September 2022
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2 Reads
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September 2022
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2 Reads
December 2014
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321 Reads
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9 Citations
Journal of Engineering Education
August 2014
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65 Reads
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15 Citations
International Journal of Engineering Education
Self Determination Theory (SDT) states that intrinsic motivation (IM) in a particular context is supported by increasing an individual's sense of autonomy, relatedness, and competence with respect to that context. When instructors use IM-supportive methods, they promote learning of class content. This research seeks to describe through narratives how students’ motivation changes in response to a pedagogy designed with fostering intrinsic motivation as a primary class objective. After being observed in the classroom of an IM-supportive class conversion, students were interviewed to document their narratives. Interview transcripts were coded to describe students' motivational orientation throughout the class. The majority of interviewed students demonstrated increases in intrinsic motivation for studying the class content. The interviews revealed that individual choice, interpersonal relationships, and constructive failure were critical in moving students toward intrinsic motivation. While the IM-supportive learning environment did not affect all students equally, the common themes of individual choice, interpersonal relationships, and constructive failure provide deeper insights into how to improve and assess students’ motivational changes in technical engineering classes.
October 2013
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948 Reads
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9 Citations
Proceedings - Frontiers in Education Conference
The low-cost intrinsic motivation (IM) course conversion project is an effort to create a new system of course design that focuses on creating scalable and sustainable courses that emphasize promoting students' IM to learn. Unlike many course design methods such as idea-based learning, project- or problem-driven learning, or “flipped” classrooms, which first ask, “How do we help students learn X better,” we ask “how do we foster intrinsically-motivated learners who want to learn X?” While this course design method still uses theories of cognition to design course structures, it uses motivational constructs such as purpose, autonomy, relatedness, and competence as the primary design considerations of a course. Secondarily, the course design method considers and documents the financial, time, political, and psychological costs of course design. In this paper, we present a preliminary attempt to formalize this IM-driven course design method as well as a system for evaluating the short- and long-term costs of implementing a specific course design.
June 2013
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135 Reads
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8 Citations
We present our efforts to create a scalable engineering education reform process that has a low barrier to adoption by focusing primarily on promoting students’ intrinsic motivation (IM) to learn. Students who are intrinsically motivated rather than extrinsically motivated to learn are more likely to persist in their learning and perform better. Despite major investments in, and promising innovations for, reforming engineering education, many instructors are slow to adopt these innovations because of prohibitive time, money, and training investments. In contrast, the intrinsic motivation (IM) course conversion project has three goals: (1) to redesign the classroom based on motivational theories, (2) to improve students’ learning by promoting their intrinsic motivation to learn, and (3) to implement the reform through methods that require minimal or zero additional costs to the faculty. We initially piloted one such IM course conversion in a sophomore-level computer engineering course (ECE 290) during the Fall 2011 term with 37 students. This pilot was scaled to encompass the full course in Fall 2012 with 220 students.
June 2012
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4 Reads
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8 Citations
July 2011
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239 Reads
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33 Citations
Soft Computing
Evolutionary algorithms (EAs) are particularly suited to solve problems for which there is not much information available. From this standpoint, estimation of distribution algorithms (EDAs), which guide the search by using probabilistic models of the population, have brought a new view to evolutionary computation. While solving a given problem with an EDA, the user has access to a set of models that reveal probabilistic dependencies between variables, an important source of information about the problem. However, as the complexity of the used models increases, the chance of overfitting and consequently reducing model interpretability, increases as well. This paper investigates the relationship between the probabilistic models learned by the Bayesian optimization algorithm (BOA) and the underlying problem structure. The purpose of the paper is threefold. First, model building in BOA is analyzed to understand how the problem structure is learned. Second, it is shown how the selection operator can lead to model overfitting in Bayesian EDAs. Third, the scoring metric that guides the search for an adequate model structure is modified to take into account the non-uniform distribution of the mating pool generated by tournament selection. Overall, this paper makes a contribution towards understanding and improving model accuracy in BOA, providing more interpretable models to assist efficiency enhancement techniques and human researchers.
