The Experts below are selected from a list of 610509 Experts worldwide ranked by ideXlab platform
Matthew W. Ohland - One of the best experts on this subject based on the ideXlab platform.
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Measuring Engineering Design self-efficacy
Journal of Engineering Education, 2010Co-Authors: Adam R Carberry, Hee Sun Lee, Matthew W. OhlandAbstract:BACKGROUND Self-concept can influence how an individual learns, but is often overlooked when assessing student learning in Engineering. PURPOSE (HYPOTHESIS) To validate an instrument Designed to measure individuals' self-concepts toward Engineering Design tasks, three research questions were investigated: (a) how well the items in the instrument represent the Engineering Design process in eliciting the task-specific self-concepts of self-efficacy, motivation, outcome expectancy, and anxiety, (b) how well the instrument predicts differences in the self-efficacy held by individuals with a range of Engineering experiences, and (c) how well the responses to the instrument align with the relationships conceptualized in self-efficacy theory. Design/METHOD A 36-item online instrument was developed and administered to 202 respondents. Three types of validity evidence were obtained for (a) representativeness of multi-step Engineering Design processes in eliciting self-efficacy, (b) the instrument's ability to differentiate groups of individuals with different levels of Engineering experience, and (c) relationships between self-efficacy, motivation, outcome expectancy, and anxiety as predicted by self-efficacy theory. RESULTS Results indicate that the instrument can reliably identify individuals' Engineering Design self-efficacy (alpha = 0.967), motivation (alpha = 0.955), outcome expectancy (alpha = 0.967), and anxiety (alpha = 0.940). One-way ANOVA identified statistical differences in self-efficacy between high, intermediate, and low experience groups at the p < 0.05 level. Self-efficacy was also shown to be correlated to motivation (0.779), outcome expectancy (0.919), and anxiety (-0.593) at the P < 0.01 level. CONCLUSIONS The study showed that the instrument was capable of identifying individuals' self-concepts specific to the Engineering Design tasks.
Adam R Carberry - One of the best experts on this subject based on the ideXlab platform.
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Measuring Engineering Design self-efficacy
Journal of Engineering Education, 2010Co-Authors: Adam R Carberry, Hee Sun Lee, Matthew W. OhlandAbstract:BACKGROUND Self-concept can influence how an individual learns, but is often overlooked when assessing student learning in Engineering. PURPOSE (HYPOTHESIS) To validate an instrument Designed to measure individuals' self-concepts toward Engineering Design tasks, three research questions were investigated: (a) how well the items in the instrument represent the Engineering Design process in eliciting the task-specific self-concepts of self-efficacy, motivation, outcome expectancy, and anxiety, (b) how well the instrument predicts differences in the self-efficacy held by individuals with a range of Engineering experiences, and (c) how well the responses to the instrument align with the relationships conceptualized in self-efficacy theory. Design/METHOD A 36-item online instrument was developed and administered to 202 respondents. Three types of validity evidence were obtained for (a) representativeness of multi-step Engineering Design processes in eliciting self-efficacy, (b) the instrument's ability to differentiate groups of individuals with different levels of Engineering experience, and (c) relationships between self-efficacy, motivation, outcome expectancy, and anxiety as predicted by self-efficacy theory. RESULTS Results indicate that the instrument can reliably identify individuals' Engineering Design self-efficacy (alpha = 0.967), motivation (alpha = 0.955), outcome expectancy (alpha = 0.967), and anxiety (alpha = 0.940). One-way ANOVA identified statistical differences in self-efficacy between high, intermediate, and low experience groups at the p < 0.05 level. Self-efficacy was also shown to be correlated to motivation (0.779), outcome expectancy (0.919), and anxiety (-0.593) at the P < 0.01 level. CONCLUSIONS The study showed that the instrument was capable of identifying individuals' self-concepts specific to the Engineering Design tasks.
Hee Sun Lee - One of the best experts on this subject based on the ideXlab platform.
