KLI complexity-matching hypothesis: instructional principles of a given complexity are effective for knowledge components of equal or greater complexity but not less complex ones¶

Claim · All claims
Evidence · 1 study (2 entries) · theoretical r3 · q1
Currency · newest study 2010 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? The KLI framework hypothesizes that an instructional principle at a particular complexity level is effective for knowledge components of similar or greater complexity, but not for less complex ones. → Kenneth R. Koedinger 2010 q1 i? As a specific instance, self-explanation prompting is hypothesized to be effective for complex KCs like science principles but not for simpler KCs like English article selection. → Kenneth R. Koedinger 2010 (2)

Evidence¶

Kenneth R. Koedinger 2010¶

Kenneth R. Koedinger, Albert T. Corbett, Charles Perfetti. (2010). The Knowledge-Learning-Instruction (KLI) Framework: Toward Bridging the Science-Practice Chasm to Enhance Robust Student Learning. Carnegie Mellon University Technical Report. http://learnlab.org/research/wiki

q1 · i? · theoretical · r3

This is the authors' central hypothesis stated in the executive summary of the framework paper, not a tested result. The article offers it as a hypothesis-generating claim: 'an instructional principle at a particular level of complexity will be effective for knowledge components of similar complexity and those of greater complexity'.

"We hypothesize that an instructional principle at a particular level of complexity will be effective for knowledge components of similar complexity and those of greater complexity, but not for ones of less complexity."

Kenneth R. Koedinger 2010 (2)¶

Kenneth R. Koedinger, Albert T. Corbett, Charles Perfetti. (2010). The Knowledge-Learning-Instruction (KLI) Framework: Toward Bridging the Science-Practice Chasm to Enhance Robust Student Learning. Carnegie Mellon University Technical Report. http://learnlab.org/research/wiki

q1 · i? · theoretical · r3

The article presents this as a specific instance of the complexity-matching hypothesis, contrasting self-explanation prompting for complex knowledge components like electric field principles against simpler ones like English articles. It is stated as a hypothesis instance, not an experimental finding in this paper.

"the moderately complex instructional principle of prompting students to self-explain (or asking deep questions) is effective and efficient for learning more complex knowledge components (e.g., principles, like electric field properties, in mathematics and science) but is not effective and efficient for learning less complex knowledge components (e.g., categorical decisions, like English articles, in second language learning)"

Discussion¶

Related Claims¶

• Students are significantly worse at translating two-step algebra story problems than matched one-step problems, implicating a hidden integrative knowledge component
• Simpler knowledge components are executed faster: correct application times rise from about 3-6 seconds for constant-constant KCs to 10-14 seconds for variable-variable KCs
• Multiple-hypothesis strategies improve performance only when simulation complexity is low — related
• Learning relational categories becomes more difficult as the complexity of the knowledge type increases — related
• Model progression benefits learning only in sufficiently complex simulations; in a simple domain it was less efficient than full complexity from the start — related