January 2011
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13 Reads
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2 Citations
Power plant design using digital engineering based on 3-D computer-aided design has become a mainstream technology because of possessing higher speed and improvement in design accuracy. To take a coal-fired boiler building as an example, it has many complex structures with several million parts including the boiler itself, large fans, steel structures, and piping in varying sizes. Therefore, it is not easy to maintain integrity of the whole design throughout all the many engineering processes. We have developed a smart design system for coal-fired boiler buildings to solve the integrity problem. This system is capable of creating and allocating 3-D models automatically in accordance with various technical specifications and engineering rules. Lately, however, there has been a growing demand for more effectiveness of the developed system. We have begun to look into the feasibility of further improvements of the system function. The first point to note, when considering effectiveness, is the piping path routing process in the coal-fired boiler building. The quantity of piping is large, and it has a considerable impact on performance of the whole plant because hot steam is fed into the steam turbine and cold steam is taken from it through the piping. A considerable number of studies have been made on automatic searching methods of piping path routing. Although, the decision of piping path routing by using the Dynamic Programming method is most commonly, a previously decided routing becomes an interference object because of the single searching method. Therefore, basically, the later the order of the routing becomes, the longer the length of the routing becomes. To overcome this problem, in this paper we have proposed a new searching method based on the Genetic Algorithm (GA). The GA is a multipoint searching algorithm based on the mechanics of natural selection and natural genetics. Virtual prohibited cells are introduced into the proposed search method as a new idea. The virtual prohibited cells are located in a search space. The different paths are generated by avoiding the virtual prohibited cells while searching for the piping path routing. The optimum locations of the prohibited cells which are expressed in a genotype are obtained by using the GA in order to get a lot of paths independent of the order of the routing. The proposed method was evaluated using a simple searching problem. The results showed that many effective paths are generated by making the virtual prohibited cells.
November 2010
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58 Reads
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1 Citation
Proceedings - Frontiers in Education Conference
Reform in engineering education requires not only identifying what needs to change, but also understanding implicit barriers to change and the tools that can help overcome them. Language is an excellent example of such a barrier, and of such a tool. For example, engineering educators sometimes refer to “the basics” (math, science, and engineering science) thereby assigning those topics a privileged position in the engineering canon; the same educators will sometimes use the term “soft” to deflate certain qualitative critical thinking, creative, and communications skills, thereby assigning them lower status - and less air time - in the education of student engineers. These examples demonstrate the ability of language to obstruct change, and also suggest that the careful choice of memorable or “sticky” locations can provide reformers with a powerful means of reframing the debate. In this special session we examine the use of language in the resistance to and promotion of change, and identify promising locutions that can help transform engineering education.
August 2010
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126 Reads
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25 Citations
This paper shows how the extended compact genetic algorithm can be scaled using data-intensive computing techniques such as MapReduce. Two different frameworks (Hadoop and MongoDB) are used to deploy MapReduce implementations of the compact and extended compact genetic algorithms. Results show that both are good choices to deal with large-scale problems as they can scale with the number of commodity machines, as opposed to previous efforts with other techniques that either required specialized high-performance hardware or shared memory environments.
... Experience provides engineering students with a broad interdisciplinary view of the field of engineering that emphasizes the remarkable place in society that engineers hold, fosters the continuous and rigorous development of intellectual skills and judgment, and emphasizes the critical importance of social connectedness and societal obligations. 65 Macalester College (Diane Michelfelder), RIT (Wade Robison): Modules designed for generic introductory design courses (for both engineering and non-engineering students) that incorporate best practices for teaching engineering ethics in solving real-world design problems. 97 MIT (Samuel Bowring, Ari Epstein): Terrascope: A project-based learning community open to all first-year MIT students in which participants address a single complex environmental problem that includes technical components, but that also requires expertise in the traditional liberal arts such as ethics, political science, economics, history, and art. ...
