Editorial Policies

Focus and 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 comprised of leading scholars, JLA is the first journal dedicated to research into the challenges of collecting, analysing, reporting and mobilizing data traces with the specific intent to improve learning processes and outcomes. “Learning” is broadly defined across a range of contexts, including informal learning on the internet, formal academic study in institutions at all levels (primary/secondary/tertiary), and workplace learning.

The Journal of Learning Analytics connects researchers and practitioners who share the common interest of using data traces to better understand and improve learning through the creation and dissemination 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 domains of study must be brought into dialogue with each other to ensure that analytic tools, learning implementations and organizational systems serve the needs of all stakeholders. Together, these communities each bring a valuable perspective to provide ongoing input, evaluation and critique of the conceptual, technical, and practical advances of the field.

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 or generate learning theory; learner and knowledge-state modeling; development of data-based indicators of productive / unproductive learning processes; trajectories of the cognitive, social, and affective dimensions of learning; personalization and adaptation in the learning process; the development of learner or instructor-facing feedback systems.

  • 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.

  • 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 a number of 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 Policies



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 work 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).


Research papers should be between 6,000 and 10,000 words in length not including the abstract, key words, tables/figures, acknowledgements, and reference list and should not be formally published nor under review elsewhere. The Journal of Learning Analytics welcomes only papers which are neither 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 research paper submissions should include ‘Notes for Practice’ that highlight the significance of the work for practice. These notes should be comprised of bullet points outlining:

  • A brief accessible overview of the 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



Practical Reports

The Journal of Learning Analytics welcome 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.


Practical Reports should be between 3,000 and 5,000 words in length not 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 should be comprised of bullet points outlining:

  • What prior research findings does this practical report draw on

  • What new contributions does the paper make

  • What significance does this have for researchers (contextualise existing findings, suggest new areas needing research etc.)



Tools & Data Set Presentations

To build the community and its impact, the Journal of Learning Analytics is now accepting 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 detail 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.

Both data and tools reports should be between 4,000 and 6,000 words in length not 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.

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. 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 to not 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. Please do not send a review without first proposing it and having it approved by the editor.

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

Special Section Proposals

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

  1. The guest editors and their respective affiliations

  2. The proposed theme of the special section, and background on why it is important and timely

  3. The scope and relevant topics for the special section

  4. A proposed timeline for the review process and publication


These details will form the basis of the call for papers for the special section. Special section proposals will be reviewed based on their relevance to the learning analytics community and likely impact on the field.

Statement on Publication Ethics and Misconduct


The Journal of Learning Analytics subscribes to the UTS ePress Statement on Publication Ethics and Transparency and follows the ethical standards set by the Committee on Publication Ethics (COPE).

As part of the submission process, authors are required to check off their submission's compliance with the following items related to publication ethics. Submissions that do not adhere to these guidelines will be returned to authors.

  • The submission is original and has not been previously published, nor has it been submitted to another journal for consideration.

  • The text adheres to the ethic, stylistic and bibliographic requirements outlined in the Author Guidelines


In case of misconduct, plagiarism, or if a paper is found not to be original, it will be rejected or removed.

 

Section Policies

 

Peer Review Process

The review process includes at least two anonymous referees. Once a new paper is submitted, it is assigned to one of the editors who verifies if 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. Final decisions on manuscripts are made by the editors based on the ratings and comments provided by the referees.

 

Open Access Policy

This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.

 

Indexing

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