Focus & scope

The Journal of Learning Analytics is a peer-reviewed, open-access journal, 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.
The Journal of Learning Analytics accepts the submission types that are described below. 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 Author Guidelines for Submissions.
 

Section 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.

    • Papers describing original research should include (a) thorough coverage of the related literature, (b) explanation of the research objectives and question, (c) detailed description of the research methods used, (b) clear presentation of the results found, and (e) discussion of how the obtained results advance the body of learning analytics knowledge.
    • Review papers must offer a rigorous examination of relevant literature(s) in order to put forward a novel theory, framework, or empirical result (e.g. via meta-analysis).
Maximum word lengths for the journal were revised in November 2022. Research papers should be no longer than 9,000 words in length. This includes the abstract, key words, tables/figures, acknowledgements, and reference list. References should not take up more than two pages when formatted using the journal template. We will consider manuscripts up to 12,000 words that are submitted together with a justification for their extended length.
 
The Journal of Learning Analytics welcomes only papers which are neither formally published, nor under review elsewhere. Sharing your preprints (for example, on a preprint server) will not count as prior publication. Submissions that extend previously published conference papers are welcome provided that the journal submission is sufficiently novel (at least 25-30% of new contribution) and permission to republish any prior sections is obtained. Submissions that are extensions of previously published conference papers must be accompanied by a cover letter explaining how the criterion for novelty has been met. Note that extended conference papers will undergo single-blind peer review rather than the double-blind peer review used for other submissions.

Along with the abstract, all research paper submissions should include ‘Notes for Practice’ that highlight the significance of the work for practice. These notes should comprise 3–5 bullet points outlining:
    • A brief accessible overview of established knowledge on the topic
    • A summary of the contribution of the paper
    • Key implications of the paper’s findings for practice, policy, and implementation of research
Checked Open Submissions Checked Indexed Checked Peer Reviewed

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.

Dataset reports will typically introduce data that arises from actual learning processes and will frame it with theoretical foundations that will allow understanding its context and its potential analyses. Such data can be drawn from a learning experience in any domain, in any learning setting, and with any population – all, of course, should be explicitly presented in the paper. Such data can be based on online or face-to-face settings. If relevant, complementary data (such as demographics, data from surveys, etc.) should also be provided, in order to allow a rich understanding of the learning experience.

Tools reports will typically introduce novel tools and methods to analyze data, in a way that may enable replication studies and extensions of existing analyses to other learning settings. These reports should provide details about the tool’s purpose and how to properly use it. We also expect such papers to educate readers about the ways the presented tools might enrich exploration of data, for example by presenting a few case studies.

Maximum word lengths for the journal were revised in November 2022. Data and tools reports can be up to 6,000 words in length, including the abstract, key words, tables/figures, acknowledgements, and reference list. They 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.

Checked Open Submissions Checked Indexed Checked Peer Reviewed

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 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 policies or practices surrounding learning analytics use, and critical examinations of organizational challenges, tactics and strategies.

    • Practical reports should include (a) thorough description of the pedagogical and/or institutional context for the work and the drivers / need for analytics, (b) detailed presentation of the innovation introduced (this can be a combination of tools and/or processes), (c) description of the results found and how they were obtained, (d) discussion of issues that arose / lessons learned / implications for future efforts and any known factors impacting the transferability of the findings to another context.
Maximum word lengths for the journal were revised in November 2022. Practical Reports should be up to 5,000 words in length including the abstract, key words, tables/figures, acknowledgements, and reference list and should not be formally published nor under review elsewhere. Submissions that extend previously published conference papers are welcome provided that the journal submission is sufficiently novel (at least 25-30% of new contribution) and permission to republish any prior sections is obtained. Submissions that are extensions of previously published conference papers must be accompanied by a cover letter explaining how the criteria for novelty has been met.

Along with the abstract, all practical report submissions should include ‘Notes for Research’ that highlight the significance of the work for research. These notes comprised 3–5 bullet points outlining:
    • Prior research findings the practical report draws on
    • New contributions the paper makes
    • Significance of these contributions for researchers (contextualise existing findings, suggest new areas needing research etc.)
Checked Open Submissions Checked Indexed Checked Peer Reviewed

Open Peer Commentary

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.

Checked Open Submissions Checked Indexed Checked Peer Reviewed

 

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.

Maximum word lengths for the journal were revised in November 2022. Journal of Learning Analytics book reviews should be no more than 2,000 words in length and provide a concise introduction to the book's primary topics, themes and messages followed by a critical analysis of its strengths, weaknesses, and relevance to the field.

To propose a book review, please email the editors at jla.editorial@gmail.com with:

    • The citation and a brief description of the book proposed for review
    • The proposed reviewer's CV
    • A short rationale for why the book is worthy of review / will be of interest to the JLA audience
All prospective reviewers must adhere to our ethics and conflict of interest policy: they are expected not to have close personal or collegial relations to the authors of materials they propose to review, nor any vested interest in the promotion or sale of the book. Unsolicited reviews are not accepted. Do not submit a review without first proposing it and having it approved by the editor.s

Commissioned reviews will be given careful editorial attention and a dialogue with the reviewer may occur before a final version is accepted.

 

Unchecked Open Submissions Checked Indexed Unchecked Peer Reviewed

Editorial

Editors
  • Rebecca Ferguson
  • Vitomir Kovanović
  • Olga Viberg
  • Xavier Ochoa
Unchecked Open Submissions Checked Indexed Unchecked Peer Reviewed

Peer Review Process

The Journal of Learning Analytics operates a 'double blind' review process in which authors do not know the identity of reviewers and reviewers do not know the identity of authors. Details on how to prepare your manuscript to ensure a blind review are given in the Author Guidelines for Submissions.

The review process includes at least two anonymous referees. Once a new paper is submitted, it is assigned to one of the editors who checks whether the submission is relevant for the scope of the journal and ready for consideration by reviewers (e.g., adequate coverage of the related peer-reviewed work, description of the research method, or quality of language). Passing verification, the submission is sent out for external review, using the appropriate reviewer form. Where revisions are requested, authors should provide a detailed covering letter outlining how they have responded to each point raised by the reviews. Revised versions of the manuscripts should not exceed the word limits given above. Final decisions on manuscripts are made by the editors based on ratings and comments provided by the reviewers.

Our editors are required to declare any potential competing interests in undertaking their editorial duties. In cases where a manuscript is submitted by a colleague at their own institution or from their research networks, editors will remove themselves from the decision-making process. A co-editor, or an external trusted expert, with no such connections, is then asked to act as the editor for that particular paper. An editor will have no input or influence on the peer review process or publication decision for an article they have authored and submitted to the journal. Should a member of a journal’s editorial team submit a manuscript to the journal, a co-editor, or external trusted expert, will be assigned to manage the entire review process and act as editor for that paper. If the article proceeds to publication, it will be explicitly stated on the article that the editor who submitted the paper has had no involvement with the journal’s handling of this particular article, along with the reasons for this, and the name of the assigned editor.

This policy should be read alongside other relevant journal policies, including those concerning conflicts of interest.