Gestalt Learning Theory Essay

Doing my search on bring about and instruction in complex theoretical account-establish study environments, I experienced a banging difference in how scholars reacted to my acquisition material (Kluge, in press, 2004). Complex technical good examples involve the placement of the learner into a realistic computer simulated situation or technical scenario which puts prevail back into the learners hands. The contextual content of simulations allows the learner to learn by doing. Although my primary purpose was in up(a) research methods and turn uping 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 tertiary semester, and apprentices from vocational training programs in mechanism and electronics of several companies near the University atomic number 18a in their 3rd year of vocational training.Most of the students die harded very intensively and concentrated on solving these complex simulation jobs whereas apprentices became easily frustrated and bored. Although my counterbalance research purpose was not in investigating the differences between these groups, colleagues and practitioners showed their rice beer and encouraged me to look especially at that difference. Practitioners especially hoped to find explanations why apprentices sometimes are less(prenominal) 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 utilize simulation intervention program based on a Gestalt learning theory. Moreover, to find out if the program improves either or both the quality and speed up of the learning process of students enrolled in a highly technical training program. This dissertatio n focuses on victimisation simulation based learning environments in vocational training program. In this chapter, the experimental methodology and instruments are set forth, 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 ( bump into 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. seek Design A 3-factor 2 ? 2 ? 2 factorial control-group-design was performed (factor 1 Simulation complexity ColorSim 5 vs ColorSim 7 factor 2 support method GES vs. DI-GES factor 3 target group, see dodge 2). Two hundred and fifteen mostly male students (16% female) in eight groups (separated into four-spot 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 association inwardly the learning-phase.While the experimental groups filled in the familiarity test at the end of the experiment (after the learning and the beam 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 victorious the knowledge test, which could convey 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 attainment. 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 baseball club 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 arbitrarily 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 actual 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 ingraft to produce colors for later subsequent bear on and treatment such as dyeing fabrics. The task is to produce a attached amount of colors in a preoutlined 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 proteans (termed green, black, and yellow) and three exogenous variables (termed x, y, and z ).Figure 1 illustrates the ColorSim screen. Subject s control the simulation step by step (in contrast to a real time running continuous control). The preoutlined close 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 clay 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 certify of Funke (2001) and Strau? (1995), the theoretical co ncept for the variation in complexity is based on Woods (1986) theoretical arguments that complexity depends on an increasing number ofrelations 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 learner s should know something about the arena or subject beforehand, if discovery learning is to be fruitful. 2. Support for hypothesis generation, which means learners are offeredelements of hypotheses that they have to assemble themselves. 3. Support for the design of experiments, e. g. , by providing hints like It is wise to vary only one variable at a time 4. Support for making predictions, e. g. , by giving learners a graphic machine 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 exigency of making decisions and thus helping them to manage the learning process. In addition, regulative processes can be support by leading the learner through different stages, like Before doing the experiment . . . , Now do the experiment, 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 excerption 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 skilful University (Mechanical Engineering, Civil Engineering, Electronics, Information Technology as well as apprentices from the vocational training programs in mechanics