Focus & scope

The Journal of Learning Analytics is a peer-reviewed, open-access journal (Journal Impact Factor 3.9), disseminating the highest quality research in the field. The journal is the official publication of the Society for Learning Analytics Research (SoLAR). With an international Editorial Board comprising leading scholars, it is the first journal dedicated to research into the challenges of collecting, analysing and reporting data with the specific intent to improve learning. “Learning” is broadly defined across a range of contexts, including informal learning on the internet, formal academic study in institutions (primary/secondary/tertiary), and workplace learning.

The journal seeks to connect learning analytics researchers, developers and practitioners who share a common interest in using data traces to better understand and improve learning, and the environments in which it takes place, through the creation and implementation of new tools and techniques and the study of transformations they engender.

The interdisciplinary focus of the journal recognizes that computational, pedagogical, institutional, policy and social perspectives must be brought into dialogue with each other to ensure that interventions and organizational systems serve the needs of all stakeholders. Together, these communities each bring a valuable lens to provide ongoing input, evaluation and critique of the conceptual, technical, and practical advances of the field.

The journal provides immediate open access to its content through its early access system on the principle that making research freely available to the public supports a greater global exchange of knowledge. The journal does not charge authors submission or processing charges to submit – costs are borne by the Society for Learning Analytics Research.

Topics of interest to the Journal of Learning Analytics include, but are not limited to, the following:

  • Educational Perspectives: Use of analytics to test, expand, refine or generate learning theory; learner and knowledge-state modeling; development of data-based indicators of productive / unproductive learning processes; trajectories of cognitive, social, and/or affective dimensions of learning; personalization and adaptation in the learning process; development of learner or instructor-facing feedback systems; changes to educational practices when analytics are introduced.
  • Computational Perspectives: Development or application of data mining and machine learning techniques to address questions of learning; use of text mining and natural language processing to assess learning processes and outcomes; capture and analysis of multi-modal learning data; advancement of network analytics to provide insight into learning communities; construction of recommendation engines; utilization of linked data; development of data or analytic frameworks.
  • Information and Sensemaking Perspectives: Information visualization and representation for various stakeholder groups (learners, instructors, designers administrators); data and multimedia literacy; user motivation and experience; support for interpretation, decision-making and action based on analytics; socio-cultural practices of learning data and learning analytics use.
  • Institutional and Societal Perspectives: Policy issues (at the institutional, national and international levels); ethical issues related to privacy, transparency and accountability; data stewardship issues; organizational dynamics; institutional processes, organizational structures and facilitatory roles.

Submission Types

Research papers

The Journal of Learning Analytics welcomes papers that describe original empirical or theory-building research. Research papers must describe original work of relevance to learning analytics or review the state of the art in a particular area of learning analytics. All research papers must make explicit their significance for the wider field of learning analytics.

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Data and tool reports

To build the community and its impact, the Journal of Learning Analytics accepts papers that describe datasets and/or tools and their significance for the learning analytics community. The learning analytics field brings data and learning together; these new submission types recognise this in the journal by making data and the tools to analyse that data available, contextualised in learning environments of relevance to the learning analytics community. These papers are intended to foster collaboration and development of new approaches based on existing community work. The data and tools reports must include links to the data or tools described, preferably in openly available public repositories; if this is not the case, the report should describe procedures for requesting access. JLA does not offer hosting services for tools or data.

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Practical reports

The Journal of Learning Analytics welcomes papers that report on the application of learning analytics across a diversity of contexts. Practical reports provide value by serving as case studies of authentic learning analytics applications and developments with relevance to the wider community. Reports describe new or innovative learning analytics practices, programs, techniques or application in a specific context of practice. These may include efforts to apply learning analytics in pilot projects or in “at scale” implementations, efforts to evaluation learning analytics use in practice, efforts to develop institutional data repositories or pipelines, efforts to develop institutional or national policies or practices surrounding learning analytics use, and critical examinations of organizational challenges, tactics and strategies.

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Open peer commentaries

In order to promote cross-community dialogue on matters of significance within the field of learning analytics, the Journal of Learning Analytics welcomes proposals in the form of abstracts for papers that will be opened for peer commentary. Once an abstract has been accepted, a date will be agreed for submission of a full paper (up to 8,000 words including the abstract, key words, tables/figures, acknowledgements, and reference list ). A paper that has been submitted and accepted for commentary will be shared as a pre-print, together with a call for commentary proposals.

Commentary proposals (up to 250 words) should include the name of the paper the commentary relates to, all proposal authors, the aspect of the paper the authors propose to comment on, and (briefly) relevant expertise authors would bring to bear. Invitations to comment will also be sent to scholars whose work is discussed in the paper and to commentators suggested by authors and editors. A maximum of ten proposals will be accepted. Selection will take into account many factors, including different areas of expertise, different perspectives, and other aspects of academic diversity. 

Successful proposal authors will be invited to submit a full commentary (up to 1,000 words including abstract, key words, tables/figures, acknowledgements, and reference list ) by an agreed deadline and may be asked to participate in the reviewing process for other commentaries. Paper and commentaries will be published together in the same journal issue.

Both paper and commentaries will undergo review by the journal editors and light-touch, single-blinded review for relevance and soundness before publication.

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Book reviews

The Journal of Learning Analytics is pleased to consider proposals for book reviews. Reviews inform readers about recent publications relevant to learning analytics and the use of big data in education. Reviews may be of single books or can be review essays that discuss and compare two or more books addressing related topics. Reviewers must have sufficient up-to-date expertise in the topic(s) of the book they propose to review and familiarity with the literature of the learning analytics field overall.

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Editorials (by invitation only)

Short articles providing an overview of a special section or journal issue, written by the journal editors or invited authors.

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Special sections

In addition, manuscripts can be submitted to Special Sections which are organised by guest-editors around a particular theme. Special Section submissions undergo the same peer review process as regular submissions. As JLA has a broad international readership that cuts across multiple disciplines and both researcher and practitioner roles, all submissions should be written to be broadly accessible and make their relevance to the field of learning analytics clear. All manuscripts submitted to the Journal of Learning Analytics should follow the manuscript submission guidelines.

Special section proposals

To submit a proposal for a special section, potential guest editors should email jla.editorial@learning-analytlcs.info with a 1-3 page document detailing:

  1. Guest editors and their respective affiliations.
  2. Proposed theme of the special section and background on why it is important and timely.
  3. Scope and relevant topics for the special section.
  4. Proposed timeline for the review process and publication.

Special section proposals will be reviewed based on their relevance to the learning analytics community, previous coverage of the topic in the journal and other publications, and likely impact on the field.