This pilot study investigated the use of a pedagogical agent to administer a conversational survey to second graders following a digital reading activity, measuring comprehension, persistence, and enjoyment. Analysis of survey responses and behavioral log data provide evidence for recommendations for the design of agent-mediated assessment in early literacy.

Teaching simulations with feedback are one way to provide teachers with practice opportunities to help improve their skill. We investigated methods to build evaluation models of teacher performance in leading a discussion in a simulated classroom, particularly for tasks with little performance data.

This research is situated in the space between an existing NLP capability and its use(s) in an educational context. We analyze oral reading data collected with a deployed automated speech analysis software and consider how the results of automated speech analysis can be interpreted and used to inform the ideation and design of a new feature – feedback to learners and teachers. Our analysis shows how the details of the system’s performance and the details of the context of use both significantly impact the ideation process.

We present research aimed at solving a problem in assessment of oral reading fluency using children’s oral reading data from our online book reading app. It is known that properties of the passage being read aloud impact fluency estimates; therefore, passage-based measures are used to remove passage-related variance when estimating growth in oral reading fluency. However, passage-based measures reported in the literature tend to treat passages as independent events, without explicitly modeling accumulation of lexical experience as one reads through a book. We propose such a model and show that it helps explain additional variance in the measurements of children’s fluency as they read through a book, improving over a strong baseline. These results have implications for measuring growth in oral reading fluency.