The simulation-based learning environment Essay
The simulation-based learning environment
The computer-based simulation ColorSim, which we had developed for our experimental research previously, was used in two different variants. The simulation is based on the work by Funke (1993) and simulates a small chemical plant to produce colors for later subsequent processing and treatment such as dyeing fabrics. The task is to produce a given amount of colors in a predefined number of steps (nine steps). To avoid the uncontrolled influence of prior knowledge, the structure of the plant simulation cannot be derived from prior knowledge of a certain domain, but has to be learned by all subjects.
ColorSim contains three endogenous variables (termed green, black, and yellow) and three exogenous variables (termed x, y, and z ). Figure 1 illustrates the ColorSim screen. Subjects control the simulation step by step (in contrast to a real time running continuous control). The predefined goal states of each color have to be reached by step nine. Subjects enter values for x, y, and z within the range of 0-100. There is no time limit for the transfer tasks. During the transfer tasks, the subjects have to reach defined system states for green (e. g. , 500), black (e. g.
, 990), and yellow (e. g. , 125) and/or try to keep the variable values as close as possible to the values defined as goal states. Subjects are instructed to reach the defined system states at the end of a multi-step process of nine steps. The task for the subjects was first to explore or learn about the simulated system (to find out the causal links between the system variables), and then to control the endogenous variables by means of the exogenous variables with respect to a set of given goal states. With respect to the empirical evidence of Funke (2001) and Strau?
(1995), the theoretical concept for the variation in complexity is based on Woods’ (1986) theoretical arguments that complexity depends on an increasing number of relations between a stable number of (in this case six) variables (three input, three output: for details of the construction rational and empirical evidence (Kluge, 2004) Altogether, empirical findings and theoretical assumptions have so far led to the conclusion that experiential learning needs additional support to enhance knowledge acquisition and transfer. Target Population and Participant Selection:
In the introductory part, I mentioned that there were two sub groups in the sample which I see as different target groups for using simulation-based learning environments. Subjects were for the most part recruited from the technical departments of a Technical University (Mechanical Engineering, Civil Engineering, Electronics, Information Technology as well as apprentices from the vocational training programs in mechanics Figure 1. ColorSim5 and ColorSim7 screen shot and electronics of several companies in the University area. Students were about 20-23 years old, apprentices were about 16-19 years old.
I admit that this is going to be a point to be discussed in terms of internal validity of the experiment in the discussion section: not only the educational background of both groups differ, but also their age and schooling experience. However, these conditions contribute to the discussion later in the next chapter. Procedures: The procedure subjects had to follow included a learning phase in which they explored the structure of the simulation aiming at knowledge acquisition. After the learning phase, subjects first had to fill in the four-item questionnaire on self-efficacy before they performed 18 transfer tasks.
The transfer tasks were separated into two blocks (consisting of nine control tasks each) by a 30-minute break. In four experimental groups (EG), 117 students and apprentices performed the learning phase (28 female participants), the 18 control tasks and the knowledge test. As said before, the knowledge test was applied at the end because of its sensitivity to additional learning effects caused by filling in the knowledge test. In four control groups (CG), 98 students and apprentices performed the knowledge test directly after the learning phase, without working on the transfer task (four female participants).
The EGs took about 2-2. 5 hours and the CG about 1. 5 hours to finish the experiment. Both groups (EGs and CGs) were asked to take notes during the learning phase. Subjects were randomly assigned to the EGs and CGs, nonetheless ensuring that the same number of students and apprentices were in each group. Experiential Learning Support Methods Used Two different types of learning support were developed: Guided exploratory search (GES), which consists in the support in designing an experiment, and a combination of direct instruction and guided exploratory search (DI-GES), which comprises direct access to the knowledge domain.
The guided exploratory search (GES) provided the opportunity to look actively for relations between the input and output variables by testing hypothesis (Funke, 1993, 2001; Kamouri, Kamouri, & Smith, 1986; Vollmeyer, Burns, & Holyoak, 1996; Wallach, 1996). No direct instructions were given. Subjects had to follow four steps: in three separated screens they were required to enter input values for x, or y, or z and observe the consequences. On the last (fourth) screen, all input variables could be altered to see how variables interact with each other.
So subjects were guided into manipulating each variable one after another to draw clear conclusions about relationships. The last screen allowed them to manipulate all variables in parallel to explore interactions between variables. The learning phase was standardized to 15 minutes all together. The direct instruction and guided exploratory search (DI-GES) used the ColorSim screen shot to illustrate and explain the relations and their weights between variables before subjects had the opportunity to discover and practice these directly (Kluwe, 1997, 1996; Merril, 1991; Putz-Osterloh, 1993; Rieber, 1992).
The subjects are guided through four screens. In the first one, learners were given direct instruction about the relation between x and the other variables for 100 seconds, which they had to actively explore while observing the relations in a “try-out simulation” phase (meaning, that they were asked to enter input values for x). Then variable y and its influences were explained, and so on. Finally, all relationships were shown, and learners had the chance to control all three variables in parallel. All in all, subjects had 15 minutes maximum to learn and explore all relations.