June 2010
... They could even choose elective topics to study. Our previous quantitative research found that students in the IM courses achieved learning gains that were comparable to students who were in collaborative problem solving courses, but IM students expressed a greater sense of ownership and support for their learning [3]. ...
June 2012
... This mode of thinking is fundamentally at odds with the notions of universality and context-free engineering concepts, which philosophers have promoted as the only valid approaches derived from validations through mathematics and modern physics. Additionally, design is inextricably linked to historical, sociocultural, and personal factors [107]. ...
January 2010
... Motivation, understood as cognitive curiosity, affects the willingness to learn. A student with an intrinsic motivation for learning who believes that the task of learning will generate expectations that will lead to a high degree of participation in their learning activities (Stolk & Martello, 2015;Trenshaw et al., 2014). In addition, students who believe that their chances of success in the learning process depend on their efforts and feel capable of completing their learning tasks tend to adopt meaningful learning methods in their learning process (Nelson Laird et al., 2014). ...
August 2014
International Journal of Engineering Education
... In order to minimize faculty time and effort necessary to adopt "intrinsic motivation course conversion, " Herman [78] designed the innovation so that it could take place entirely in teaching assistant-led discussion sections, requiring no changes by the faculty themselves. Sabagh and Saroyan [117] suggest a course release or reduction of service expectations for instructors trying new innovations in their classrooms. ...
Reference:
Propagating Educational Innovations
June 2013
... Goldberg et al. used the ideological and moral quality, intellectual education quality, physical and mental quality, and development ability quality indicators to evaluate and subdivided the indicators into multiple secondary indicators. e evaluation adopts evolutionary algorithm, fuzzy comprehensive evaluation, multivariate statistical analysis method, etc., to obtain effective weights, and the data items are summed according to the weights to obtain the quantitative score evaluation value [16]. Kang et al. selected some data from a large-scale data set, used these data to construct an adjacency matrix, and obtained eigenvectors by eigendecomposition of the adjacency matrix, and finally used Nyström to approximate the eigensolution of the original matrix [17]. ...
December 2014
Journal of Engineering Education
... For the first step, the global extrema are grossly located utilizing maximum inherit optimization (MIO). 45,46 This new optimization algorithm combines fitness inheritance with a genetic algorithm. The fits for the initial population of all the individuals are assessed. ...
July 2008
... In doing so, university educators allow students to continue developing as independent and autonomous learners (Leenknecht et al. 2020). Past research has considered how teachers can affect their students' motivation through course design, specific teaching and learning activities, and through the facilitation of social relationships in courses (Herman et al. 2013;Kember, Ho, and Hong 2010;Leenknecht et al. 2020;Loes 2022). It is plausible to assume that the peculiarities of digital communication also affect the mechanisms of motivation development in a learning context (Ferrer et al. 2022). ...
October 2013
Proceedings - Frontiers in Education Conference
... The convergence analysis of the cGA is established in Reference [9], while an expanded version, known as the extended compact genetic algorithm (ecGA), is detailed in Reference [10]. Research on the scalability of the ecGA is explored in Reference [11], while Reference [12] documents the utilization of the cGA in training neural networks. Additionally, a number of related algorithmic enhancements utilizing the compact strategy have been put forth, such as compact Bat Algorithm(cBA), compact Particle Swarm Optimization (cPSO) [13], compact Sine Cosine Algorithm(cSCA), etc. ...
June 2007
... Their optimization to reproduce computationally demanding quantum-mechanics-based simulation methods is a significantly challenging problem, despite significant advances in finding the best model parameters to reproduce a fit database. For example, algorithms for global minimization techniques (simulated annealing [28,29], genetic optimization [30]), development of flexible models (genetic algorithms [31]), and selection among different models (cross-validation [32]). All of these approaches help ensure that the best model representation is available for a given set of data. ...
June 2007