General Call
The 2027 edition of The International Conference on Learning Analytics & Knowledge (LAK27) will take place in-person in Recife, Brazil. LAK27 is organized by the Society for Learning Analytics Research (SoLAR) with CESAR and Universidade Federal Rural de Pernambuco.
LAK27 is a collaborative effort by researchers and practitioners to share the most rigorous, cutting-edge research in learning analytics (LA).
The theme for the 17th annual LAK conference is Leveraging learning analytics to help individuals and communities thrive. Learning analytics has made significant strides in modeling and predicting learner performance. Still, the field is increasingly recognizing that performance metrics alone cannot capture what it means for a person to learn, grow, and succeed. Learners are complex human beings, and learning analytics is uniquely positioned to engage with that complexity. This means turning our analytical tools toward broader dimensions of human development, such as agency, equity, well-being, and lifelong growth, and designing systems that support inclusive, sustainable learning experiences. This year's conference invites work that takes up that challenge by using learning analytics not just to describe or predict, but to actively help individuals and communities thrive.
We call for research investigating how learning analytics can generate theoretically grounded and actionable insights that support learners, educators, institutions, and communities in achieving meaningful educational outcomes. We particularly welcome work that explores how learning analytics can promote learner agency, well-being, equity, inclusion, and lifelong learning across diverse educational contexts. We encourage contributions that move beyond prediction and description towards the design, implementation, and evaluation of interventions that help individuals and communities thrive. We also invite research examining the ethical, social, and organizational conditions necessary for learning analytics to create sustainable and positive impacts at scale.
The LAK conference is intended for both researchers and practitioners. We invite researchers and practitioners of learning analytics to join a proactive dialogue around the future of learning analytics and its practical adoption. We also extend our invitation to educators, leaders, administrators, government, and industry professionals interested in learning analytics and its related disciplines.
Conference Theme and Topics
We welcome submissions from researchers and practitioners across disciplines who use data to generate theoretically grounded, actionable insights about learning and teaching. We encourage submissions from across learning analytics processes, including:
Concept Development: Studies that investigate foundational questions about the data sources, methods, technologies, and theoretical frameworks that underlie learning analytics, including new theoretical perspectives, novel conceptualizations of data and methods, and critical examinations of how the field understands the role of learning analytics in teaching and learning.
Prototyping & Proof of Concept: Studies that use learning analytics methods to build and test tools for teaching and learning, and that demonstrate how methods and technologies — including AI — can advance specific phases of the analytics process (data collection, transformation, analysis, interpretation, communication) through iterative development and testing.
Evaluation: Studies that assess the efficacy, generalizability, and real-world viability of learning analytics implementations in educational systems, including the data-driven insights, predictions, and recommendations they generate for teachers, learners, and other stakeholders. This category also welcomes critical examination of the ethical and responsible use of analytics tools and technologies such as AI.
Dissemination: Studies that examine how learning analytics innovations are adopted by individuals, scaled, and implemented across institutions. Including data pipelines, training programs, communities of practice, policy implementation, and the development of sustainable pathways for getting analytics tools, including those powered by AI, into educators' hands.
Impact & Iteration: Studies that assess the longitudinal, real-world impact of learning analytics interventions at scale, including how real-time analytics, such as AI-driven systems, adapt to changing educational contexts over time, and how learning analytics implementation feeds back into new research and implementation cycles.
Usual topics of interest include, but are not limited to, the following:
Tracing Learning & Teaching:
- Finding evidence of learning: Studies that identify and explain useful data for analyzing, understanding, and enhancing learning and teaching. This may include – but is not limited to – sampling frames; software interfaces for tracing learning and affective events; sensors for recording physiological indicators of learning and other processes; properties of archival data; utterances exchanged with chatbots; artificially intelligent agents for soliciting learners’ on-the-fly accounts of learning processes and affect/emotions; teacher observation methods; and experience sampling methods.
- Assessing student learning: Studies that assess learning progress through the computational analysis of learner actions or artifacts and social interactions with peers and teachers that might support the attainment of intended learning outcomes.
- Analytical and methodological approaches: Studies introducing analytical techniques, methods, and tools for modeling student learning to empower learners or other educational stakeholders and enhance learning. Methods could be based on artificial intelligence, machine learning, natural language processing, social network analysis, text mining, and knowledge tracing, among others, if targeted toward LA-oriented questions. These approaches include the use of multimodal learning analytics as well as flexible approaches to learning and teaching that support learning processes, learner achievements, and learner well-being.
