Cognitive load theory, educational research, and instructional design: some food for thought

Cognitive load is a theoretical notion with an increasingly central role in the educational research literature. The basic idea of cognitive load theory is that cognitive capacity in working memory is limited, so that if a learning task requires too much capacity, learning will be hampered. The recommended remedy is to design instructional systems that optimize the use of working memory capacity and avoid cognitive overload. Cognitive load theory has advanced educational research considerably and has been used to explain a large set of experimental findings. This article sets out to explore the open questions and the boundaries of cognitive load theory by identifying a number of problematic conceptual, methodological and application-related issues. It concludes by presenting a research agenda for future studies of cognitive load

Software scaffolds to promote regulation during scientific inquiry learning

This research addresses issues in the design of online scaffolds for regulation within inquiry learning environments. The learning environment in this study included a physics simulation, data analysis tools, and a model editor for students to create runnable models. A regulative support tool called the Process Coordinator (PC) was designed to assist students in planning, monitoring, and evaluating their investigative efforts within this environment. In an empirical evaluation, 20 dyads received a “full” version of the PC with regulative assistance; dyads in the control group (n = 15) worked with an “empty” PC which contained minimal structures for regulative support. Results showed that both the frequency and duration of regulative tool use differed in favor of the PC+ dyads, who also wrote better lab reports. PC− dyads viewed the content helpfiles more often and produced better domain models. Implications of these differential effects are discussed and suggestions for future research are advanced

Immunity in barren and enriched housed pigs differing in baseline cortisol concentration

It was shown recently that barren housed pigs (small pens, no substrate) have a blunted circadian rhythm of salivary cortisol as compared to enriched housed pigs (large pens with daily fresh bedding). In the light period, enriched housed pigs showed significantly higher concentrations of cortisol in saliva than barren housed pigs, whereas in the dark period, cortisol concentrations were low in both enriched and barren housed pigs. In the present study, the immunological consequences of the difference in baseline salivary cortisol concentration in the light period were evaluated. It appeared that leukocyte and lymphocyte distributions, and in vitro lymphocyte proliferation following ConcanavalineA (ConA) stimulation in the assay using purified lymphocytes were not affected. However, barren and enriched housed pigs did show a different proliferation response to ConA in the whole blood assay. At day 2 of culture, proliferation was higher in barren housed pigs than in enriched housed pigs, whereas at day 4 of culture, proliferation was higher in enriched housed pigs than in barren housed pigs. Lymphocyte proliferation at day 2 of culture in the whole blood assay correlated negatively with plasma cortisol levels, which might thus explain the higher proliferation in barren housed pigs at day 2 of culture. The in vivo humoral and cellular (delayed type hypersensitivity, DTH) immune response to KLH was not affected by housing conditions. We conclude that, although baseline salivary cortisol concentrations differ between enriched and barren housed pigs, immune function appears to be relatively unaffected.

What Can Be Learned from Computer Modeling? Comparing Expository and Modeling Approaches to Teaching Dynamic Systems Behavior

Computer modeling has been widely promoted as a means to attain higher order learning outcomes. Substantiating these benefits, however, has been problematic due to a lack of proper assessment tools. In this study, we compared computer modeling with expository instruction, using a tailored assessment designed to reveal the benefits of either mode of instruction. The assessment addresses proficiency in declarative knowledge, application, construction, and evaluation. The subscales differentiate between simple and complex structure. The learning task concerns the dynamics of global warming. We found that, for complex tasks, the modeling group outperformed the expository group on declarative knowledge and on evaluating complex models and data. No differences were found with regard to the application of knowledge or the creation of models. These results confirmed that modeling and direct instruction lead to qualitatively different learning outcomes, and that these two modes of instruction cannot be compared on a single “effectiveness measure”