Learning analytics (LA) researchers often fail to collect the necessary data to answer research questions due to a lack of understanding of what types of data they need. This workshop aims to advance understanding of the necessities of iterative processes in data collection and analysis to address the issue.

The workshop includes panel talks by the organizers, who have experience in data collection tool design, and discussion to sketch out designs of potential Evidence-Based Iterative (EBI) processes. which can help LA community update the design of the data collection system in consideration of research contexts and findings from previous iterations.

Organisers

• Heeryung Choi, University of Michigan, USA
• Christopher Brooks, University of Michigan, USA
• Caitlin Hayward, University of Michigan, USA
• Neil Heffernan, Worcester Polytechnic Institute, USA
• Dragan Gasevic, Monash University, Australia
• Kirsty Kitto, University of Technology Sydney, Australia
• Abelardo Pardo, University of South Australia, Australia
• Phil Winne, Simon Fraser University, Canada