Gestalt Learning Theory Essay

Doing my research on learning and instruction in complex simulation-based learning environments, I experienced a large difference in how learners reacted to my learning material (Kluge, in press, 2004). Complex technical simulations involve the placement of the learner into a realistic computer simulated situation or technical scenario which puts control back into the learner’s hands. The contextual content of simulations allows the learner to â€Å"learn by doing. † Although my primary purpose was in improving research methods and testing procedures for evaluating learning results of simulation-based learning, the different reaction of our participants were so obvious that we took a closer look. I had two different groups participating in my learning experiments: students from an engineering department at the University, mostly in their 3rd semester, and apprentices from vocational training programs in mechanics and electronics of several companies near the University area in their 3rd year of vocational training. Most of the students worked very intensively and concentrated on solving these complex simulation tasks whereas apprentices became easily frustrated and bored. Although my first research purpose was not in investigating the differences between these groups, colleagues and practitioners showed their interest and encouraged me to look especially at that difference. Practitioners especially hoped to find explanations why apprentices sometimes are less enthusiastic about simulation learning although it is said to be motivating for their perception. Therefore, in this dissertation I address the difference in the effectiveness of using simulation intervention program based on a Gestalt learning theory. Moreover, to find out if the program improves either or both the quality and speed of the learning process of students enrolled in a highly technical training program. This dissertation focuses on using simulation based learning environments in vocational training program. In this chapter, the experimental methodology and instruments are described, results presented and finally discussed. As mentioned above, my primary purpose when I started to investigate learning and simulation based on Gestalt learning theory was focused on improving the research methodology and test material (see Kluge, in press, 2004) for experimenting with simulation-based learning environments. But observing the subjects’ reactions to the learning and testing material the question arose whether there might be a difference in the quality of and speed of the learning process of students involved in my study. Research Design: A 3-factor 2 ? 2 ? 2 factorial control-group-design was performed (factor 1: â€Å"Simulation complexity†: ColorSim 5 vs ColorSim 7; factor 2: â€Å"support method†: GES vs. DI-GES; factor 3: target group, see Table 2). Two hundred and fifteen mostly male students (16% female) in eight groups (separated into four experimental and four control groups) participated in the main study. The control group served as a treatment check for the learning phase and to demonstrate whether subjects acquired any knowledge within the learning-phase. While the experimental groups filled in the knowledge test at the end of the experiment (after the learning and the transfer tasks), the control groups filled in the knowledge test directly after the learning phase. I did not want to give the knowledge test to the experimental group after the learning phase because of its sensitivity to testing-effects. I assumed that learners who did not acquire the relevant knowledge in the learning phase could acquire useful knowledge by taking the knowledge test, which could have led to a better transfer performance which is not due to the learning method but caused by learning from taking the knowledge test. 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. 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 see Kluge, 2004, and Kluge, in press, see Table 1). To meet reliability requirements, subjects had to complete several trials in the transfer task. For each of the 18 control tasks a predefined correct solution exists, to which the subjects’ solutions could be compared. In addition, knowledge acquisition and knowledge application phases were separated. The procedure for the development of a valid and reliable knowledge test is described in the next section. Different methods have been developed to provide learners with support to effectively learn from using simulations. De Jong and van Joolingen (1998) categorize these into five groups: 1. Direct access to domain knowledge, which means that learners should know something about the field or subject beforehand, if discovery learning is to be fruitful. 2. Support for hypothesis generation, which means learners are offered elements of hypotheses that they have to assemble themselves. 3. Support for the design of experiments, e. g. , by providing hints like â€Å"It is wise to vary only one variable at a time† 4. Support for making predictions, e. g. , by giving learners a graphic tool in which they can draw a curve that gives predictions at three levels of precision: as numerical data, as a drawn graph, and as an area in which the graph would be located. 5. Support for regulative learning processes: e. g. , by introducing model progression, which means that the model is introduced gradually, and by providing planning support, which means freeing learners from the necessity of making decisions and thus helping them to manage the learning process. In addition, regulative processes can be supported by leading the learner through different stages, like â€Å"Before doing the experiment . . . ,† â€Å"Now do the experiment,† â€Å"After doing the experiment. . . .† 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