- Technological infrastructures for data storage and sharing: Proposals of technical and methodological procedures to store, share, and preserve learning and teaching traces, taking appropriate privacy-preserving and ethical considerations into account, and involving feedback loops to stakeholders.
Understanding Learning & Teaching:
- Data-informed learning theories: Proposals of new learning/teaching theories or revisions to / reinterpretations of / support for existing theories based on or related to large-scale data analysis.
- Insights into specific learning processes: Studies to understand particular aspects of a learning/teaching process through a learning-theory-informed use of data science methods and techniques, including negative results.
- Learning and teaching modeling: Creating mathematical, statistical, or computational models of a learning/teaching process, including its actors and context, when targeted toward learning analytics-oriented questions. Note that this contrasts with work focused on comparing algorithms or prediction models, which may be better targeted toward educational data mining (EDM).
- Systematic and meta reviews: Studies that provide a systematic and methodological synthesis of the available evidence in learning analytics.
Impacting Learning & Teaching:
- Human-centered design processes: Research that documents practices of giving learners, teachers, and other educational stakeholders an active voice in the design process of learning analytics initiatives and enabling technologies.
- Providing decision support and feedback: Studies that evaluate the use and impact of feedback or decision-support systems based on learning analytics (dashboards, early-alert systems, automated messages, etc.). This includes data visualization and data storytelling with learning-related data.
- Data-informed decision-making: Studies that examine how teachers, students or other educational stakeholders come to, work with, and make changes using learning analytics information. It also includes action research and qualitative investigation of how stakeholders understand and utilize data in educational contexts.
- Personalized and adaptive learning: Studies that evaluate the effectiveness and impact of adaptive technologies based on learning analytics.
- Practical evaluations of learning analytics efforts: Empirical evidence about the effectiveness of learning analytics implementations or educational initiatives guided by learning analytics.
Implementing Change in Learning & Teaching:
- Ethical issues around learning analytics: Analysis of issues and approaches to the lawful and ethical capture, protection, and use of educational data traces, including data privacy; tackling unintended bias and value judgements in selecting data and algorithms; perspectives and methods that empower stakeholders.
- Equity, fairness, and transparency in learning analytics: Consideration of how certain practices of data collection, analysis, and subsequent action impact particular populations and affect human well-being, specifically groups that have been previously disadvantaged. Discussions of how learning analytics may impact (positively or negatively) social change and transformative social justice.
- Learning analytics adoption: Discussions and evaluations of strategies to promote and embed learning analytics initiatives in educational institutions and learning organizations. Studies that examine organizational change processes and professional development practices that support impactful learning analytics use.
- Learning analytics strategies for scalability: Discussions and evaluations of strategies for scaling the capture and analysis of information in useful and ethical ways at the program, institution or national level; critical reflections on organizational structures that promote analytics innovation and impact in an institution.
- Strategic planning of learning & teaching: Consideration and application of trustworthy learning analytics in all phases of strategic planning, including needs analysis for change, decision-making, implementation and monitoring, as well as evaluation of strategic decisions, taking into account the complexity and context of the strategic decision-making domain.
Out of Scope for Learning Analytics
- Work focused solely on aspects of methodology and data manipulation that does not discuss, work towards or include closing the loop to stakeholders, for example, by empowering learners, teachers, or other educational stakeholders.
- Work without any data or evidence that relates to learners, educators, or learning processes.
- Work introducing analytical techniques, methods, and tools without connecting to learners or learning processes.
- Work focused on comparisons of different approaches to predictive modelling or algorithm accuracy is likely to be a better fit for educational data mining (EDM).
- Work that evaluates the effectiveness and impact of adaptive/personalised learning technologies that are not explicitly grounded in learning analytics.
- Classic ed-tech research is better suited for the International Society of the Learning Sciences (ISLS) or similar venues in the education sciences. This includes quantitative, qualitative and mixed-methods studies that use data related to education and learning but do not make strong connections to learning analytics in their research questions or findings.
- Work that focuses on comparisons between departments, institutions, regions or countries (for example management dashboards or work using PISA data) is usually classed as academic analytics.
- Studies involving AI may be in or out of scope, depending on the context and intention of the work. For example, work on how AI is deployed in primary classrooms might be a better fit for AI-Ed, but using analytics in evaluations of the effectiveness of AI-supported learning environments is in scope for learning analytics.
- Studies that focus primarily on AI system performance, technical optimization, benchmarking, or general-purpose AI applications without meaningful connection to learning processes, stakeholder action, or learning analytics questions are likely to be out of scope.
- Work without a sound theoretical foundation in learning theory or that does not use sound methodologies when collecting and analysing data.
