  The flipped classroom is a new pedagogical strategy that has been gaining
increasing importance recently. Spoken discussion dialog commonly occurs in
flipped classroom, which embeds rich information indicating processes and
progression of students' learning. This study focuses on learning analytics
from spoken discussion dialog in the flipped classroom, which aims to collect
and analyze the discussion dialogs in flipped classroom in order to get to know
group learning processes and outcomes. We have recently transformed a course
using the flipped classroom strategy, where students watched video-recorded
lectures at home prior to group-based problem-solving discussions in class. The
in-class group discussions were recorded throughout the semester and then
transcribed manually. After features are extracted from the dialogs by multiple
tools and customized processing techniques, we performed statistical analyses
to explore the indicators that are related to the group learning outcomes from
face-to-face discussion dialogs in the flipped classroom. Then, machine
learning algorithms are applied to the indicators in order to predict the group
learning outcome as High, Mid or Low. The best prediction accuracy reaches
78.9%, which demonstrates the feasibility of achieving automatic learning
outcome prediction from group discussion dialog in flipped classroom.
