Learning Analytics: at the Nexus of Big Data, Digital Innovation, and Social Justice in Education

Aguilar, Stephen J.

doi:10.1007/s11528-017-0226-9

Cited by 59 publications

(58 citation statements)

References 70 publications

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“…On the application side of research in learning and navigation there has been an increase in interest in learning analytics. In education, Aguilar (2018) writes that learning analytics has emerged as the discipline associated with analyzing and reporting big data. Stakeholders in education utilize learning analytics as a way to make learning and learners' navigation more personalized.…”

Section: Learning and Navigationmentioning

confidence: 99%

“…Stakeholders in education utilize learning analytics as a way to make learning and learners' navigation more personalized. Aguilar (2018) points out that most learning and navigational infrastructure is primarily built to serve the "average" student, e.g., students are grouped into classrooms, grades, and schools and teachers provide instruction one class at a time. More personalized instruction has been shown to be impactful, but can be difficult to scale.…”

Section: Learning and Navigationmentioning

confidence: 99%

“…More personalized instruction has been shown to be impactful, but can be difficult to scale. Aguilar (2018) argues that learning analytics provides a solution to this problem by using computational methodology and visualizations to allow personalized learning and navigation at scale. Methods for learning analytics include:…”

Section: Learning and Navigationmentioning

confidence: 99%

“…This allows researchers and instructors to observe the impact of the content, resources, and interventions on student learning. However, Aguilar (2018) points out learning analytics is not a silver bullet for the problem of providing personalized learning. The data that are collected and the procedures for processing and creating visualizations needs to be thought through ahead of time.…”

Section: Learning and Navigationmentioning

confidence: 99%

“…For example, Teasley (2017) points out that providing learning analytics dashboards directly to students has not been fruitful and requires further research. Finally, Aguilar (2018) points out potential data privacy concerns, which remain to be comprehensively addressed.…”

Section: Learning and Navigationmentioning

confidence: 99%

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Computational Psychometrics Approach to Holistic Learning and Assessment Systems

et al. 2019

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Learning and assessment systems have grown and taken shape to incorporate concepts from both models for assessment and models for learning. In this paper we argue that a third dimension is necessary. Not only is it important to understand what the capabilities of a learner are, and how to grow and expand these capabilities, but we must consider where the learner is headed; we need to consider models for navigation. This holistic perspective of learning and assessment systems is encapsulated in the extended learning and assessment system, a framework for conducting research. Fundamental to this framework is the role of computational psychometrics to facilitate the abstraction from raw data to conceptual models. We provide several examples of research projects and describe how they fit into the described framework.

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Section: Learning and Navigationmentioning

confidence: 99%

Section: Learning and Navigationmentioning

confidence: 99%

Section: Learning and Navigationmentioning

confidence: 99%

Section: Learning and Navigationmentioning

confidence: 99%

Section: Learning and Navigationmentioning

confidence: 99%

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