Investigation of Assessment Methods for Measuring the Effectiveness of Student Design Learning

Deep learning approach in educational context is focused on analyzing ideas, and creating a strong connection between the ideas and prior knowledge. A Problem-Based Learning (PBL), considered as students' deep learning approach, is a widely-adopted educational strategy designed to teach students to use their engineering knowledge to solve the real-life engineering problems. The goal of this thesis is to investigate of assessment methods for measuring the effectiveness of students' learning under a flying house design session which is a PBL teaching method. To do so, the Environment Based Design (EBD) approach is used to determine assessment criteria. Two assessment methods (i.e., Study Process Questionnaire (SPQ), Logos Comparison Task (LCT)) have been applied to two groups of students with and without EBD knowledge. Through the investigation and analysis, the results indicate a significant effect of EBD knowledge and skills on the LCT grades. However, no major effect of students' learning approach (SLA) and interaction between EBD and SLA has been detected. Similarly, no linear relationship between students' deep learning approach and higher LCT grades has been found

To investigate power of brain activity using EEG comparison between creative and non-creative design task

In recent times, neurophysiological measurement methods such as EEG and fMRI are widely used in an Engineering field to study designer’s brain activity during creative thinking. In literature, many researchers reported the synchronization and desynchronization of EEG activity in specific brain cortex during creative thinking. However, we do not find many studies associated to comparison of designer’s brain activity during creativity/non-creativity related task demands. The chief objective of present thesis is to investigate the power of brain activity using EEG comparison between creative and non-creative design task. For psychometric measures of creative thinking, Torrance Test of Creative Thinking (TTCT) (Torrance, 1966) is widely used. In present thesis, we use modified TTCT according to our experiment requirement. The test was decomposed between creative and non-creative design task. In creative design task, designers were instructed to think creatively whereas in non-creative design task they were required to think intuitively. When designers were performing these design tasks, their EEG recordings were obtained to investigate brain activity during design tasks. The EEG powers are calculated through spectral analysis. We aggregate electrode positions to identify distribution of EEG powers among brain regions during cognitive task performance. In order to compare EEG between creative and non-creative design task using cortical area to area approach, we perform repeated measure ANOVA for within-subject factors such as design task and brain areas. However, we found non-significant interaction effect between creative/non-creative design task and cortical areas

A Systematic Study of Design Conflict: Modeling, Representation, Resolution, and Application

ABSTRACT A Systematic Study of Design Conflict: Modeling, Representation, Resolution, and Application Baiquan Yan, Ph.D. Concordia University, 2013 Conflicts drive the development of technical systems and the evolution of design process. Conflict management, which mainly includes conflict identification and resolution, is a crucial part of design activity. This research conducts a systematic study and proposes a formal structure of design conflicts. The first step of conflict management is to build up a formal model for technical system. Currently, there exist some inconsistences among different design theories because of the lack of a cohesive set of fundamental concepts about technical systems. This lack also causes misunderstanding among researchers and therefore hinders the development of design theories. This thesis presents a formal approach to representing technical systems. Both theoretical derivation and extensive example have shown that this formal representation meets the five requirements: completeness, clarity, independence, flexibility, and adaptability. A set five concepts— purpose, function, structure, behaviour and state— is identified and formally defined as the base set for technical systems. The second step is to model conflicts based on the formalization of technical system. Current studies are based on heuristics and lack a systematic approach, and therefore fail to detect conflicts that are not predefined. This research puts forward a formal structure of design conflicts based on systematic analysis. This formal structure shows that any conflict is composed of at least three objects: two competing objects and one resource object that the former two contend for. This formal structure can be applied to different design fields and helps designers identify all conflicts existed in different design stages. Based on the formal structure of conflicts and analysis of relation among the three objects in a conflict, this research also proposes three formal methods for detecting conflicts and presents a set of general resolution principles, which include modifying resource object, separating conflict relations in time or in space, changing the two competing objects, using optimization methods, and replacing the whole conflict. An example demonstrates the application of the formal structures, followed with conclusion and suggestions for future research

Rule-based Machine Learning Algorithms for Smart Automatic Quadrilateral Mesh Generation System

Mesh generation, as one of six basic research directions identified in NASA Vision 2030, is an important area in computational geometry and plays a fundamental role in numerical simulations in the area of finite element analysis (FEA) and computational fluid dynamics (CFD). With the rapid progress of high-performance computing hardware, mesh generation methods are required to handle geometric domains with more complex shapes and higher resolution in reliable and fast fashions. Yet, existing mesh generation methods suffer from high computational complexity, low mesh quality in complex geometries, and speed limitations, and have continued to be the bottleneck in those simulation tasks. This thesis addresses the quadrilateral mesh generation problem from three aspects, element extraction, sequential decision making, and data generation, and their combinations. First, a self-learning system, FreeMesh-S, for finite element extraction system is investigated. Element extraction is a major mesh generation method for its capabilities to generate high-quality meshes around the domain boundary and can be formulated into a sequential decision making process. Three kinds of primitive element extraction rules are conceptually identified. FreeMesh-S, then learns the rules by 1) sampling the element generation rules by a reinforcement learning (RL) algorithm, 2) extracting high quality samples, and 3) training the final rules by a feedforward neural network (FNN). The comprehensive experiments demonstrate the effectiveness of the self-learned meshing rules by FreeMesh-S. Second, an RL-based computational framework for automatic mesh generation is proposed to improve algorithm automation further. A state-of-the-art RL algorithm, soft actor-critic (SAC), is used to learn the mesh generator's policy from trials. It achieves a fully automatic mesh generation without human intervention and any extra clean-up operations, which are typically needed in current commercial software. The reward function is carefully designed to balance the contradiction between the instant element quality and the remaining boundary quality, in order to achieve an overall high quality mesh. The experiments have shown the competitive performance with two representative meshing methods with respect to generalizability, robustness, and effectiveness. The potentials of mesh generation as a benchmark problem for RL are also identified. Last, a quality function-based data generation method for the meshing algorithm is devised to increase learning efficiency and algorithm performance. For any data-driven algorithms, high quality and balanced data are essential and deterministic to the performance. This method samples the input-output of the three rules according to their feature spaces; selects high quality samples by a quality function that evaluates if the output is an appropriate solution to the input; and trains an FNN model to simulate the mapping relation via the obtained data. The experiments show that the learning time is greatly reduced while the model has competitive performance comparing with other meshing methods. To conclude, this thesis combines artificial intelligence techniques, rule-based system, neural networks, and RL, to automate the quadrilateral mesh generation while significantly reducing the time and expertise needed during the creation of high quality mesh generation algorithm. All the techniques can be directly generalized to 3D mesh generation