- Studies that do not situate themselves in relation to previous work in LA. This might involve building on, extending, supporting, or revising that work, or identifying a gap that should be filled. A paper that does not mention ‘learning analytics’ in its text or references is highly likely to be out of scope.
- Studies that do not articulate how their findings, methods, or interventions contribute to learning analytics theory, practice, or stakeholder impact are unlikely to be considered within scope.
Conference Tracks
The conference has three distinct submission types, which are described below. Please see the submission guidelines page (coming soon) for information on paper format and other technical details for each track.
The research track focuses on advancing scholarly knowledge in the field of learning analytics through rigorous reports of learning analytics research studies. The primary audience includes academics, research scientists, doctoral students, postdoctoral researchers, and other types of educational research staff working in different capacities on learning analytics research projects.
This year, accepted LAK Research Track papers will be published in the Journal of Learning Analytics (JLA). As such, all submissions in this track must follow the JLA journal paper template (Word, LaTeX or Overleaf). Please note that all submissions, reviews AND camera ready paper uploads will all take place within EasyChair. We will not be uploading to TAPS for 2027. No copyediting for LAK papers, onus is on authors to ensure correct language, grammar, template are accurate and followed.
Submission types for the research track are similar to other years:
- Full research papers (from 10 to 14 pages in JLA format, including references and notes for practitioners) include a clearly explained substantial conceptual, technical or empirical contribution to learning analytics. The scope of the paper must be placed appropriately with respect to the current state of the field, and the contribution should be clearly described. This includes the conceptual or theoretical aspects at the foundation of the contribution, an explanation of the technical setting (tools used, how are they integrated into the contribution), analysis, and results. See the bulleted list of questions above for more detailed ideas on useful elements to include.
- Short research papers (from 6 to 8 pages in JLA format, including references and notes for practitioners) can address on-going work, which may include a briefly described theoretical underpinning, an initial proposal or rationale for a technical solution, and preliminary results, with consideration of stakeholder engagement issues. See the bulleted list of questions above for more detailed ideas on useful elements to include.
DEADLINE: All LAK27 submission deadlines can be found here.
Should you have further questions regarding paper length or format, please contact us at lakconference@gmail.com.
The practitioner track is part of the main conference program and provides a way in which real-world learning analytics (LA) implementations in use by practitioners can be shared with the entire community. The intent of the stream is to contribute to our collective understanding of learning analytics in practice, analysis of the use of learning analytics in product development and improvement, learning analytics intervention deployment, research-informed evaluations of learning analytics implementations and methodologies. Specifically, some of the goals of practitioner presentations are to:
- contribute to the conversation between researchers and practitioners around adoption, implementation, scaling and evaluation of learning analytics,
- provide insights from practice around factors enabling or constraining learning analytics adoption and implementation,
- share practical insights and lessons derived from deploying learning analytics in different learning contexts,
- conduct critical analysis of learning analytics adoption strategies and approaches,
- share experiences on developing a business case for learning analytics adoption, and/or
- provide experience of innovative evaluation methodologies or approaches.
To meet these goals, submissions are encouraged to reflect on the context and purpose of the presented learning analytics initiative, discuss implementation, outcomes, impacts, and what was learned, then consider the implications for others attempting similar work. We also encourage submissions where an initiative did not achieve what was expected, as we believe that such examples provide valuable knowledge to the community.
We welcome submissions that fall in the scope described above from anyone, regardless of their institutional roles. Some examples of practitioner participants are:
- Educators, students, learning/instructional designers, faculty developers, and other staff directly involved in learning, teaching, and assessment within educational institutions.
- Policy makers, department leads, instructional technologists, analysts, and other services staff from education institutions.
- Developers, designers, analysts, and other representatives from commercial and industry entities, start-ups, non-profit organizations, and government bodies.
- Corporate trainers, instructors and other staff involved in the skills development of employees, alternative credentialing models and the impact on using data to guide corporate learning programs.
Successful submissions are expected to offer unique or distinct insights into practical applications, intervention designs, analyses, and/or the processes surrounding their implementation. Such practical implications are intended to shed light on how LA can be applied in real-world settings and support practitioners, policymakers, educators, designers, or other non-academic or cross-sector stakeholders.
The more complete the report of the work is, including usage of the learning analytics and their impact, the higher the probability of being selected for inclusion. Submissions should include:
- thorough description of the institutional/organizational context for the work,
- the background of why the initiative was implemented and/or product was developed,
- detailed presentation of its implementation and results,
- discussion of issues that arose / lessons learned / implications for future efforts by others in the LAK community attempting similar work.
