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.

Assessing conceptual mapping based active learning for advancing engineering diagnostic skills

“…Our method of comparing concept maps is based on combining the weighting mechanism, SFA, and semantic similarity of two strings. The detail introduction of the method is included in the paper by Shahhosseini et al 29 .…”

Application of a Computerized Method for Evaluating Systems-Level Thinking

“…Prior work by Shahhosseini served as a foundation for machine-based diagram evaluation [43][44][45][46]. Software was developed to compare two node-and-link diagrams and produce an overall similarity rating.…”

Board 58: Concept Map-based Aviation Competency Mapping and Training

“…Our method of comparing concept maps is based on combining the weighting mechanism, SFA, and semantic similarity of two strings. The detail introduction of the method is included in the paper by Shahhosseini et al 14 and shown in Figure 2. In Figure 2, we adopt SFA program (Open source Java code developed by Melnik et al) to match the nodes based on their relationships.…”