LAK 2020 : International Conference on Learning Analytics And Knowledge

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 related disciplines.

If you have further questions do not hesitate to contact [email protected]

Conference theme and topics

We welcome submissions from both research and practice, covering different theoretical, methodological, empirical and technical contributions to learning analytics field. Specifically, this year, we invite contributors to think about the implications and potential impact of the presented work for the next 10 years. In their contributions, we encourage authors to address some of the following questions:

1. What are the practical and scholarly implications of the presented work for the next ten years?
2. What are the challenges of the presented work we need to address to improve its impact in the next ten years?
3. How can the presented work be practically implemented and adopted?

We also explicitly encourage research that validates, replicates and examines the generalisability of previously published findings, as well as the aspects of practical adoption of the existing learning analytics methods and approaches. Finally, we also invite authors to submit dedicated short research paper submissions (see below for details) that explicitly address the theme of this year’s conference.

Some of the topics of interest include, (but are not limited) are:

Capturing Learning & Teaching:

• Finding evidence of learning: Studies that identify and explain useful data for analysing, understanding and optimising learning and teaching.
• Assessing student learning: Studies that assess learning progress through the computational analysis of learner actions or artefacts.
• Analytical and methodological approaches: Studies that introduce analytical techniques, methods, and tools for capturing and modelling student learning.
• Technological infrastructures for data storage and sharing: Proposals of technical and methodological procedures to store, share and preserve learning and teaching traces.

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.
• 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.

Impacting Learning & Teaching:

• Providing decision support and feedback: Studies that evaluate the impact of feedback or decision-support systems based on learning analytics (dashboards, early-alert systems, automated messages, etc.).
• Practical evaluations of learning analytics efforts:  Empirical evidence about the effectiveness of learning analytics implementations or educational initiatives guided by learning analytics.
• Personalised and adaptive learning: Studies that evaluate the effectiveness and impact of adaptive technologies based on learning analytics.

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 for value-sensitive, participatory design that empowers stakeholders.
• Learning analytics adoption: Discussions and evaluations of strategies to promote and embed learning analytics initiatives in educational institutions and learning organisations.

Learning analytics strategies for scalability: Discussions and evaluations of strategies to scale the capture and analysis of information at the program, institution or national level; critical reflections on organisational structures that promote analytics innovation and impact in an institution.

Important dates

all submission dates are 23:59 GMT-12 (AOE Timezone)

Submission deadlines:

• 1 Oct 2019: Deadline for full and short research papers, practitioner reports, and workshop/tutorial proposal submissions
• 14 Oct 2019: Deadline for doctoral consortium submissions
• 1 Nov 2019: Deadline for research and practitioner posters and interactive demo submissions
• 14 Nov 2019: Deadline for full and short research paper rebuttal (submissions open 8 Nov 2019) submissions
• 15 Dec 2019: Deadline for workshop paper submissions (submissions open 1 Nov 2019)
• 20 Dec 2019: Deadline for camera-ready versions of all accepted submissions

Acceptance notifications:

• 21 Oct 2019: Notification of acceptance for workshops and tutorials
• 15 Nov 2019: Notification of acceptance for doctoral consortium
• 1 Dec 2019: Notification of acceptance for full and short research papers, practitioner  reports, posters/demos
• 5 Jan 2020: Notification of acceptance for workshop papers

Conference and registration dates:

• 20 Jan 2020: 21 Jan 2020: Extended for 24 HOURS ONLY! Early-bird registration closes at 11:59pm CET / Frankfurt
• 23-27 Mar 2020: LAK20 conference, Frankfurt, Germany