The following criteria will guide reviewers when selecting submissions, although we recognise that this list may not be applicable to all submissions. Authors are encouraged to consider the following when preparing their submissions:
- Learning/education related: The submission should describe work that utilizes learning analytics, either at an educational institution or in an area (such as corporate training, health care or informal learning) where the goal is to improve the learning environment or the learning outcomes.
- Stakeholder involvement: All submissions should include perspectives from people who have participated in the initiative or intervention in a learning environment, and/or have been impacted by it (such as faculty, students, administrators and trainees).
- No sales pitches: While submissions from commercial suppliers are welcomed, reviewers will not accept overt (or covert) sales pitches. Reviewers will look for evidence that the presentation will take into account challenges faced, problems that have arisen, and/or user feedback that needs to be addressed.
- Disclosure: Submissions from commercial or funded entities should disclose potential conflicts of interest and note how stakeholder feedback or ethical concerns were incorporated into their development process.
Practitioner Report (2-4 page document including references, using the SoLAR companion proceedings template) should include accounts and findings that stem from practical experience in implementing learning analytics projects incorporating the following sections:
- Presenter’s Names (Left blank for double blind submission // Only add in for camera-ready version)
- Presentation Title
- Abstract (100-150 words)
- Keywords (up to 10)
- Full Presentation Description
DEADLINE: All LAK27 submission deadlines can be found here.
All accepted submissions to the Practitioner track will be published in the LAK27 Companion Proceedings and archived on the SoLAR website.
- Posters (3 pages, SoLAR companion proceedings template) represent i) a concise report of recent findings or other types of innovative work not ready to be submitted as a full or short research paper or ii) a description of a practical learning analytics project implementation which may not be ready to be presented as a practitioner report. Poster presentations are part of the LAK Poster & Demo session, and authors are given a physical board to present and discuss their projects with colleagues.
- Interactive demos (200 words abstract in SoLAR companion proceedings template + 5 min video) provide opportunities to showcase interactive learning analytics tools. Interactive demonstrations are part of the LAK Poster & Demo session, and presenters are given a physical space to demonstrate their latest learning analytics projects, tools, and systems. Demos should be used to communicate innovative user interface designs, visualizations, or another novel functionality that tackles a real user problem. Tools may be prototypes in an early stage of development or relatively mature products. In whichever stage, tools should have been field-tested with an authentic use case and provide some results and feedback. Submissions for conceptual products or for products that have not been used by instructors and/or students are unlikely to be accepted.
DEADLINE: All LAK27 submission deadlines can be found here.
The focus of pre-conference events is on providing space for new and emerging ideas in learning analytics and their further development. They also offer valuable opportunities to connect with colleagues around shared research interests, methodologies, or ongoing research projects. Events can have either a research or a practical focus and can be structured in a way that best serves their particular purpose.
The types of submissions for the pre-conference event track are:
- Workshops (4 pages, SoLAR companion proceedings template) provide an efficient forum for community building, sharing of perspectives, training, and idea generation for specific and emerging research topics or viewpoints.
We welcome proposals for a diverse range of workshop formats that foster meaningful engagement across the Learning Analytics community. Workshops may emphasize knowledge exchange, collaborative problem solving, skill development, community building, or the creation of shared research resources. Organizers are encouraged to select the format that best supports their goals. Successful proposals should clearly define the types of activities participants should expect, level of participation etc. Some example formats might include:- Symposia, featuring position papers, invited presentations, and moderated discussion around a focused research area.
- Guided Exploration Workshops, which use structured activities to examine emerging questions, develop shared understanding, or identify future research directions.
- Collaborative Working Groups, where participants work together to produce tangible community resources such as white papers, benchmark datasets, evaluation protocols, software, or research roadmaps.
- Innovation Labs or design jams that facilitate collaborative ideation and rapid prototyping of new concepts, methods, or tools.
- Other creative and interactive formats that clearly articulate their objectives, participant experience, and expected outcomes.
- Tutorials (4 pages, SoLAR companion proceedings template) aim to educate or train stakeholders on specific learning analytics topics and/or perspectives. Proposals should be clear about the need for particular knowledge, the target audience and their prior knowledge, and the intended learning outcomes. Successful proposals should also describe how the tutorial will engage participants through active learning and interactive activities, the minimum prior knowledge to join the tutorial, and outline the resources, materials, or other take-home artifacts that will support participants in applying and extending their learning beyond the conference.
To ensure a consistent and equitable review process, all workshop and tutorial proposals must be submitted in English. However, accepted sessions may be delivered in English, Spanish, or Portuguese, or incorporate multiple languages where appropriate. The intended language(s) of delivery should be clearly stated in the proposal.
