What's an Expert? Using Learning Analytics to Identify Emergent Markers of Expertise through Automated Speech, Sentiment and Sketch Analysis

Assessing student learning across a variety of environments and tasks continues to be a crucial educational concern. This task is of particular difficulty in non-traditional learning environments where students endeavor to design their own projects and engage in a hands-on educational experience. In order to improve our ability to recognize learning in these constructionist environments, this paper reports on an exploratory analysis of learning through multiple modalities: speech, sentiment and drawing. A rich set of features is automatically extracted from the data and used to identify emergent markers of expertise. Some of the most prominent markers of expertise include: user certainty, the ability to describe things efficiently and a disinclination to use unnecessary descriptors or qualifiers. Experts also displayed better organization and used less detail in their drawings. While many of these are things one would expect of an expert, there were areas in which experts looked very similar to novices. To explain this we report on learning theories that can reconcile these seemingly odd findings, and expound on how these domain-independent markers can be useful for identifying student learning over a series of activities.