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LAK 2027: International Learning Analytics & Knowledge Conference

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Submission Date:
2026-09-28 Due in 2 days
Notification Date:
2026-11-24
Conference Date:
2027-03-08
Location:
Recife, Brazil
Years:
ICORE: A   Viewed: 20434   Tracked: 3   Attend: 0

Conference Partner Index (CP-I)

60.5 / 100
Ranked #590 of 5,687 conferences · Top 11%
Academic recognition (35%)
88
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
67
Community attention (10%)
25
Public record completeness (15%)
25

Inputs used: Listed as ICORE A · Editions on record: 11 · Researchers following it here: 3 · Researchers who opened this page in the past 24 months: 4

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 80% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-25

Call For Papers

LAK 2027 (International Learning Analytics & Knowledge Conference) is a ICORE A conference held in Recife, Brazil on 2027-03-08. The paper submission deadline is 2026-09-28. Acceptance notifications are sent on 2026-11-24.

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.
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