Science Learning (Davenport, Klahr & Koedinger)
• Co-training of Chinese characters (Liu, Perfetti, Dunlap, Zi, Mitchell)
• Studying the Learning Effect of Personalization and Worked Examples in the Solving of Stoichiometry Problems (McLaren, Koedinger & Yaron)
• Implicit vs. explicit instruction on word meanings (Juffs & Eskenazi) [Was in Fluency]
• Learning Chinese pronunciation from a “talking head” (Liu, Massaro, Dunlap, Wu, Chen,Chan, Perfetti) [Was in Fluency]

Annotated Bibliography

Forthcoming