Phyllalintang Nafasa, I. Waspada, N. Bahtiar, A. Wibowo
{"title":"基于MOODLE事件日志数据的在线学习活动过程挖掘应用开发中Alpha Miner算法的实现","authors":"Phyllalintang Nafasa, I. Waspada, N. Bahtiar, A. Wibowo","doi":"10.1109/ICICoS48119.2019.8982384","DOIUrl":null,"url":null,"abstract":"Moodle is one of the widely used Learning Management Systems in the field of education. Moodle stores all online learning activities to the database in the form of event log. These event logs can be used to improve the quality of learning through process analysis. One of the fields of science that can be used to discover the process model based on event log is Process Mining. The problem arise when an instructor willing to use the Moodle event log data to do a Process Mining activities. There are some preprocessing issues need to be done to the Moodle event log data as prerequisite to continue with Process Mining algorithm. As the solution, Moodle need to be integrated with the Process Mining. In this study an application was developed to integrate the Moodle event log data with the activities of Process Mining, especially to facilitate the preprocessing tools. The alpha miner algorithm was used here as the process model discovery algorithm. As the result, we successfully develop the application to discover process model from Moodle log event data. Instructors can use some functional features of the application to meet their need in process mining analysis. Experiments using real and artificial case studies have been conducted and it is proven that the implementation of the alpha miner algorithm can work correctly on the Moodle event log data.","PeriodicalId":105407,"journal":{"name":"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"163 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Implementation of Alpha Miner Algorithm in Process Mining Application Development for Online Learning Activities Based on MOODLE Event Log Data\",\"authors\":\"Phyllalintang Nafasa, I. Waspada, N. Bahtiar, A. Wibowo\",\"doi\":\"10.1109/ICICoS48119.2019.8982384\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Moodle is one of the widely used Learning Management Systems in the field of education. Moodle stores all online learning activities to the database in the form of event log. These event logs can be used to improve the quality of learning through process analysis. One of the fields of science that can be used to discover the process model based on event log is Process Mining. The problem arise when an instructor willing to use the Moodle event log data to do a Process Mining activities. There are some preprocessing issues need to be done to the Moodle event log data as prerequisite to continue with Process Mining algorithm. As the solution, Moodle need to be integrated with the Process Mining. In this study an application was developed to integrate the Moodle event log data with the activities of Process Mining, especially to facilitate the preprocessing tools. The alpha miner algorithm was used here as the process model discovery algorithm. As the result, we successfully develop the application to discover process model from Moodle log event data. Instructors can use some functional features of the application to meet their need in process mining analysis. Experiments using real and artificial case studies have been conducted and it is proven that the implementation of the alpha miner algorithm can work correctly on the Moodle event log data.\",\"PeriodicalId\":105407,\"journal\":{\"name\":\"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)\",\"volume\":\"163 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICoS48119.2019.8982384\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICoS48119.2019.8982384","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Implementation of Alpha Miner Algorithm in Process Mining Application Development for Online Learning Activities Based on MOODLE Event Log Data
Moodle is one of the widely used Learning Management Systems in the field of education. Moodle stores all online learning activities to the database in the form of event log. These event logs can be used to improve the quality of learning through process analysis. One of the fields of science that can be used to discover the process model based on event log is Process Mining. The problem arise when an instructor willing to use the Moodle event log data to do a Process Mining activities. There are some preprocessing issues need to be done to the Moodle event log data as prerequisite to continue with Process Mining algorithm. As the solution, Moodle need to be integrated with the Process Mining. In this study an application was developed to integrate the Moodle event log data with the activities of Process Mining, especially to facilitate the preprocessing tools. The alpha miner algorithm was used here as the process model discovery algorithm. As the result, we successfully develop the application to discover process model from Moodle log event data. Instructors can use some functional features of the application to meet their need in process mining analysis. Experiments using real and artificial case studies have been conducted and it is proven that the implementation of the alpha miner algorithm can work correctly on the Moodle event log data.