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Activity Number: 195 - SPEED: Modernization of What, How, and Where We Teach Statistics Part 2
Type: Contributed
Date/Time: Monday, July 29, 2019 : 11:35 AM to 12:20 PM
Sponsor: Section on Statistics and Data Science Education
Abstract #307578
Title: Incorporating Real-Time Clustering of Student Responses into an E-Learning System
Author(s): Philipp Burckhardt* and Christopher Genovese and Rebecca Nugent and Ronald J. Yurko
Companies: Carnegie Mellon University and Statistics, CMU and Carnegie Mellon University and Carnegie Mellon University
Keywords: e-learning; hashing; real-time; clustering; nlp; tool

Classroom response systems (“clickers”) are popular among instructors to promote student engagement and to check students' understanding of class material. Since free-text responses can give a richer picture than multiple-choice answers, we have built functionality into ISLE (Integrated Statistics Learning Environment) that allows one to analyze student answers on-the-fly during lectures or computer labs.

The ISLE response visualizer displays graphs and statistics for the collected data from a question or a chosen subset. For free-text responses, the response visualizer clusters answers and displays a representative answer from each cluster. Because documents arrive in real-time, we rely on an an incremental k-means clustering approach (Lloyd, 1982) using cosine distance and employ the “hashing trick” (Weinberger et al., 2009), whereby words are mapped to indices via a hash function instead of building a dictionary of encountered words.

We discuss insights we have gained on how students from different backgrounds learn introductory statistics and data analysis, and how the system can be used by instructors in the classroom to clarify points in case of student misconceptions.

Authors who are presenting talks have a * after their name.

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