Bandura Theories On Social Cognition

Albert Bandura`s social learning theory places learning in a social context. Bandura and his colleagues take the position that personality is acquired, or learned behavior. In particular, Bandura`s insistence that behavior can be learned from mere observation is a significant departure from Skinner’s behaviorist position. An original empirical demonstration of observational learning was presented in a study by Bandura, Ross, and Ross (1993). Nursery school children were allowed to watch an adult’s unusual aggressive actions against an inflated Bobo doll – the kind that pops back up after it has been punched or knocked down.The adult models hit the doll with a hammer and kicked it, tossed it in the air, and even sat on it and punched it. After merely observing this behavior, the children were later allowed to play with toys that included the Bobo doll and hammer. The children who observed the adult model, either live or on videotape, hit the doll more frequently t han a control group who had not seen a model. They also tended to hit the doll the way they had observed the adult model do it. Bandura interpreted this study as demonstrating that the probability of behavior can be strengthened through observation.Indeed, in Bandura`s approach to personality, much of one’s behavior is learned and strengthened through imitation, which is a kind of social cognition learning. In this term paper I address the difference in the effectiveness of using simulation intervention program based on a Bandura`s Social 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 term paper focuses on using simulation based learning environments in vocational training program.In this paper, the experimental methodology and instruments are described, results and findings presented and finally discussed and concluded. METHODOLOGY Do ing my research on Bandura`s Social learning theory 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 the participants were so obvious that I 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 Univer sity 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. Purposes of the Study 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.As mentioned above, my primary purpose when I started to investigate learning and simulation based on Bandura`s Social Cognition theories 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 mate rial 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 b ecause 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 EnvironmentThe 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 i n 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 st ates 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 me ntioned 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