Using Log Visualizations to Interpret Online Interactions During Self-Quizzing Practice for Taxonomic Identification

• In Event: Artistic and Other Informal Learning Environments

Abstract

The lack of practice opportunities for citizen science trainees in taxonomic identification for water quality monitoring adds difficulty to an already complex learning task. We created a self-quizzing feature to complement an online visual teaching and learning platform for citizen scientists in aquatic macroinvertebrate identification. Here we present a preliminary study of fifteen learners whose log interactions and performance on pre- and post-study identification tests provide insights to the design of this quizzing feature. We describe our visualization platform for analyzing data and propose design directions for future work.

Authors

• Chelsea Cui, Carnegie Mellon University

• Jordan Hill, Georgia Institute of Technology

• Marti Louw, Carnegie Mellon University

• Jessica Roberts, Georgia Institute of Technology