Self-Efficacy Data Essay

Custom Student Mr. Teacher ENG 1001-04 19 May 2017

Self-Efficacy Data

INTRODUCTION Instruments: Self-efficacy is said to influence the choices people make, their goal aspirations, how much effort they mobilize in a given endeavor, how long they persevere in the face of difficulties, their vulnerability to stress and depression in coping with taxing demands, and their resilience to adversity (Bandura, Debowski, & Wood, 2001).

In this study self-efficacy (measured in the between-learning and transfer phases) was measured with four items in which participants were asked to rate after the learning phase, e.g. , how sure they feel that they understood the chemical plant, how to handle the simulation, and how variables can be influenced (Scoring: 1 = low self-efficacy, 6 = high self-efficacy).

In addition to self-efficacy, the participants learning motivation was measured at the end of the whole experiment. In three items, subjects rated their motivation to take part in such an experiment again, and the motivational potential of this kind of learning tasks (Scoring: 1 = very low motivation, 6 = very high motivation).

Knowledge Test The ColorSim training phase requires the acquisition of knowledge differing in levels of precision in the following manner: • semi-qualitative knowledge: to identify a relationship, e. g. , “If you change the values of x, z, and y, those of black and yellow alter as well. ” • qualitative knowledge: to identify features like exponential growth and side-effects. • quantitative knowledge: to specify the exact weight of influence (e. g., “If you change the value of x, black increases by 10 times the value of x), or to recognize the exact simulation algorithm (e. g. , “Yellowt+1= 2 * yt + 0. 5 * zt + 0. 9 * yellowt“).

The knowledge test contains 24 multiple-choice questions. Twelve items require (semi-) qualitative knowledge, the other half requires quantitative knowledge. The total knowledge test scores represented the percentage of correct answers. Hypotheses and Research Questions In pursuance of the purpose of this study, the following two hypotheses were formulated for testing: 1.

HO^sub 1^: The use of the Bandura learning theory in the teaching of a highly technical training program will have no significant effect on the two groups of subjects’ quality and speed of the learning process. 2. HO^sub 2^: The use of the Bandura learning theory in the teaching of a highly technical training program will have no significant effect on the two groups of subjects’ perception of their learning experiences in a highly technical course. Data Collection 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). Table 1. Linear Equations of the Two ColorSim Variants Variant “ColorSim 5” (5 relations between variables) Green t+1 = 10* xt Black t+1 = 3 * zt + 1. 0 * blackt Yellow t+1 = 2 * yt + 0. 5 * zt.

Variant “ColorSim 7” (7 relations) Green t+1 = 10* xt + 1. 1 * greent Black t+1 = 3 * zt + 1. 0 * blackt + 0. 9 * yellowt Yellow t+1 = 2 * yt + 0. 5 * zt 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.

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