Root node

PSLC theoretical hierarchy’s Root Node

Abstract

PSLC research is primarily concerned with finding out what instructional environments, methods or activities causes students’ learning to be robust. Although normal learning can be measured with immediate, near-transfer post-tests, we measure robustness with three addition measures: retention, far transfer and preparation for learning.

Glossary

The current glossary it has not yet been updated to reflect recent work by the clusters on their glossaries.

Glossary Item

• accelerated future learning
• automaticity
• accessibility
• availability
• chunking
• competition
• consolidation
• coordinated input training
• cue strength
• cue validity
• declarative
• entrenchment
• explicit instruction
• features
• fluency
• fluency and refinement
• foundational skill building
• headroom
• human learning
• implicit instruction
• instruction and learning events practice
• integration
• knowledge components
• learning cycle
• learning events
• level adjustment
• linking
• long-term retention
• modeling
• procedural
• refinement
• reliability
• robustness
• savings in re-learning
• scheduling
• sense making
• stability
• transfer
• working memory

Definitions of dependent measures for normal and robust learning are given in the this PowerPoint file [Michael: Please add Ken's Powerpoint from the lunch talk].

Research question

What instructional activities or methods cause students’ learning to be robust?

Dependent variables

Measures of basic learning (an immediate, near-transfer post-test) and measures of robust learning (retention, far-transfer and preparation for future learning)

Independent variables

Instructional activities and methods.

Hypotheses

Learning will be robust if the instructional activities are designed to include appropriate paths, and the students tend to follow those paths during instruction.

Explanation

Instructional activities influence the depth and generality of the students’ acquired knowledge components, the knowledge components’ strength and feature validity, and the student’s motivation. These in turn influence the students’ performance on measures of robust learning. That is, we take a cognitive stance, rather than a radically distributed or situated stance.

At the macro-level, instruction produces robust learning if it increases the frequency of:

• sense-making processes: rederivation, adaptation and self-supervised learning
• and foundational skill-building processes: strengthening, deep feature perception and cognitive headroom.

At the micro-level, instruction produces robust learning if:

• The instruction is designed so that the learning event space has some target paths that would cause an ideal student to acquire knowledge that is deep, general, strong and retrieval-feature-valid.
• Most students follow a target path most of the time. There are many factors outside the easy control of the experimenter or instructor, such as motivation and recall, that affect whether students actually follow the target paths designed into the instruction.

Descendents

• Interactive Communication.
• Coordinative Learning.
• Refinement and Fluency.

Annotated bibliography

Forthcoming.