The Society for Learning Analytics Research (SoLAR) is an interdisciplinary network of leading international researchers who are exploring the role and impact of analytics on teaching, learning, training and development.

The 2024 edition of The International Conference on Learning Analytics & Knowledge (LAK24) will take place in Kyoto, Japan. LAK24 is organized by the Society for Learning Analytics Research (SoLAR) with Kyoto University. LAK24 is a collaborative effort by learning analytics researchers and practitioners to share the most rigorous cutting edge work in learning analytics.

The theme for the 14th annual LAK conference is Learning Analytics in the Age of Artificial Intelligence. Artificial intelligence has been relevant for learning analytics since the early days of the field. This has mostly been manifested by building upon the algorithms of machine learning to analyze data about learners and learning environments. The conversations about artificial intelligence in education used to be mostly contained within specialized communities of practitioners and researchers. Since late 2022, this has rapidly changed. Discourse in mainstream media and among the general public has been dominated by the coverage of the developments in generative artificial intelligence. The notable examples are such technologies as ChatGPT and DALL-E that harness the power of deep learning algorithms to generate impressively human-like text and images based on relatively simple human prompts. These technologies have given some glimpses about the emerging age of artificial intelligence. The prominence of artificial intelligence has also opened profound debates about implications on education from the need to develop relevant literacies to work with artificial intelligence to challenging the established notions of assessment in education. Through the theme of the 14th annual LAK conference, we encourage the authors to consider implications for learning analytics and the role the field can play in the age of artificial intelligence.

The LAK conference is intended for both researchers and practitioners. We invite both researchers and practitioners of learning analytics to come and join a proactive dialogue around the future of learning analytics and its practical adoption. We further extend our invite to educators, leaders, administrators, government and industry professionals interested in the field of learning analytics and its related disciplines.

Authors should note that:

The LAK conference has received a CORE ranking of A (top 16% of all 783 ranked venues).
LAK is the only conference in the top 15 Google Scholar citation ranks for educational technology publications.

Conference Theme and Topics

We welcome submissions from both research and practice, encompassing different theoretical, methodological, empirical and technical contributions to the learning analytics field. Learning analytics research draws on many distinct academic fields, including psychology, the learning sciences, education, neuroscience, computer science, artificial intelligence, human-computer interaction, and design. We encourage the submission of work conducted in any of these traditions, as long as it is done rigorously. We also welcome research that validates, replicates and examines the generalizability of previously published findings, as well as examines aspects of adoption of existing learning analytics methods and approaches.

Specifically, this year, we encourage contributors to consider learning analytics in the age of artificial intelligence. Artificial intelligence offers novel technologies that can be useful for addressing important problems in learning analytics from analysis of large amounts of data to assessment and to generation of personalized feedback. Artificial intelligence sets the new context in education that has triggered many to take relevant steps to adapt from the ways we generate content to the ways we design assessments and to the types of skills we need to develop. Adaptation to this new context requires fundamental research about implications on learning, teaching, and education, and learning analytics can play a considerable role.

Thus for our 14th Annual conference, we encourage authors to address some of the following questions in their submissions:

How can novel artificial intelligence technologies be used to advance data collection, measurement, analysis and reporting in learning analytics?
What is the role of learning analytics in the changing focus of assessment from product to process that is happening due to the growing adoption of artificial intelligence?
To what extent can learning analytics explain, inform, and support learning and teaching processes that involve the use of generative artificial intelligence technologies?
What are the implications for ethics and trustworthiness in learning analytics in the age of artificial intelligence?
What are the skills teachers and students need to have to work with learning analytics in the age of artificial intelligence?
How should educational policies and strategies be adapted to support the use of learning analytics in the age of artificial intelligence?
To what extent can communication of the results of learning analytics be enhanced with the use of artificial intelligence?
How do we establish the right balance between human control and automation of learning analytics in the age of artificial intelligence?
What approaches should we use to design learning analytics for the age of artificial intelligence?

Disclaimer: The authors are not required to address the theme of the conference and the consideration of the papers for acceptance will not be based on their fit to the conference theme. The purpose of every annual conference’s theme is to encourage discussions and debate on emerging topics and issues related in the field.

Topics of interest include, but are not limited to, the following:

Implementing Change in Learning & Teaching:

Ethical issues around learning analytics: Analysis of issues and approaches to the lawful and ethical capture and use of educational data traces; tackling unintended bias and value judgements in the selection of data and algorithms; perspectives and methods that empower stakeholders.
Learning analytics adoption: Discussions and evaluations of strategies to promote and embed learning analytics initiatives in educational institutions and learning organizations. Studies that examine processes of organizational change and practices of professional development that support impactful learning analytics use.
Learning analytics strategies for scalability: Discussions and evaluations of strategies to scale 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.
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.

Understanding Learning & Teaching:

Data-informed learning theories: Proposals of new learning/teaching theories or revisions/reinterpretations of existing theories based on large-scale data analysis.
Insights into specific learning processes: Studies to understand particular aspects of a learning/teaching process through the use of data science techniques, including negative results.
Learning and teaching modeling: Creating mathematical, statistical or computational models of a learning/teaching process, including its actors and context.
Systematic reviews: Studies that provide a systematic and methodological synthesis of the available evidence in an area of learning analytics.

Tracing Learning & Teaching:

Finding evidence of learning: Studies that identify and explain useful data for analyzing, understanding and optimizing learning and teaching.
Assessing student learning: Studies that assess learning progress through the computational analysis of learner actions or artifacts.
Analytical and methodological approaches: Studies that introduce analytical techniques, methods, and tools for modeling student learning.
Technological infrastructures for data storage and sharing: Proposals of technical and methodological procedures to store, share and preserve learning and teaching traces, taking appropriate ethical considerations into account.

Impacting Learning & Teaching:

Human-centered design processes: Research that documents practices of giving an active voice to learners, teachers, and other educational stakeholders 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.).
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.
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.