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Measuring Engineering Design self-efficacy
Journal of Engineering Education, 2010Co-Authors: Adam R Carberry, Hee Sun Lee, Matthew W. OhlandAbstract:BACKGROUND Self-concept can influence how an individual learns, but is often overlooked when assessing student learning in Engineering. PURPOSE (HYPOTHESIS) To validate an instrument Designed to measure individuals' self-concepts toward Engineering Design tasks, three research questions were investigated: (a) how well the items in the instrument represent the Engineering Design process in eliciting the task-specific self-concepts of self-efficacy, motivation, outcome expectancy, and anxiety, (b) how well the instrument predicts differences in the self-efficacy held by individuals with a range of Engineering experiences, and (c) how well the responses to the instrument align with the relationships conceptualized in self-efficacy theory. Design/METHOD A 36-item online instrument was developed and administered to 202 respondents. Three types of validity evidence were obtained for (a) representativeness of multi-step Engineering Design processes in eliciting self-efficacy, (b) the instrument's ability to differentiate groups of individuals with different levels of Engineering experience, and (c) relationships between self-efficacy, motivation, outcome expectancy, and anxiety as predicted by self-efficacy theory. RESULTS Results indicate that the instrument can reliably identify individuals' Engineering Design self-efficacy (alpha = 0.967), motivation (alpha = 0.955), outcome expectancy (alpha = 0.967), and anxiety (alpha = 0.940). One-way ANOVA identified statistical differences in self-efficacy between high, intermediate, and low experience groups at the p < 0.05 level. Self-efficacy was also shown to be correlated to motivation (0.779), outcome expectancy (0.919), and anxiety (-0.593) at the P < 0.01 level. CONCLUSIONS The study showed that the instrument was capable of identifying individuals' self-concepts specific to the Engineering Design tasks.
Roger B. Hill - One of the best experts on this subject based on the ideXlab platform.
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Features of Engineering Design in Technology Education
2007Co-Authors: Paul A. Asunda, Roger B. HillAbstract:The purpose of this study was to find critical features of Engineering Design that can be incorporated within technology education learning activities, and develop a rubric for assessing these features. Data were collected through semi-structured interviews with three professors actively involved in Engineering education. Supporting documents such as Engineering Design course outlines and rubrics were also examined. Using a phenomenological approach, this study identified the concept of Engineering Design, key features of the Engineering Design process, and critical elements that should be assessed in an Engineering Design activity in the context of technology education. A key product of the study was development of a rubric to be used in evaluating integration of Engineering Design as a focus for technology education.
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Critical Features of Engineering Design in Technology Education
Journal of Industrial Teacher Education, 2007Co-Authors: Paul A. Asunda, Roger B. HillAbstract:The purpose of this study was to find critical features of Engineering Design that can be incorporated within technology education learning activities, and develop a rubric for assessing these features. Data were collected through semi-structured interviews with three professors actively involved in Engineering education. Supporting documents such as Engineering Design course outlines and rubrics were also examined. Using a phenomenological approach, this study identified the concept of Engineering Design, key features of the Engineering Design process, and critical elements that should be assessed in an Engineering Design activity in the context of technology education. A key product of the study was development of a rubric to be used in evaluating integration of Engineering Design as a focus for technology education.
Holger Luczak - One of the best experts on this subject based on the ideXlab platform.
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Knowledge representation for Engineering Design
International Journal of Product Development, 2005Co-Authors: Ludger Schmidt, Holger LuczakAbstract:Engineering Design tasks are mainly knowledge-intensive cognitive actions. Therefore, an adequate computer-based structure and representation of Engineering Design knowledge is a crucial point to support the Design engineer's cognitive tasks. On the one hand, this knowledge representation should correspond to an intuitive way of thinking. On the other hand, this representation should comply with conscious systematic Design methods without limiting human problem solving. In a first step of an analytical research approach, this contribution is concerned with deriving detailed requirements on a useful knowledge representation for computer-aided Engineering Design. For that purpose, generalised characteristics of human problem solving and information processing can be specified for Design problem solving. According to their fulfilment of these requirements, eight main models of knowledge representation were compared concerning their utility to support human problem solving in Engineering Design. As a result of this evaluation, a useful model of knowledge representation is suggested and illustrated by an example of use.
