{"title":"Towards Optimization of Learning Analytics Dashboards That are Customized for the Students’ Requirements","authors":"Rotem Israel-Fishelson;Dan Kohen-Vacs","doi":"10.1109/TLT.2023.3332500","DOIUrl":null,"url":null,"abstract":"Educational dashboards enable students to monitor and reflect on academic performance and administrative aspects of the learning processes. Occasionally, educational institutions integrate dashboards using the information found in their learning management systems or their students' information desks. Learning analytics offers ways to enrich these dashboards and expose students to analyzed information beyond the monitored data provided such as smart recommendations. Despite the large variety of dashboards, the students’ centric perspective and the ability to adapt the dashboard to their personal needs is not a common practice. To identify and support the needs of students who wish to track aspects of their learning routine, it is very important to position the students at the core of the design process of these dashboards. This article presents a new phase in our research to expand our understanding of the students' needs in monitoring their educational routines and preferences while using an advanced form of a learning analytics dashboard. We propose an optimized approach for designing educational dashboards. In this sense, we examine and seek to integrate the components that are prominently required by students. Hence, we address both the type of components as well as their arrangement within the customized dashboard. The outcomes of our efforts reveal findings concerning students’ trends and habits when exploiting these dashboards. It also offers pivotal insights and recommendations for the optimized implementation of learning analytics dashboards that are aligned with the students’ authentic requirements.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"794-802"},"PeriodicalIF":2.9000,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Learning Technologies","FirstCategoryId":"95","ListUrlMain":"https://ieeexplore.ieee.org/document/10319391/","RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Educational dashboards enable students to monitor and reflect on academic performance and administrative aspects of the learning processes. Occasionally, educational institutions integrate dashboards using the information found in their learning management systems or their students' information desks. Learning analytics offers ways to enrich these dashboards and expose students to analyzed information beyond the monitored data provided such as smart recommendations. Despite the large variety of dashboards, the students’ centric perspective and the ability to adapt the dashboard to their personal needs is not a common practice. To identify and support the needs of students who wish to track aspects of their learning routine, it is very important to position the students at the core of the design process of these dashboards. This article presents a new phase in our research to expand our understanding of the students' needs in monitoring their educational routines and preferences while using an advanced form of a learning analytics dashboard. We propose an optimized approach for designing educational dashboards. In this sense, we examine and seek to integrate the components that are prominently required by students. Hence, we address both the type of components as well as their arrangement within the customized dashboard. The outcomes of our efforts reveal findings concerning students’ trends and habits when exploiting these dashboards. It also offers pivotal insights and recommendations for the optimized implementation of learning analytics dashboards that are aligned with the students’ authentic requirements.
期刊介绍:
The IEEE Transactions on Learning Technologies covers all advances in learning technologies and their applications, including but not limited to the following topics: innovative online learning systems; intelligent tutors; educational games; simulation systems for education and training; collaborative learning tools; learning with mobile devices; wearable devices and interfaces for learning; personalized and adaptive learning systems; tools for formative and summative assessment; tools for learning analytics and educational data mining; ontologies for learning systems; standards and web services that support learning; authoring tools for learning materials; computer support for peer tutoring; learning via computer-mediated inquiry, field, and lab work; social learning techniques; social networks and infrastructures for learning and knowledge sharing; and creation and management of learning objects.