7th International Conference on Learning Analytics & Knowledge LAK 2017

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活动简介

The 7th International Conference on Learning Analytics and Knowledge (LAK17) returns to the coastal city of Vancouver, BC, Canada from March 13-17, 2017. The LAK17 conference is organised by the Society for Learning Analytics Research (SOLAR) and will be hosted by Simon Fraser University (SFU), Canada's leading community-engaged research university. Consolidating the experience from previous LAK conferences, we extend invitations to researchers, practitioners, administrators, government and industry professionals interested in the field of learning analytics and related disciplines. This annual conference provides a forum to address critical issues and challenges confronting the education sector today. The success of LAK arises from its transdisciplinary nature which creates a unique intersection of cutting-edge learning technologies, educational research and practice, and data science.

Learning Analytics is defined more by its goals to better understand and improve learning processes using data than by a particular theory or methods. The challenges facing learning analytics require collaborations among a broad range of disciplinary experts to create holistic solutions generated from differing perspectives and approaches. This year LAK17 aims to bring more discussion and focus to these transdisciplinary efforts.

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重要日期

2016-10-17

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The following categories represent the objectives of Learning Analytics research and practice and will be used to classify submissions:

Tracing Learning:

Feature Finding: Studies that identify and explain useful data features for analyzing understanding and optimizing learning.

Learning Metrics: Studies that assess the learning progress through the computation and analysis of learner actions or artefacts.

Data Storage and Sharing: Proposals of technical and methodological procedures to store, share and preserve learning traces.

Understanding Learning:

Data-Informed Learning Theories: Proposal of new learning theories or revisions / reinterpretations of existing theories based on large-scale data analysis.

Insights into Particular Learning Processes: Studies to understand particular aspects of a learning process based on data analysis. Examples are inquiries that analyse traces of students’ cognition, metacognition, affect or motivation using various forms of data.

Modeling: Creating mathematical, statistical or computational models of a learning process, including its actors and context.

Improving Learning:

Feedback and Decision-Support Systems: Studies that evaluate the impact of feedback or decision-support systems based on learning analytics (dashboards, early-alert systems, automated messages, etc.).

Data-Informed Efforts: Empirical evidence about the effectiveness of learning analytics implementations or educational initiatives guided by learning analytics.

Personalized and Adaptive Learning: Studies that evaluate the effectiveness and impact of (semi-)automatic adaptive technologies based on learning analytics.

Meta-Issues:

Ethics and Law: Exploration of issues and approaches to the lawful and ethical capture and use of educational data traces and the application of learning analytics tools and implementations