Conclusion: Future Considerations for Designing Instructions in High-Stakes Learning Environments

Abstract

We started this book from the perspective that high-stakes domains are those in which the decisions made by individuals or groups have significant consequences for the preservation of human lives, as well as broader social, legal, ethical, economic and environmental consequences. Although the various high-stakes chapters in Part II of this book present some clear differences between high-stakes settings, the theoretical considerations in the chapters in Part I of this book constitute a foundation for any high-stakes setting. Despite the fact that the factors that contribute to task or problem complexity may to some extent differ from one high-stakes setting to another, the instructional design must respect the narrow limits of human working memory and facilitate the development and automation of cognitive schemas through evidence-based principles and guidelines. These cognitive schemas stored in long-term memory determine what information elements must be processed with less effort (type 1 processing) or with more effort (type 2 processing). Routine tasks, especially among experienced professionals and experts, often lend themselves for type 1 processing. However, irregularities, anomalies and anticipating or checking for errors typically require type 2 processing. Since in practice many tasks may involve both routine aspects and deviations from ‘the normal’ or ‘the typical’, processing for tasks that involve fewer abnormalities should be closer to what we view as type 1 processing than the processing for tasks where abnormalities are more common. The latter may be even more the case when the risks and consequences of overlooking an abnormality or of a failure to timely anticipate or detect an error are large. Although there is contention in some domains with regard to the nature and achievability of expertise, high-stakes environments appear to call for a special type of expertise that may have relevance across domains: adaptive expertise or the ability to adapt to unknown territory.