Complex Solutions to Complex Problems: The Comparative Effectiveness of Educational Interventions

2001 ◽  
Vol 46 (3) ◽  
pp. 258-260
Author(s):  
Julian G. Elliott
Keyword(s):  
Comparative Effectiveness ◽  
Educational Interventions ◽  
Complex Problems ◽  
Complex Solutions

Complex Solutions for Complex Problems? Exploring the Role of Design Task Choice on Learning, Design for Additive Manufacturing Use, and Creativity

2019 ◽  
Vol 142 (3) ◽  
Author(s):  
Rohan Prabhu ◽  
Scarlett R. Miller ◽  
Timothy W. Simpson ◽  
Nicholas A. Meisel
Keyword(s):  
Additive Manufacturing ◽  
Self Efficacy ◽  
Engineering Students ◽  
Educational Interventions ◽  
Problem Statement ◽  
Design Task ◽  
Complex Solutions ◽  
Future Work ◽  
Am Processes

Abstract The integration of additive manufacturing (AM) processes in many industries has led to the need for AM education and training, particularly on design for AM (DfAM). To meet this growing need, several academic institutions have implemented educational interventions, especially project- and problem-based, for AM education; however, limited research has explored how the choice of the problem statement influences the design outcomes of a task-based AM/DfAM intervention. This research explores this gap in the literature through an experimental study with 175 undergraduate engineering students. Specifically, the study compared the effects of restrictive and dual (restrictive and opportunistic) DfAM education, when introduced through design tasks that differed in the explicit use of design objectives and functional and manufacturing constraints in defining them. The effects of the intervention were measured through (1) changes in participant DfAM self-efficacy, (2) participants' self-reported emphasis on DfAM, and (3) the creativity of participants' design outcomes. The results show that the choice of the design task has a significant effect on the participants' self-efficacy with, and their self-reported emphasis on, certain DfAM concepts. The results also show that the design task containing explicit constraints and objectives results in participants generating ideas with greater uniqueness compared with the design task with fewer explicit constraints and objectives. These findings highlight the importance of the chosen problem statement on the outcomes of a DfAM educational intervention, and future work is also discussed.

Complex Solutions for Complex Problems?: Exploring the Effects of Task Complexity on Student Use of Design for Additive Manufacturing and Creativity

2019 ◽  
Author(s):  
Rohan Prabhu ◽  
Scarlett R. Miller ◽  
Timothy W. Simpson ◽  
Nicholas A. Meisel
Keyword(s):  
Additive Manufacturing ◽  
Self Efficacy ◽  
Engineering Students ◽  
Task Complexity ◽  
Educational Interventions ◽  
Problem Statement ◽  
Design Task ◽  
Complex Design ◽  
Complex Solutions ◽  
Future Work

Abstract The integration of additive manufacturing (AM) processes in many industries has led to the need for AM education and training, particularly on design for AM (DfAM). To meet this growing need, several academic institutions have implemented educational interventions, especially project- and problem-based, for AM education; however, limited research has explored how the choice of the problem statement influences the design outcomes of a task-based AM/DfAM intervention. This research explores this gap in the literature through an experimental study with 222 undergraduate engineering students. Specifically, the study compared the effects of restrictive and dual (restrictive and opportunistic) DfAM education, when introduced through either a simple or complex design task. The effects of the intervention were measured through (1) changes in student DfAM self-efficacy, (2) student self-reported emphasis on DfAM, and (3) the creativity of student AM designs. The results show that the complexity of the design task has a significant effect on the participants’ self-efficacy with, and self-reported emphasis on, certain DfAM concepts. The results also show that the complex design task results in participants generating ideas with greater median uniqueness compared to the simple design task. These findings highlight the importance of the chosen problem statement on the outcomes of a DfAM educational intervention, and future work is also discussed.

scholarly journals Competitive Coevolution through Evolutionary Complexification

2004 ◽  
Vol 21 ◽  
pp. 63-100 ◽  
Author(s):  
K. O. Stanley ◽  
R. Miikkulainen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Arms Race ◽  
Network Architectures ◽  
Complex Problems ◽  
Wide Range ◽  
Competitive Coevolution ◽  
Neural Network Architectures ◽  
Complex Solutions ◽  
Fixed Structure

Two major goals in machine learning are the discovery and improvement of solutions to complex problems. In this paper, we argue that complexification, i.e. the incremental elaboration of solutions through adding new structure, achieves both these goals. We demonstrate the power of complexification through the NeuroEvolution of Augmenting Topologies (NEAT) method, which evolves increasingly complex neural network architectures. NEAT is applied to an open-ended coevolutionary robot duel domain where robot controllers compete head to head. Because the robot duel domain supports a wide range of strategies, and because coevolution benefits from an escalating arms race, it serves as a suitable testbed for studying complexification. When compared to the evolution of networks with fixed structure, complexifying evolution discovers significantly more sophisticated strategies. The results suggest that in order to discover and improve complex solutions, evolution, and search in general, should be allowed to complexify as well as optimize.

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