User-Centered Design for a Student-Facing Dashboard Grounded in Learning Theory

Teasley, Stephanie D.; Kay, Matthew; Elkins, Shannon; Hammond, J. W.

doi:10.1007/978-3-030-81222-5_9

Cited by 8 publications

(3 citation statements)

References 60 publications

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“…Hence, the acceptance and effectiveness of dashboards highly depend on the benefits learners may expect, including a clear, simple and fit-for-purpose design (Pokhrel and Awasthi, 2021). However, dashboard designs are expected to be unique regarding the design of the visualisation and are also expected to fit the current needs of learners when they are used (Teasley et al , 2021). While the currently implemented dashboard is being further developed, the learners expectations are being evaluated in a participatory design approach (Könings et al , 2014).…”

Section: Discussionmentioning

confidence: 99%

Empowering higher education students to monitor their learning progress: opportunities of computerised classification testing

Şahi̇n

2023

ITSE

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Purpose This study aims to focus on providing a computerized classification testing (CCT) system that can easily be embedded as a self-assessment feature into the existing legacy environment of a higher education institution, empowering students with self-assessments to monitor their learning progress and following strict data protection regulations. The purpose of this study is to investigate the use of two different versions (without dashboard vs with dashboard) of the CCT system during the course of a semester; to examine changes in the intended use and perceived usefulness of two different versions (without dashboard vs with dashboard) of the CCT system; and to compare the self-reported confidence levels of two different versions (without dashboard vs with dashboard) of the CCT system. Design/methodology/approach A total of N = 194 students from a higher education institution in the area of economic and business education participated in the study. The participants were provided access to the CCT system as an opportunity to self-assess their domain knowledge in five areas throughout the semester. An algorithm was implemented to classify learners into master and nonmaster. A total of nine metrics were implemented for classifying the performance of learners. Instruments for collecting co-variates included the study interest questionnaire (Cronbach’s a = 0. 90), the achievement motivation inventory (Cronbach’s a = 0. 94), measures focusing on perceived usefulness and demographic data. Findings The findings indicate that the students used the CCT system intensively throughout the semester. Students in a cohort with a dashboard available interacted more with the CCT system than students in a cohort without a dashboard. Further, findings showed that students with a dashboard available reported significantly higher confidence levels in the CCT system than participants without a dashboard. Originality/value The design of digitally supported learning environments requires valid formative (self-)assessment data to better support the current needs of the learner. While the findings of the current study are limited concerning one study cohort and a limited number of self-assessment areas, the CCT system is being further developed for seamless integration of self-assessment and related feedback to further reveal unforeseen opportunities for future student cohorts.

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Section: Discussionmentioning

confidence: 99%

Empowering higher education students to monitor their learning progress: opportunities of computerised classification testing

Şahi̇n

2023

ITSE

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“…The forecast is based on course grades, time spent by a student on a certain task and past overall performance. Another example is the My Learning Analytics dashboard [12], which is aimed at helping students to plan their learning throughout a course and understand their course performance with respect to that of their classmates.…”

Section: An Overview Of Existing Learning Analytics Tools and Practicesmentioning

confidence: 99%

Designing an Education Database in a Higher Education Institution for the Data-Driven Management of the Educational Process

Kustitskaya,

Esin,

Kytmanov

et al. 2023

Education Sciences

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During the past two decades, higher education institutions have been experiencing challenges in transforming the traditional way of in-class teaching into blended learning formats with the support of e-learning technologies that make possible the collection and storing of considerable amounts of data on students. These data have considerable potential to bring digital technologies in education to a new level of personalized learning and data-driven management of the educational process. However, the way data are collected and stored in a typical university makes it difficult to achieve the mentioned goals, with limited examples of data being used for the purposes of learning analytics. In this work, based on the analysis of existing information systems and databases at Siberian Federal University, we propose principles of design for a university database architecture that allow for the development and implementation of a data-driven management approach. We consider various levels of detail of education data, describe the database organization and structure, and provide examples of learning analytics tools that can benefit from the proposed approach. Furthermore, we discuss various aspects of its implementation and associated questions.

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“…Existing research focuses on the success and engagement of SFDs (Pokhrel, J., et al, 2021)), the frameworks SFDs can be based on (Teasley 2021) and which components might best be displayed (De Barba et al, 2022). Google Scholar searches yielded a limited amount of papers ) highlighting the implementation and software of a dashboard including Quincey, E., et al, (2019).…”

Section: Introductionmentioning

confidence: 99%

The missing link in Learning Analytics

White,

Dave,

Huijser

2023

APUBS

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Learning Analytics and staff-facing dashboards enable educators to gain insights into student learning, enhancing teaching practices and learning outcomes. Student-facing dashboards (SFDs) can also prove advantageous for students as they empower them to better interpret their results, promote self-reflection with the goal of improving their academic performance. Whilst there is no currently preferred software solution for implementing dashboards. Through the authors’ reflections of creating SFDs, using assessment and ePortfolio asset data, this can be achieved with free or readily available tools. This concise paper used initiatives for the author to reflect upon the process creating SFDs. Investigations for opportunities to refine dashboards are also presented.

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User-Centered Design for a Student-Facing Dashboard Grounded in Learning Theory

Cited by 8 publications

References 60 publications

Empowering higher education students to monitor their learning progress: opportunities of computerised classification testing

Empowering higher education students to monitor their learning progress: opportunities of computerised classification testing

Designing an Education Database in a Higher Education Institution for the Data-Driven Management of the Educational Process

The missing link in Learning Analytics

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