Representational scripting for carrying out complex learning tasks

Abstract

Learning to solve complex problems is important because in our rapidly changing modern society and work environments knowing the answer is often not possible. Although educators and instructional designers acknowledge the benefits of problem solving, they also realize that learners need good instructional support to make their problem-solving process efficient and effective. To this end, this thesis introduces an instructional approach representational scripting - and examines how and why it affects the learning process and learning results. Integrating scripting with multiple representational tools sequences different part-task demands, makes those demands explicit and tailors the congruence of each tool’s content related guidance to the demands of each part-task. The studies in this thesis strongly indicate that providing (i.e., passive scripting) and constructing (i.e., active scripting) part-task congruent representations guides a team’s collaboration process and betters their performance on a complex business-economics problem. To avoid turning complex learning into another brick in the wall (Pink Floyd, 1979), this thesis advocates proper guidance for learners, namely supporting them in gradually developing and applying their understanding of the domain. Educators and instructional designers should, thus, introduce qualitative representations before quantitative ones, and both, in a part-task congruent manner