Education Learning Analytics in Higher Education
Jennifer Stokes, Anthea Fudge
  • LAST MODIFIED: 21 February 2023
  • DOI: 10.1093/obo/9780199756810-0297


In the 2010s, growth of information and communication technologies and the emergence of big data led to the possibility of meaningful analysis of data at scale. University student interactions were channeled through learning management systems (LMS) or virtual learning environments (VLEs), so researchers were able to collect clickstream data and observe patterns of use which were previously invisible or non-existent. Leading thinkers saw the potential for learning analytics and led the development of this distinct field, separating away from other data-informed approaches, such as electronic data mining and academic analytics. The field was strengthened through the development of the Society of Learning Analytics Research (SoLAR). SoLAR publish the Journal of Learning Analytics and established leading conferences to build a worldwide network of disciplinary expertise. Thought leaders from Australia, Canada, Europe, the United Kingdom, and the United States led the global movement to implement learning analytics. Initially, learning analytics focused on the potential for using data-driven decision-making to inform actionable insights or interventions, which could improve student learning outcomes. As data reveals information not previously accessible, ensuring ethical approaches and respecting student privacy have been consistent themes. Data collection has grown to be multimodal in nature and analytic approaches have continued to develop, expanding to include social and networked analyses, cluster analyses, and others. Attention was directed to the way students and instructors visualize and communicate findings from the wealth of data. There is a continued focus on sense-making via visual displays to ensure information is effectively interpreted and understood. Tools and applications for implementing interventions were often initially tested in siloed or individual courses. The field is now expanding to bring insights and positive findings from initial learner support to inform a broader understanding of how and in what way these tools specifically support learners through this complex, situational, and social process across institutions worldwide. Researchers argue for greater pedagogical and theoretical links to ensure scalability and support for learners and educators alike. The most effective use of these technologies combines established learning theories and learning design with analytics to generate useful and actionable insights. The ideal is to support student success through personalized learning. However, the significant potential for improving student learning outcomes can only be achieved through broad stakeholder engagement. To support widespread adoption by educators and implementation at an institutional-level, policy frameworks such as the SHEILA framework and DELICATE checklist have been developed.

General Overviews

These works introduce key ideas and document the emergence of the field. In the late 2000s, the rise of big data led to new opportunities to analyze student interaction. Ferguson 2012 observed that a focus on employing data analysis to improve education emerged and split into three separate fields: electronic data mining, academic analytics, and learning analytics. Learning analytics is distinct in that it focuses on using analytics to inform learning, including the use of data-driven insights to develop interventions aimed at enhancing student success. Siemens and Long 2011 highlight the potential of using big data to support and transform learning. In 2011, the Society for Learning Analytics Research (SoLAR) defined the field, stating, “Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.” SoLAR made leading contributions to the field and many of the key thinkers emerged from this group, clustered in Australia, Canada, Europe, the United Kingdom, and the United States. Gašević, et al. 2015 emphasized the promise of learning analytics while also highlighting the central focus on learning as its application. Initially researchers documented potential applications, then quickly moved to focus on ethical application as fundamental to using the technologies well. Lang, et al. 2017 noted that research has diversified with the emergence of new technologies and possibilities for extended application in both online and face-to-face teaching. In order to encourage wider adoption, Reyes 2015 assessed stakeholder perspectives, and the distinct opportunities and challenges for implementing learning analytics. Lodge, et al. 2018 identified that specific support is needed to assist practitioners and educators to use learning analytics. Sclater 2017 strived to inform practice by disseminating expert guidance. Reyes 2015 and Lang, et al. 2017 note new challenges emerge with technological change, which researchers aim to address in order to support wider adoption of these technologies and the fulfillment of their potential.

  • Ferguson, Rebecca. 2012. Learning analytics: Drivers, developments, and challenges. International Journal of Technology Enhanced Learning 4.5/6: 304–317.

    DOI: 10.1504/IJTEL.2012.051816

    This highly cited article provides a chronological overview of the factors that led to the emergence of learning analytics as a distinct field in the early 2010s. Ferguson clearly conveys the relationship and distinction between academic analytics, educational data mining, and learning analytics. She outlines the historical context, specifically the impact of big data, online learning, and economic drivers to improve learning, while also identifying future issues for consideration.

  • Gašević, Dragan, Shane Dawson, and George Siemens. 2015. Let’s not forget: Learning analytics are about learning. TechTrends 59:64–71.

    DOI: 10.1007/s11528-014-0822-x

    Amid much excitement over the emergence of and potential applications for learning analytics, the authors provide a timely reminder that the key function is improving learning. This work provides an overview of key research literature and case studies in the field, then looks toward emerging issues which must be addressed, ultimately arguing that technical issues must be integrated with established education praxis for sustainable growth of the field.

  • Lang, Charles, George Siemens, Alyssa Wise, and Dragan Gašević. 2017. Handbook of Learning Analytics. New York: Society for Learning Analytics Research.

    DOI: 10.18608/hla17

    This comprehensive handbook brings together experts to author thirty chapters which serve as a detailed overview of the field of learning analytics, from foundational information useful for the informed novice reader, through to detailed discussion of emergent challenges, leading analytic approaches, deep applications, issues of institutional adoption, and links to complex systems which are relevant to those who are immersed in the field.

  • Lodge, Jason M., Jared Cooney Horvath, and Linda Corrin, eds. 2018. Learning Analytics in the Classroom: Translating Learning Analytics Research for Teachers. Abingdon, UK: Routledge.

    DOI: 10.4324/9781351113038

    This edited book is designed to support educators in the practical application of learning analytics in university classrooms and other learning environments. Lodge, Horvath, and Corrin have coordinated guest chapters from experts, which cover theory, understanding students, learning design, case studies of application, and guidance on implementation.

  • Reyes, Jacqueleen A. 2015. The skinny on big data in education: Learning analytics simplified. TechTrends 59.2: 75–80.

    DOI: 10.1007/s11528-015-0842-1

    Reyes provides a useful introduction to learning analytics from the perspective of multiple stakeholders. She maps the hierarchical movement of information from students to educators and institutions, and potentially broader stakeholders including researchers and government. She considers the challenges relating to shifting analytical frameworks, datasets, technologies, and reviews ethical processes, while also indicating the transformative benefits for education.

  • Sclater, Niall. 2017. Learning analytics explained. New York: Routledge.

    DOI: 10.4324/9781315679563

    As educational institutions increasingly recognize the benefits of learning analytics, Sclater argues that support is needed to inform adoption of approaches In this book, through interviews with experts and cases studies, Sclater provides guidance in practical application of learning analytics, to inform curriculum design, adaptive learning, early alerts, and other interventions for student success.

  • Siemens, George, and Phil Long. 2011. Penetrating the fog: Analytics in learning and education. EDUCAUSE Review 46.5:30–40.

    This accessible and insightful article provides a useful introduction to learning analytics. Long and Siemens clearly articulate the benefits of big data analysis for informing action, improving quality, and adding value, with the potential to transform higher education. In contrast to academic analysis which uses data to inform institutional practice, analysis specifically focused on learning provides specific benefits for students and clear directions for educators.

  • Society for Learning Analytics Research. n.d. What is Learning Analytics?.

    The Society for Learning Analytics Research (SoLAR) provides the most cited definition of learning analytics, which was established in the 2011 call for papers for their international Learning Analytics and Knowledge Conference (LAK) and widely used from that time onward. SoLAR provides a wide range of useful online content, hosts the leading conferences in the field, and publishes the Journal of Learning Analytics.

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