How do students interpret feedback delivered via dashboards?

Corrin, Linda; Barba, Paula de

doi:10.1145/2723576.2723662

Cited by 75 publications

(81 citation statements)

References 2 publications

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“…More recent research in LA investigates student reactions to presented data visualisations regarding their engagement and how such information is used to inform any change in their approach to study. For example, (Corrin & de Barba, ) found that students were unable to accurately interpret information provided in commonly used dashboards and thus, the effects on their learning were either non‐existent and sometimes even negative. As pointed out by some researchers (Gašević, Dawson, & Siemens, ; Wise, ), additional focus needs to be placed on how to transform the use of data into a positive influence in learning scenarios.…”

Section: Related Researchmentioning

confidence: 99%

Using learning analytics to scale the provision of personalised feedback

Pardo

Jovanović

Dawson

et al. 2017

Brit J Educational Tech

264

140

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There is little debate regarding the importance of student feedback for improving the learning process. However, there remain significant workload barriers for instructors that impede their capacity to provide timely and meaningful feedback. The increasing role technology is playing in the education space may provide novel solutions to this impediment. As students interact with the various learning technologies in their course of study, they create digital traces that can be captured and analysed. These digital traces form the new kind of data that are frequently used in learning analytics to develop actionable recommendations that can support student learning. This paper explores the use of such analytics to address the challenges impeding the capacity of instructors to provide personalised feedback at scale. The case study reported in the paper showed how the approach was associated with a positive impact on student perception of feedback quality and on academic achievement. The study was conducted with first year undergraduate engineering students enrolled in a computer systems course with a blended learning design across three consecutive years (N 2013 5 290, N 2014 5 316 and N 2015 5 415).

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Section: Related Researchmentioning

confidence: 99%

Using learning analytics to scale the provision of personalised feedback

Pardo

Jovanović

Dawson

et al. 2017

Brit J Educational Tech

264

140

View full text Add to dashboard Cite

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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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“…With evidence from learning analytics' studies pointing to the success of personalised dashboards in assisting students to reflect and identify actions needed for behavioural change (Corrin & Barba, 2015), a personalised student dashboard was included to provide a more personalised experience for the user. To enable students to self-manage their actions and succeed, a personalised dashboard with reminder settings and visualised progress of student actions was included in 2017.…”

Section: Further Improvements To a Personalised Experiencementioning

confidence: 99%

“…To enable students to self-manage their actions and succeed, a personalised dashboard with reminder settings and visualised progress of student actions was included in 2017. It is hypothesised that this personalised dashboard will help students realise what future improvements are required and thus make them more likely to act (Corrin & Barba, 2015). This aims to assist students to realise and understand gaps in performance and empower them to implement the action plan that is created.…”

Section: Further Improvements To a Personalised Experiencementioning

confidence: 99%

“…Encouraging students to reflect on and review personal goals, providing them with relevant information upon which to act, can also promote self-management (Robbins, Oh, Le, & Button, 2009). Corrin and Barba (2015) reported that having immediate and personalised feedback assists students to effectively identify their gaps in performance for future improvements. They further recommend that to assist students to translate the feedback into a course of action for behaviour change, support resources and guidance should be provided alongside the immediate feedback.…”

Section: Introductionmentioning

confidence: 99%

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