Resources for:
  • Current Students
  • Faculty & Staff
  • Alumni
  • Directory
  • News
  • Events
UCI School of Education
  • About Us
    • Dean's Welcome
    • Our Mission & Vision
    • Facts & Information
    • Climate Council
    • Maps & Directions
  • Academics
    • Ph.D. in Education
    • MAT + Credential
    • Undergraduate
  • Community Engagement
    • Overview
    • Teacher Academy >
      • California Reading & Literature Project
      • UCI CalTeach
      • UCI History Project
      • UCI Math Project
      • UCI Science Project
      • UCI Writing Project
    • Orange County Educational Advancement Network
    • Center for Educational Partnerships >
      • SAGE Scholars Program
      • COSMOS
      • California Alliance for Minority Participation
    • Center for Research on Teacher Development and Professional Practice
  • Faculty
    • Our Faculty
    • Faculty Interviews
    • Centers
    • publications
  • Giving

Poster: "Representing and Predicting Student Navigational Pathways in Online College Courses"

9/21/2018

 
Event: 2018 School of Education Research Poster Celebration
Date: Friday, September 28, 2018
Time: 3:30-5:00 pm
Location: School of Education Courtyard
 
Presenter: Renzhe Yu
Poster Title: "Representing and Predicting Student Navigational Pathways in Online College Courses"
Poster Advisor: Mark Warschauer

Abstract

Understanding and predicting how students navigate through course space is crucial to improving instruction yet challenging in educational research. Building on prior research on MOOCs, this study investigates students' navigational pathways by fitting neural network models on clickstream data of an online college course for residential students. We first learnt vector representations of resource pages from students' visiting sequences. Comparing their locations in the space to pre-designed course structure, we found that students who got different final grades exhibited different levels of adherence to the designed sequence. Next, we used a neural network architecture to predict the next page that a student visits given her prior sequence of visits. The highest accuracy reached 50.8% and largely outperformed the frequency-based baseline of 41.3%. These results show that neural network methods have the potential to help instructors understand students' learning behaviors and facilitate automated instructional support.

Picture

Comments are closed.
Quick Links:

Fall 2021 Magazine
​Faculty & Research
Faculty Interviews
Directory
Admissions
​Giving
​News Center
Employment
Programs:
​
PhD in Education
MAT
Major in Edu Science
Minor in Edu Studies
CalTeach
CASE
Resources for:
​

​Current Students​
Faculty & Staff
University of California, Irvine
School of Education
401 E. Peltason Drive
Suite 3200
Irvine, CA  92617
(949) 824-8073

Picture
© ​2022 UC Regents 
  • About Us
    • Dean's Welcome
    • Our Mission & Vision
    • Facts & Information
    • Climate Council
    • Maps & Directions
  • Academics
    • Ph.D. in Education
    • MAT + Credential
    • Undergraduate
  • Community Engagement
    • Overview
    • Teacher Academy >
      • California Reading & Literature Project
      • UCI CalTeach
      • UCI History Project
      • UCI Math Project
      • UCI Science Project
      • UCI Writing Project
    • Orange County Educational Advancement Network
    • Center for Educational Partnerships >
      • SAGE Scholars Program
      • COSMOS
      • California Alliance for Minority Participation
    • Center for Research on Teacher Development and Professional Practice
  • Faculty
    • Our Faculty
    • Faculty Interviews
    • Centers
    • publications
  • Giving
  • Resources For:
  • Future Students
  • Current Students
  • Faculty & Staff

  • Search This Site
  • Directory
  • News
  • Events