In addition to the information described above, proposals should clearly indicate whether the session is intended as a half-day or full-day workshop or tutorial and include a proposed agenda or schedule that outlines the planned activities and approximate timing. Proposals should also describe the expected number and roles of organizers and facilitators, any anticipated participant prerequisites or preparation, any technical or room requirements, and the expected number of participants (including any capacity limits, if applicable). Where relevant, proposers are encouraged to describe any planned outputs or follow-up activities, such as publications, community resources, or ongoing collaborations that will extend the impact of the session beyond the conference.
DEADLINE: All LAK27 submission deadlines can be found here.
For more information please review the Doctoral Consortium Call.
DEADLINE: All LAK27 submission deadlines can be found here.
For more information and how to participate in the AI Agents Academy at LAK27, please visit the AI Academy webpage for full application details.
DEADLINE: All LAK27 submission deadlines can be found here.
Review Process
LAK27 will use a double-blind peer review process for all submissions except workshops, demos, AI Agents Academy, and the doctoral consortium (which each require elements that prevent blinding).
To continue to strengthen the review process for both authors and reviewers, LAK27 will have a rebuttal phase for full and short research papers in which authors will be given seven days to flag any inaccuracies, omissions, or errors in the reviews in a maximum of 500 words. Please note that rebuttals are optional and should not be treated as responses to reviewer comments for revisions. Therefore, we urge authors to prepare and submit a rebuttal if and only if they note inaccuracies, misunderstandings, or other kinds of errors in the reviewers’ comments. In such cases, the authors are requested to list inaccuracies, omissions, and errors in reviewer comments first, and then reply to specific errors. When writing the reply, authors should keep in mind that papers are being evaluated as submitted and thus, the response should not propose new results or restructuring of the presentation.
Meta-reviewers, senior members of the research community, make final recommendations for paper acceptance or rejection with justification to the program committee chairs after the rebuttal phase is concluded. Acceptance decisions are ultimately taken by the program committee chairs based on all available information from the review process in combination with the constraints of the allowable space in the conference program.
Proceedings Publication
Accepted full and short research papers will be included in the LAK27 conference proceedings published as a special issue(s) in the Journal of Learning Analytics. Other types of submissions (posters, demos, workshops, tutorials, practitioner reports and doctoral consortium) will be included in the open access LAK companion proceedings, published on SoLAR’s website. Please note at least one of the authors of each accepted submission must register for the conference by the Early Bird deadline in order for the paper to be included in the LAK Conference or LAK Companion Proceedings.
Important Dates for LAK27
All LAK27 Deadlines are 11:59 pm AOE.
Research Track
| Full / Short Submission Deadline | 28 Sept 2026 |
| Rebuttal submission open | 2 Nov 2026 |
| Deadline for rebuttals | Nov 2026 |
| Notification of Acceptance | 24 Nov 2026 |
| Deadline for camera ready | 14 Dec 2026 |
Practitioner Reports
| Practitioner Submission Deadline | 13 Oct 2026 |
| Notification of Acceptance | 1 Dec 2026 |
| Deadline for camera ready | 16 Dec 2026 |
Poster / Demos
| Poster/Demo Submission Deadline | 9 Nov 2026 |
| Notification of Acceptance | 1 Dec 2026 |
| Deadline for camera ready | 16 Jan 2026 |
Doctoral Consortium
| DC Submission Deadline | 13 Oct 2026 |
| Notification of Acceptance | 1 Dec 2026 |
| Deadline for camera ready | 16 Dec 2026 |
AI Agents Academy
| AI Agents Academy Submission Deadline | 13 Oct 2026 |
| Notification of Acceptance | 1 Dec 2026 |
Workshops / Tutorials
| Workshop/Tutorial Submission Deadline | 21 Sept 2026 |
| Notification of Acceptance for workshop organization | 13 Oct 2026 |
| Submission Deadline for papers to individual workshops that issue calls** | 4 Dec 2026 |
| Notification of Acceptance for papers submitted to individual workshops | 18 Dec 2026 |
| Deadline for camera ready (Workshop organizer proposal docs) (individual workshop papers are not included in LAK proceedings) | 17 Jan 2027 |
**Workshop Paper Submissions - this term refers to papers submitted to be presented within an accepted LAK pre-conference workshop. Many LAK workshops are mini-symposium style and issue calls for papers. Please visit the pre-conference schedule when available to view which workshops have CFP’s that you may submit to.
Conference and Registration Dates
| Early-bird registration closes at 11:59 pm PST | 15 Jan 2027 |
| LAK27 conference, Recife, Brazil | 8-12 March 2027 |