Visual Analytics and Storytelling of Data from Massive Open Online Courses

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering

PhD Thesis Defence

Title: "Visual Analytics and Storytelling of Data from Massive Open 
Online Courses"


Miss Qing CHEN


Since 2012, Massive Open Online Courses (MOOCs) have attracted millions of 
learners to learn and communicate at an unprecedented scale. MOOC data 
contains not only learner profile and learning outcome information but 
also the web log records of learner interactions with various course 
materials. Such large amounts of heterogeneous and multivariate data 
provide great opportunities for analyzing online learning behaviors while 
at the same time posing new challenges. Visual analytics and storytelling 
turns out to be an effective solution to help instructors and education 
experts better discover how students learn, understand the reasons behind 
various learning behaviors, and present learning analytics stories.

In this thesis, we introduce three visualization systems to facilitate 
instructors and education experts in understanding, exploring, analyzing, 
gaining and sharing insights from MOOC data. The first work, PeakVizor, is 
a comprehensive visualization system which integrates well-established 
visualization techniques and several novel visual designs to investigate 
clickstream peaks. The second system, ViSeq, focuses on the visual 
analytics of learning sequences of different learner groups. The four 
linked views facilitate users in exploring learning sequences from 
multiple levels of granularity. In the last work, we propose a narrative 
visualization approach with an interactive slideshow that helps 
instructors and education experts explore potential learning patterns and 
convey data stories. This approach contains three key components: the 
guided-tour concept, the drill-down path, and the dig-in exploration 
dimension. Case studies and interviews conducted with domain experts have 
demonstrated the usefulness and effectiveness of the three systems.

Date:			Tuesday, 21 August 2018

Time:			10:00am - 12:00noon

Venue:			Room 5501
 			Lifts 25/26

Chairman:		Prof. Kam-Tim Tse (CIVL)

Committee Members:	Prof. Huamin Qu (Supervisor)
 			Prof. Xiaojuan Ma
 			Prof. Chiew-Lan Tai
 			Prof. Bertram Shi (ECE)
 			Prof. Remco Chang (COMP, Tufts University)

**** ALL are Welcome ****