A. Mehran Shahhosseini

Bio: A. Mehran Shahhosseini is an academic researcher from Indiana State University. The author has contributed to research in topics: Concept map & Modular design. The author has an hindex of 2, co-authored 9 publications receiving 16 citations.

Implementation of Similarity Flooding Algorithm to Solve Engineering Problems Using Diagnostic Skills Training Technique

Abstract: This article focuses on the development and field testing of instructional design concepts and educational software to teach diagnostic skills necessary to identify and solve problems in complex technical systems. Using concept mapping software along with expert-system programs, the overall software package enables technical workers and students in technology and engineering fields to benefit from personalized, iterative interactions that permit them to design visual maps of a diagnostic strategy and to allow direct and automatic comparison of their visual map to an expert’s map.The computer-based modules are developed in the Lectora authoring system and incorporate the VUE concept mapping software. The self-paced, interactive modules include an introduction stage, a visual mapping tutorial stage and a technical system and problem stage. Two technical systems will provide the context for a technical problem, the systems are: 1.) electrical power grid, and 2.) heat exchanger used in a waste plastic pelletizer machine. The educational software can be tailored to include other technical systems and technical problems.This article demonstrates implementation of Similarity Flooding Algorithm (SFA) to solve engineering problems. SFA is used to match nodes and links between learner’s and expert’s concept maps. To compare two process maps, SFA needs to consider both the relations (links) between nodes and the content of the nodes.In this article, we describe four improvements to the original SFA code to improve the comparison between two different concept (or process) maps: 1) Similarity of two strings is calculated by comparing the two strings character by character, which means that two strings get lower similarity if they are not the same exactly. During the comparison, WordNet® thesaurus is used to accurately evaluate the content of nodes; 2) Base similarity includes absolute similarity for each paired nodes, according to their links and content, but the overall similarity of the maps is calculated based on the relative similarity of each pair; 3) In a process map, the importance of each node could be different and important nodes have more weighting in overall similarity; 4) We consider two threshold values in the comparison algorithm. One is similarity threshold for the similarity based on the connections (links) of the nodes; the other is synonym threshold for the semantic similarity of content. If the similarity is lower than the thresholds, it could be considered as discarded. At the end, we analyze the collected data and show the effectiveness of the proposed technique to solve educational and training problems.Copyright © 2014 by ASME

Assessing conceptual mapping based active learning for advancing engineering diagnostic skills

Abstract: Yuetong Lin received the Ph.D. degree in Systems and Industrial Engineering from the University of Arizona, Tucson, in 2005. He was with Indiana State University from 2005 to 2016, where he became associate professor of Electronics and Computer Engineering Technology, and the program coordinator for Computer Engineering Technology. He joined Embry-Riddle Aeronautical University, Worldwide in September 2016 as an associate professor in the Department of Engineering and Technology.

Work-in-Progress: What Recent Artificial Intelligence Breakthroughs in the Game of GO Mean for Human Learning and Engineering Education

Abstract: Artificial intelligence, led by the method of deep learning, has generated enormous interest in both professional circle and general public in the last two years thanks to Deepmind’s AlphaGo’s stunning mastery of Go, the most sophisticated board game. While most interest since then has been shown in exploring the applications of AlphaGo’s algorithms in machine learning, it is the potential impact of its learning strategy on human learning that captures our attention. Can AlphaGo’s success, aside from taking advantage of superior computing power, lead to more effective learning for humans? Does AlphaGo’s learning lend support to any of the learning theories? Or does the training data reveal any notable pattern or trajectory that may suggest new perspectives on human cognition? In this work-in-progress paper, we try to make connection between human and machine learning using the technical details revealed by the Deepmind team, and examine what insights can be gained from AlphaGo’s training on human cognitive development and more specifically, engineering education.

Effects of Joint Preparation Angle of Single-vee Butt Welds on the Tensile Strength of ASTM A36 Carbon Steel

Abstract: Welding is a multi-faceted procedure of manufacturing and can occur at any point during the creation of a product. Quality issues in welding can have disastrous, or even deadly, consequences. The issue of weld preparation angle is one of several different elements that have a direct correlation on the quality of a welded joint. The purpose of this research centers on resultant tensile strength of a single-vee butt joint in carbon steel with various preparation angles. Preparation angles were machined on twenty-six pieces of ASTM A36 carbon steel in 5° increments in order to produce thirteen samples with included preparation angles ranging from 0° to 120°. Test samples were developed using an automated welding process that remained consistent for all of the welds. Each sample was plasma cut into ten coupons, which were machined to have a uniform cross section of the welded joint and surrounding parent material using a computer numerically controlled machining center. This yielded a total population of 130 coupons, which were tested to failure using a United Testing Systems stress/strain tensile tester. The empirical data were analyzed via the use of SPSS 18 statistical software. Initially, the level of population variance was assessed within groups and between groups by use of a one-way ANOVA test at the .05 alpha level. The result showed a statistically significant difference of the sample population. Secondly, a comparison of the data at various preparation angles to that of the industry standard angle of 60° was assessed using a Bonferroni multiple comparisons at the .05 alpha level, which resulted in one angle being statistically significant compared to the industry standard.© 2011 ASME

An introduction to theories of learning.

Abstract: Originally published in Contemporary Psychology: APA Review of Books, 1976, Vol 21(10), 760-761. Reviews the book, An introduction to theories of learning by B. R. Hergenhahn (see record 1976-25089-000). The author's intent was to present theories of learning to students of psychology and education

Measuring the Similarity of Concept Maps According to Pedagogical Criteria

Abstract: A concept map provides a graphic hierarchical means of representing how knowledge is structured in a domain. It visually organizes a set of concepts showing their mutual relations. An analysis of the similarities between two concept maps can produce significant results in any fields where Intelligent Knowledge Management is used, such as Healthcare, Policy Development, Energy and Waste Management, Resource Consumption Sustainability, Mobility, Safety, Citizen Empowerment, and, of course, Education. In an educational setting, a concept map conveys the various concepts connected by relations of dependence that a course must cover. However, the similarity between two concept maps for education has to be measured according to criteria that take into consideration the pedagogical properties of the maps, i.e., not only considering the structural aspects of the maps themselves. An automated analysis of the similarity between two concept maps can allow the teacher to reflect on different interpretations of the knowledge domain of a certain course as well as to assess how existing learning material can be implemented in a new course. Research into this aspect of concept mapping appears to be relatively scarce. This paper proposes criteria to assess the similarity of two concept maps, also based on pedagogical features, with the aim of providing teachers with better support during the course creation process. Each criterion is implemented through a specific measure function. The measures are then shown to be sensitive to their criterion rationale by evaluating them against a collection of random case studies.