{"title":"A Method to Semi-Automatically Identify and Measure Unmet Requirements in Learner-Created State Machine Diagrams","authors":"Takuma Kimura, Shinpei Ogata, Erina Makihara, Kozo Okano","doi":"10.1109/CSEET58097.2023.00011","DOIUrl":null,"url":null,"abstract":"The UML (Unified Modeling Language) state machine diagram notation is challenging for learners to understand because of its complexity. Therefore, educators assign modeling assignments to learners to assess their understanding. If learners do not fully understand the notation, they may make errors in their diagrams. To improve learners’ understanding, educators provide the learners with explanations of what and why the diagrams unmet the requirements of the modeling assignments. However, the variety of content and layout in Learner-created diagrams can be challenging for educators to accurately and quickly identify the unmet requirements in each diagram. Therefore, this study proposes a method to semi-automatically identify and measure unmet requirements in Learner-created diagrams. The proposed method was applied to 38 state machine diagrams created by learners to evaluate its effectiveness. Consequently, the proposed method gave reasonable results for 37 out of 38 diagrams (approximately 97%).","PeriodicalId":256885,"journal":{"name":"2023 IEEE 35th International Conference on Software Engineering Education and Training (CSEE&T)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE 35th International Conference on Software Engineering Education and Training (CSEE&T)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSEET58097.2023.00011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The UML (Unified Modeling Language) state machine diagram notation is challenging for learners to understand because of its complexity. Therefore, educators assign modeling assignments to learners to assess their understanding. If learners do not fully understand the notation, they may make errors in their diagrams. To improve learners’ understanding, educators provide the learners with explanations of what and why the diagrams unmet the requirements of the modeling assignments. However, the variety of content and layout in Learner-created diagrams can be challenging for educators to accurately and quickly identify the unmet requirements in each diagram. Therefore, this study proposes a method to semi-automatically identify and measure unmet requirements in Learner-created diagrams. The proposed method was applied to 38 state machine diagrams created by learners to evaluate its effectiveness. Consequently, the proposed method gave reasonable results for 37 out of 38 diagrams (approximately 97%).