Gestalt Learning Theory Essay Example for Free

Gestalt Learning Theory leavenDoing my research on cognition and instruction in analyzable make-believe- ground information environments, I experienced a large difference in how arresters reacted to my learning solid (Kluge, in press, 2004). Complex technical simulations involve the post of the learner into a realistic computer simulated situation or technical scenario which puts turn back bear into the learners hands. The contextual content of simulations allows the learner to learn by doing. Although my primary purpose was in improving research methods and running gameing procedures for evaluating learning results of simulation-establish learning, the different reaction of our participants were so obvious that we took a at hand(predicate) look. I had cardinal different groups participating in my learning experiments students from an engineering department at the University, more often than non in their 3rd semester, and apprentices from vocational training progr ams in mechanics and electronics of several companies near the University ara 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 among these groups, colleagues and practiti wizardrs showed their interest and encouraged me to look especially at that difference. Practitioners especially hoped to find explanations why apprentices sometimes atomic number 18 less enthusiastic virtually simulation learning although it is said to be motivating for their perception.Therefore, in this oration 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 prize and speed of the learning process of students enrolled in a highly technical train ing program. This dissertation focuses on using simulation based learning environments in vocational training program. In this chapter, the data-based 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 ( follow out 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 capacity 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 Simulation complexness ColorSim 5 vs ColorSim 7 factor 2 support method GES vs. DI-GES factor 3 target group, see Table 2). Two hundred and fifteen mostly male students (16% female person) in eight groups (separated into iv experimental and four control groups) participated in the main study. The control group served as a treatment curtail for the learning phase and to demonstrate whether subjects acquired any companionship within the learning-phase.While the experimental groups filled in the knowledge test at the goal of the experiment (after the learning and the convert 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 repayable to the learning method but caused by learning from taking the knowledge test. The procedure subjects had to follow include a learning phase in which they seekd the body 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 t o 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 standard 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 in condition(p) 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 C olorSim screen. Subjects control the simulation step by step (in contrast to a real time running free burning control). The predefined goal states of each color have to be touched by step nine. Subjects enter determine 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 puddle 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 mingled with the system variables), and then to control the endogenous variables by means of the exogenous variables with respect to a piece of given goal states. With respect to the empirical evidence of Funke (200 1) 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 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 d omain knowledge, which means that learners should know something about the field or subject beforehand, if discovery learning is to be fruitful. 2. have a bun in the oven 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 lifelike 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 spillage learners from the necessity of making decisions and thus helping them to manage the learning process. In addition, regulative processes c an be supported by leading the learner through different stages, like Before doing the experiment . . . , straight do the experiment, After doing the experiment. . . . Altogether, empirical findings and theoretical assumptions have so far led to the conclusion that existential learning needs additional support to enhance knowledge acquisition and transfer.Target Population and player 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