We welcome submissions from both research and practice, covering different theoretical, methodological, empirical and technical contributions to the learning analytics field. Specifically, this year, we invite contributors to think about how learning analytics is contributing to our understanding of learning and learning processes. Learning research occurs in many distinct academic fields, including psychology, learning sciences, education, neuroscience, and computer science. Since its inception, LA has reflected a tight coupling between research and practice. What has been the impact of the methods, the approaches, the studies, and related outputs of the LA field?
For our 11th Annual conference, we encourage authors to address some of the following questions:
How is LA contributing to our understanding of learning?
What does impact mean in the context of online, blended, and in-classroom learning analytics?
How have learning-related discoveries and research by the LA field influenced learning practices?
What are the practical and scholarly implications of the presented work for the future?
What are the challenges of the presented work we need to address to improve its impact in the future?
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.
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.