This installment describes another step toward a proof-of-concept narrative of a Learning Efficiency Analysis Paradigm (aLEAP). It describes the kind of scientific data used to assemble an infrastructure of behavior pattern choice points and options learners use to meet learning criteria during lessons.
Learning efficiency indexes the frequency of trial-and-error choices compared with errorless learning. aLEAP clarifies relationships among components of a heuristic for software programmers to automate progress a learner shows while learning. Instructors and learners may use these data to increase learning rates.
The previously posted abstract offers an overview of the project. Future drafts will add academic “meat” that a software developer uses to code a prototype program for use with a Tablet PC.
Introduction
Behavioral and social scientists have systematically studied human learning for over 100 years. Their studies require counting observations of human behavior. With counting, they have developed ways to examine and forecast achievement, intelligence, interests, and personality as separate and interacting phenomena with measured levels of confidence. These developments have resulted in a huge library of ways to describe with precision and generality how people learn.
Two distinct research conventions exist among these efforts. They sometimes use different technical definitions of shared words and logic.
One gives priority to describing observed behavior patterns. It uses as few theories and inferences about cause and effect of these patterns as scientific language permits.
Another convention uses theories to focus observations and to infer how and why learning occurs through cognition, motivation, biology, and other processes.
Educators, learners, and software developers have tried, mostly unsuccessfully, to increase students’ academic and other social performance dramatically by applying research results and explanations from both conventions.
Development of a Learning Efficiency Analysis Paradigm (aLEAP) follows the behavioral convention, more specifically, experimental empirical behavioral research. In this way, it offers potential users a common vocabulary, grammar, and visual illustrations of relationships among scientific descriptions of how people learn.
Software developers may use it to automate judgments teachers make about the likelihood of students’ behavior patterns meeting learning criteria of lessons. Teachers may use these commonalities to refine lessons intended to accelerate academic performance promptly.
Heiny, R. A Learning Efficiency Analysis Paradigm (aLEAP) Abstract. Posted by The Tablet PC In Education Blog August 07, 2009 at 3:54 AM.