Educational Data Mining in Computer Science Education (CSEDM) Workshop:

In conjunction with EDM 2018 at The University at Buffalo, New York, United States

July 15th. 2018

The objective of this workshop is to facilitate a discussion among our research community around Artificial Intelligence (AI) in Computer Science Education. The workshop is meant to be an interdisciplinary event. Researchers, faculty and students are encouraged to share their data mining approaches, methodologies and experiences where AI is transforming the way students learn Computer Science (CS) skills.

Call for Papers & Call for Technical Contributions

Check out the workshop, we look forward to your submissions!

Thank you for making CSEDM 2018 a success!

Introduction to the workshop



Peter Brusilovsky: Towards an Infrastructure for Sustainable Innovation and Research in Computer Science Education - SLIDES

Ken Koedinger: Collecting, Analyzing, & Sharing Computer Science Education Data: CTAT, LearnSphere, DataShop - SLIDES

From the SPLICE Project

Accepted papers

  • Measuring Transfer of Data-Driven Code Features Across Tasks in Alice. Nicholas Diana, Michael Eagle, John Stamper, Shuchi Grover, Marie Bienkowski and Satabdi Basu - PAPER, SLIDES
  • Reducing the State Space of Programming Problems through Data-Driven Feature Detection. Rui Zhi, Thomas Price, Nicholas Lytle, Yihuan Dong and Tiffany Barnes - PAPER, SLIDES
  • Application of Neural-Network Models to Labeling Educational Peer Reviews. Zhongcan Xiao, Chandrasekar Rajasekar, Ferry Pramudianto, Edward Gehringer, Vishal Chittoor Venkatasubramanian and Abhinav Medhekar - PAPER, SLIDES
  • Programming Pathway Clustering Using Tree Edit Distance. Bo Jiang, Zhixuan Li and John Stamper - PAPER, SLIDES
  • Using Differential Mining to Explore Bite-Size Problem Solving Practices. Partho Mandal and Sharon Hsiao - PAPER, SLIDES
  • Personalized Self-Assessing Quizzes in Programming Courses. Mohammed Alzaid and Sharon Hsiao - PAPER, SLIDES
  • Predictive Modelling of Student Reviewing Behaviors in an Introductory Programming Course. Yancy Vance Paredes, David Azcona, Alan Smeaton and Sharon Hsiao - PAPER, SLIDES

Accepted technical contributions

  • GATE. Nguyen-Thinh Le
  • QuizIT. Effort Visualization From self-assessment Data in QuizIT System. Mohammed Alzaid, Sharon Hsiao - POSTER
  • MOOC: Big Data in Education. Classifying chaotic student discourse with structured natural language processing, Yiqiao Xu, Dr. Collin F. Lynch
  • OLI (Open Learning Initiative). Steven Moore, John Stamper
  • WebPGA. Utilizing WebPGA as a Platform to Collection Students' Reviewing and Reflecting Behaviors on Paper-Based Assessments. Yancy Vance Paredes, Sharon Hsiao, Yiling Lin, David Azcona - POSTER
  • iSnap. Exploring Worked Example Support in an Open Programming Environment. Rui Zhi, Thomas W. Price
  • PredictCS. Personalizing Programming Learning by Leveraging Learning Analytics. David Azcona, Sharon Hsiao, Alan Smeaton

See the technical contribution submissions here in TABULATED FORMAT

Breakout groups

  1. Integrating multiple streams of data in CSEd. What kind of data? What kind of RQs can we answer? - SLIDES
  2. Data-driven domain and student modeling for CS subjects - SLIDES
  3. CS-Specific EDM - SLIDES


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