The use of information characteristics to design powerful learning environments has always been at the heart of cognitive load research. In order to promote understanding, the learners' resources should be allocated as much as possible to processes that contribute to schema acquisition. To rephrase this in the terminology used in cognitive load research: The learner's germane load should be optimized and their extraneous load should be minimized (Sweller, Van Merrie ¨nboer, & Paas, 1998;Van Merrie ¨nboer & Sweller, 2005). This important principle is the backbone of many studies conducted ever since the introduction of cognitive load theory (CLT;Sweller, 1988).
This principle immediately provides us with two essential characteristics of a powerful learning environment. First of all, the design of the learning environment itself should be taken into account. How are the learning materials or problems presented to the learner? In what way does the learner interact with the environment? Are there elements in the environment that might be a source of extraneous cognitive load (e.g., split attention effect, redundancy effect)? Secondly, the background of the users should be taken into consideration. What do they already know? What is their motivation to use this learning environment? But also, and often forgotten, what is their age?
The papers in this special issue reflect the continuing endeavour of cognitive load researchers to optimize instructional design by considering the individual characteristics of the learner at all times. Below I will discuss the studies reported in this special issue on Emerging Topics in Cognitive Load Research: Using Information and Learner Characteristics in the Design of Powerful Learning Environments.
In order to examine the effectiveness of reducing task complexity (i.e., intrinsic cognitive load) Ayres (this issue) conducts two experiments in which high school students had to solve mathematical problems. His results showed that low (mathematical) ability students benefited more, in terms of error scores in the test phase, from an isolated or parttask strategy than high ability students, who benefited more from an integrated or wholetask strategy. This study is a nice example of the interaction of instructional method and ability level on performance. Kalyuga and colleagues were the first within a cognitive load framework to demonstrate that design solutions for novices do not necessarily transfer to higher ability levels and vice versa (Kalyuga, Ayres, Chandler, & Sweller, 1998).