{"title":"Moodle analytics dashboard: A learning analytics tool to visualize users interactions in moodle","authors":"Luan Einhardt, T. Tavares, C. Cechinel","doi":"10.1109/LACLO.2016.7751805","DOIUrl":null,"url":null,"abstract":"The present work describes the Moodle Analytics Dashboard (MAD), a tool developed to allow the visualization of students and professors logs in Moodle disciplines. MAD provides an easy way to obtain graphical visualization of several aspects related to students and professors accesses in virtual learning disciplines, thus helping professors to better follow teaching and learning process, as well as to visually identify potential at-risk students, or to better understand how the different educational resources are being used. The paper presents the theoretical foundations and the technologies used to develop MAD, together with the most important features of the tool and the first results obtained during the preliminary stages of validation.","PeriodicalId":271823,"journal":{"name":"2016 XI Latin American Conference on Learning Objects and Technology (LACLO)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 XI Latin American Conference on Learning Objects and Technology (LACLO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/LACLO.2016.7751805","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 20
Abstract
The present work describes the Moodle Analytics Dashboard (MAD), a tool developed to allow the visualization of students and professors logs in Moodle disciplines. MAD provides an easy way to obtain graphical visualization of several aspects related to students and professors accesses in virtual learning disciplines, thus helping professors to better follow teaching and learning process, as well as to visually identify potential at-risk students, or to better understand how the different educational resources are being used. The paper presents the theoretical foundations and the technologies used to develop MAD, together with the most important features of the tool and the first results obtained during the preliminary stages of validation.
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