The purpose of this case study is to reveal prospective science teachers knowledge and achievement levels in electricity-related subjects. The data for the study were collected from 44 prospective teachers using three measurement tools. The data were then analyzed using software developed for the Probability and Possibility Calculation Statistics for Data Variables method, developed by Y?lmaz (2011). It was concluded that prospective science teachers achievement levels in mathematical logic in electricity-related subjects are influenced by their achievement levels in physics and basic mathematical procedures, as well as their knowledge levels. However, it was observed that the main influence on their achievement level in mathematical logic is the logical structure of knowledge, and not the knowledge level in the variables given-asked and operations. Based on the findings of this study, it is recommended that both the methods of teaching knowledge and teaching the logical structure of knowledge be incorporated into the educational-instructional process. The study emphasizes that it would be optimistic to expect that individuals who learn without an awareness of the logical structure of knowledge will reach their potential.
This study reveals the transformation of prospective science teachers into knowledgeable individuals through classical, combination, and information theories. It distinguishes between knowledge and success, and between knowledge levels and success levels calculated each through three theories. The relation between the knowledge of prospective teachers and their cognitive functions is defined through the results gained from three theories, and a case study that collected data through problem solving techniques in the procedural knowledge of electricity. The results reveal that prospective teachers have problems with such knowledge, which may explain why cognitive automatism is not used. Since processes of understanding are not used in cognitive automatism, it appears that prospective teachers are individuals that may differ in terms of their learning but do not use their cognitive functions. The study suggests that if the knowledge levels of independent variables are increased, cognitive functions may develop.
This study draws on the understanding that when the correlation between variables is not known yet the non-linear expectation in the correlation between the variables is present, non-linear measurement tools can be used. In education, possibility measurement tools can be used for non-linear measurement. Multiple-choice possibility measurement tools (MCPMT) can be prepared similarly to conventional multiple-choice measurement tools (CMCMT) utilized in quantitative measurements. In comparison with CMCMT, both more qualified measurements and more qualified evaluations can be carried out via possibility measurement tools; therefore, the preparation techniques of MCPMT, which is one possibility measurement tool, which can be used in information-centered and learner-centered measurements, are set forth in this study. MCPMT can resolve the problem of CMCMT in terms of the measurement of different variables with multiple options in one item. Additionally, the correlation between the variables can be determined by evaluating data obtained via MCPMT by means of two different new methods. Key word: Multiple-choice measurement tool (MCMT), multiple-choice possibility measurement tool (MCPMT), item techniques (IT), option technique (OT).
When correlation between variables is not explicit, data can be collected by adapting the quantitative measurement tools in use for quantitative measurement of possibility, a nonlinear measurement. This adaptation is possible because measurement data can be evaluated more qualitatively using parameters for possibility. These can be defined as regular-symmetric, irregular-symmetric, symmetric with regard to situation at which the distribution begins, event-based symmetric, symmetricalcontiguous, and of symmetrical discrimination, all available using possibility measurement tools. Without modifying the structure of conventional quantitative measurement tools, their premeasurement adaptation can be carried out, making quantitative possibility measurement tools. This is made possible by converting scale values and scale options of each measurement tool to situation numbers and event numbers. Post-measurement adaptation can be carried out by converting the value measured to a symmetrical situation number. In this study, adaptation techniques and principles will be provided, for conventional quantitative measurement tools which will be classified according to their scale indicators and then used for quantitative measurements of possibility.
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