Editorial Policies

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 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 the common interest in using data traces to better understand and improve learning 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.

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 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 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).


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

 

Checked Open Submissions Checked Indexed Checked Peer Reviewed

Data and Tool Reports

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.

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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.)

 

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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. 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.

 

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Editorial

Editors
  • Simon Knight
  • Xavier Ochoa
  • Alyssa Wise
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Special Section LAK-18 Invited Papers

This special section will receive invited papers from LAK-18. A single-blind review model will be used.

Editors
  • Agathe Merceron
  • Xavier Ochoa
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Peer Review Process

The Journal of Learning Analytics operates a 'double blind' review process in which the authors do not know the identity of the reviewers and the reviewers do not know the identity of the 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 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, 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. Final decisions on manuscripts are made by the editors based on the ratings and comments provided by the referees.

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. Additionally, 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 particular 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.

 

Publication Frequency

The Journal of Learning Analytics publishes 3 issues a year in the Spring, Summer and Fall.

 

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.

The journal does not charge authors submission or processing charges to submit.

 

Archiving

CLOCKSS has permission to ingest, preserve, and serve this Archival Unit

PORTICO has permission to ingest, preserve, and serve this Archival Unit

 

Special Section Proposals

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

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

    1. The scope and relevant topics for the special section

    1. 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 following the COPE retraction guidelines.

 

 

Indexing

 

ERIC - Education Resources Information Center

Google Scholar

 

 

Author Responsibilities

It is the responsibility of the author/s to ensure:

    • Any conflicting or competing interest is disclosed on submission of their work and all sources of funding are declared.

    • They contact the Journal Editor to identify and correct any material errors upon discovery, whether prior or subsequent to publication of their work.

    • The work is original, and all sources are accurately cited, according to the Journal’s style guide.

    • The authorship of the work is accurately reflected. This means all individuals credited as authors legitimately participated in the authorship of the work, an all those who participated are credited and have given consent for publication. Authorship should be limited to those who have made a significant contribution to the conception, design, or analysis and interpretation of the work. Other contributors should be mentioned in the acknowledgement section of the paper and their contribution described. Please refer to the Authorship Statement for more information on authorship.

  • That their submission follows all Author Guidelines (including those for ensuring a blind review).

 

 

Authorship Statement

Authorship of an article in the Journal of Learning Analytics (JLA) is an important label that denotes both credit and responsibility for the work conducted and should accurately reflect individuals’ contributions.

Qualification for authorship of a JLA article is based on 3 criteria:


    1. Substantial contributions to the conception of the work, design of the study, or analysis and interpretation of the data

    1. Involvement in the writing of the manuscript to be submitted

    1. Approval of the final submitted version with responsibility for the integrity of its content



The author list for a JLA submission should include all those, and only those, who satisfy all three of the criteria. Individuals who meet the first criterion should have the opportunity to qualify for authorship by being given the opportunity to participate in the review, drafting, and final approval of the manuscript. Any changes to the author list (including order) must be approved by all authors of the original submission.

Contributors who do not meet all 3 of the criteria for authorship should not be listed as authors, but can be recognized in a separate acknowledgements section. JLA explicitly disallows the practices of guest, gift and ghost authorship; responsibility for the correct attribution of authorship resides collectively with the submitting authors.

Submissions with four or more authors will be required to submit a statement that outlines the contribution that each others has made to the work and manuscript prepartion.

 

 

Complaints

Complaints related to the Journal of Learning Analytics should be directed in the first instance to the Editors via the official journal contact information. Complaints will be investigated according to recommendations by the Committee on Publication Ethics. If complainants are unsatisfied with the response they may contact the Society of Learning Analytics Research (SoLAR).