Learning dashboards: an overview and future research opportunities

Verbert, Katrien; Govaerts, Sten; Duval, Erik; Santos, José Luís; Assche, Frans Van; Parra, Gonzalo; Klerkx, Joris

doi:10.1007/s00779-013-0751-2

Cited by 257 publications

(290 citation statements)

References 24 publications

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“…For example, data visualizations are often used as artifacts to either help teachers gain insight about how a learning environment unfolds (Verpoorten et al 2011;Verbert et al 2014), but can also be offered directly to students to help them reflect on their approach to learning (Kahn and Pardo 2016; Corrin and de Barba 2015). Also, some authors have identified the need to consider LA techniques during the learning design stages and propose how to integrate the data collection, analysis, reporting, and interventions in a unified workflow (Lockyer et al 2013;Bakharia et al 2016).…”

Section: The State Of Data-driven Student Supportmentioning

confidence: 99%

Data-Driven Personalization of Student Learning Support in Higher Education

Liu

Bartimote-Aufflick

Pardo

et al. 2017

Studies in Systems, Decision and Control

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Abstract. Despite the explosion of interest in big data in higher education and the ensuing rush for catch-all predictive algorithms, there has been relatively little focus on the pedagogical and pastoral contexts of learning. The provision of personalized feedback and support to students is often generalized and decontextualized, and examples of systems that enable contextualized support are notably absent from the learning analytics landscape. In this chapter we discuss the design and deployment of the Student Relationship Engagement System (SRES), a learning analytics system that is grounded primarily within the unique contexts of individual courses. The SRES, currently in use by teachers from 19 departments, takes a holistic and more human-centric view of data -one that puts the relationship between teacher and student at the center. Our approach means that teachers' pedagogical expertise in recognizing meaningful data, identifying subgroups of students for a range of support actions, and designing and deploying these actions, is facilitated by a customizable technology platform. We describe a case study of the application of this human-centric approach to learning analytics, including its impacts on improving student engagement and outcomes, and debate the cultural, pedagogical, and technical aspects of learning analytics implementation.Keywords: actionable intelligence, implementation, intelligence amplification, learning analytics, personalization, student engagement. Abbreviations EDMEducational data mining EWS Early warning system LA Learning analytics LMS Learning management system SRES Student Relationship Engagement System 2 Introduction The state of data-driven student supportThe rise in use of technology mediation in learning scenarios is providing unprecedented amounts of data about how educational institutions work and how students participate in learning experiences. At the same time, learning scenarios are becoming increasingly diverse and complex. The areas of educational data mining (EDM) and learning analytics (LA) have emerged to address the issue of how to use data to improve our understanding of learning, and enhance the overall quality of the learning experience for the student. Although EDM and LA researchers and practitioners maintain a similar focus (Baker and Siemens 2014), they differ in their approach to data generated in educational settings. Researchers in EDM frequently focus their analyses on the formulation or improvement of data mining algorithms designed to detect and predict important factors in a learning scenario. LA, on the other hand, focuses on how these algorithms can be deployed and integrated in learning designs, used by teachers, and provide tangible improvements for students. However, in their initial stages, both disciplines placed their emphasis mostly on how data can be collected and used by algorithms and not so much on how these data can then lead to actions that have a positive effect on students.Prior to the availability of massive amounts of data, the area...

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Section: The State Of Data-driven Student Supportmentioning

confidence: 99%

Data-Driven Personalization of Student Learning Support in Higher Education

Liu

Bartimote-Aufflick

Pardo

et al. 2017

Studies in Systems, Decision and Control

View full text Add to dashboard Cite

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“…Verbert et al [42] introduced a conceptual framework for analysing LA applications and reviewed 15 dashboards based on the target users, displayed data and the focus of the evaluation. A follow-up review [43] extended this analysis to 24 dashboards, examining the context in which the dashboards had been deployed, the data sources, the devices used and the evaluation methodology. Yoo et al [47] reviewed the design and evaluation of 10 educational dashboards for teachers and students through their proposed evaluative tool based on Few's principles of dashboard design [15] and Kirkpatrick's four-level evaluation model [25].…”

Section: Introductionmentioning

confidence: 99%

Awareness Is Not Enough: Pitfalls of Learning Analytics Dashboards in the Educational Practice

Jivet

Scheffel

Drachsler

et al. 2017

Lecture Notes in Computer Science

205

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Abstract. It has been long argued that learning analytics has the potential to act as a "middle space" between the learning sciences and data analytics, creating technical possibilities for exploring the vast amount of data generated in online learning environments. One common learning analytics intervention is the learning dashboard, a support tool for teachers and learners alike that allows them to gain insight into the learning process. Although several related works have scrutinised the state-of-the-art in the field of learning dashboards, none have addressed the theoretical foundation that should inform the design of such interventions. In this systematic literature review, we analyse the extent to which theories and models from learning sciences have been integrated into the development of learning dashboards aimed at learners. Our critical examination reveals the most common educational concepts and the context in which they have been applied. We find evidence that current designs foster competition between learners rather than knowledge mastery, offering misguided frames of reference for comparison.

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“…Although sometimes teachers are asked about their opinion of the usefulness of LA, it is suggested that other evaluation techniques such as user data analysis could provide better insight in the way LA may support teachers (Verbert et al, 2014).…”

Section: Learning Analytics As Teacher Supportmentioning

confidence: 99%

Measuring teacher sense making strategies of learning analytics: a case study

Leeuwen

Wermeskerken

Erkens

et al. 2017

Learning: Research and Practice

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Teacher orchestration of the classroom is a demanding task because of the multitude of activities and the rapid pace at which these activities occur. Teachers must constantly be aware of the activities students engage in and the progress students are making, in order to be able to make informed decisions. Recent years have seen the development of learning analytics (LA) to support teachers, in the form of interfaces that capture and visualise data about learner activities. LA are hypothesised to deliver actionable knowledge, because the provided information would enable teachers to immediately translate the information into concrete intervention of some kind. This paper provides a short discussion of the hypothesised affordances of LA for supporting teachers, and subsequently, a case study is presented in which a teacher's sense making strategies of LA were investigated within a digital environment, which enabled the teacher to monitor and regulate the activities of five collaborating groups of students. Besides automatically logged teacher behaviour, eye tracking was used to examine eye movements, and cued retrospective reporting was used to evaluate the choices and decisions made by the teacher. The case study is used to discuss directions for future research concerning learning analytics to support teachers. ARTICLE HISTORY

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Learning dashboards: an overview and future research opportunities

Cited by 257 publications

References 24 publications

Data-Driven Personalization of Student Learning Support in Higher Education

Data-Driven Personalization of Student Learning Support in Higher Education

Awareness Is Not Enough: Pitfalls of Learning Analytics Dashboards in the Educational Practice

Measuring teacher sense making strategies of learning analytics: a case study

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