Learning Analytics from Spoken Discussion Dialogs in Flipped Classroom

TL;DR

This paper explores learning analytics from spoken discussion dialogs in flipped classrooms, using feature extraction and machine learning to predict group learning outcomes with high accuracy.

Contribution

It introduces a method for analyzing spoken discussion dialogs in flipped classrooms and demonstrates effective prediction of learning outcomes using machine learning.

Findings

Prediction accuracy of 78.9% for group learning outcomes.

Features extracted from dialogs can indicate learning processes.

Machine learning models are feasible for automatic outcome prediction.

Abstract

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.