The effect of combining emphasis manipulation and simplifying conditions sequencing method in gaining expertise while utilizing whole task sequencing

Despite the efforts of instructional design (ID) to solve real-life problems, it remains challenging to adapt and be flexible in such situations. In particular, problems that require simultaneous knowledge of multiple domains and contexts are more challenging to solve because real-life problems do not reconstruct the learned experience. This is generally thought to stem from differences between learning and real-life practice, but it also stems from instructional designs that fail to reflect the problem's structure and cognitive processes. This study is based on the 4C/ID model and proposes an instructional design for developing and connecting cognitive processes across multidimensional domains and contexts. It employs a simple-to-complex method that combines emphasis manipulation sequencing with simplifying condition sequencing, exposing students to the entire domain and context from the beginning of the learning process to develop a holistic cognitive process. A quasi-experiment was conducted with 34 sophomore college students majoring in education who were asked to create a lesson plan using different teaching styles. The groups consisted of students learning using emphasis manipulation sequencing and single sequencing (emphasis + condition), and the experimental procedure consisted of a total of five sessions, with between-group and within-group analyses of the effects of cognitive strategies and structural models. In the between-group analysis, cognitive strategies and structural models using the single sequencing method were effective from sessions 2 through 5, while in the within-group analysis, the development of cognitive strategies and structural models occurred from sessions 1 through 3, when the simplifying condition sequencing principle was maintained. While the proposed instructional designs are not a foolproof way to develop cognitive processes, a combined approach that considers the nature of the task provides a starting point that can enhance real-life training.