Instruction-based knowledge acquisition and modification: the operational knowledge for a functional, visual programming language

Abstract This contribution deals with instruction-based knowledge acquisition in a fairly complex but well-defined domain. The domain is the operational knowledge about the interpreter of ABSYNT, a functional, visual programming language which was developed in our project. Runnable specifications of the ABSYNT-interpreter were translated into sets of visual rules, serving as instructional material for students to acquire the operational knowledge. We are concerned with the following questions: 1. 1. How do subjects acquire the operational knowledge while simulating the interpreter of ABSYNT with the help of the instructional material? 2. 2. How can the operational knowledge gained by subjects be described? For example, does this knowledge differ from the instructional material? If the mental representation of the operational knowledge is isomorphic to the instructional material, then hypotheses about certain performance aspects can be stated. An experiment was conducted in which dyades of programming novices acquired the computational knowledge for ABSYNT by computing the value of ABSYNT-programs with the help of the instructions, thus simulating the interpreter. The hypotheses were disconfirmed. The results suggest that the mental representation of the operational knowledge consists of larger units than the instructional material, leading to the following hypotheses about the acquisition process and the mental representation of the operational knowledge: 1. 1. When faced with a difficulty, there will be problem solving with the help of the instructions. Thus new knowledge is acquired by failure-driven learning. 2. 2. When faced with familiar situations, compound rules are built. Thus the existing knowledge is improved by success-driven learning.

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