EDM-WS 2021
Joint Proceedings of the Workshops at EDM

Table of Contents

• Workshop 1: 5th Educational Data Mining in Computer Science Education (CSEDM) Workshop
• Workshop 2: A Workshop on Process Analysis Methods For Educational Data
• Workshop 3: The Second Workshop of The Learner Data Institute: Big Data, Research Challenges, & Science Convergence in Educational Data Science
• Workshop 4: Workshop for Undergraduates in Educational Data Mining and Learning Engineering

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Workshop 1: 5th Educational Data Mining in Computer Science Education (CSEDM) Workshop

Organizers: Bita Akram (NC State University), Thomas Price (NC State University), Yang Shi (NC State University), Peter Brusilovsky (University of Pittsburgh), Sharon Hsiao (Arizona State University)

• Preface
  

• How productive are homework and elective practice? Applying a post hoc modeling of student knowledge in a large, introductory computing course (Full Paper)
  Max Fowler, Binglin Chen, Matthew West, Craig Zilles
• Investigating Elements of Student Persistence in an Introductory Computer Science Course (Full Paper)
  Juan D. Pinto, Yingbin Zhang, Luc Paquette, Aysa Xuemo Fan
• Next Steps for Next-step Hints: Lessons Learned from Teacher Evaluations of Automatic Programming Hints (Full Paper)
  Benjamin Paaßen, Jessica McBroom, Bryn Jeffries, Irena Koprinska, Kalina Yacef
• Clustering Students' Short Text Reflections: A Software Engineering Course Case Study (Full Paper)
  Mohsen Dorodchi, Alexandria Benedict, Erfan Al-hossami, Andrew Quinn, Sandra Wiktor, Aileen Benedict, Mohammadali Fallahian
• Execution-Trace-Based Feature Engineering To Enable Formative Feedback on Visual, Interactive Programs (Full Paper)
  Wengran Wang, Gordon Fraser, Tiffany Barnes, Chris Martens, Thomas Price
• Dataset on an online collaborative learning situation in a computer networks course (Abstract)
  Cristina Villa-Torrano, Pankaj Chejara, Juan I. Asensio-Pérez, Yannis Dimitriadis, Miguel L. Bote-Lorenzo, Alejandra Martínez-Monés, Eduardo Gómez-Sánchez
• Clickstream Data from a Formal Languages eTextbook (Abstract)
  Mostafa Mohammed, Clifford Shaffer
• JupyterLab Extensions for Blocks Programming, Self-Explanations, and HTML Injection (Abstract)
  Andrew M. Olney, Scott D. Fleming
• Containerizing an eTextbook Infrastructure (Abstract)
  Alexander Hicks, Clifford A. Shaffer
• Comparing Ebook Student Interactions With Test Scores:A Case Study Using CSAwesome (Work in Progress)
  Hisamitsu Maeda, Barbara Ericson, Paramveer Dhillon
• Gaining Insight into Effective Teaching of AI Problem-Solving Through CSEDM: A Case Study (Work in Progress)
  Spencer Yoder, Cansu Tatar, Ifeoluwa Aderemi, Sankalp Boorugu, Shiyan Jiang, Bita Akram
• Investigate Effectiveness of Code Features in Knowledge Tracing Task on Novice Programming Course. (Work in Progress)
  Poorvaja Penmetsa, Yang Shi, Thomas Price
• Prompting for Free Self-Explanations Promotes Better Code Comprehension (Work In Progress)
  Vasile Rus, Kamil Akhuseyinoglu, Jeevan Chapagain, Lasang Tamang, Peter Brusilovsky

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Workshop 2: A Workshop on Process Analysis Methods For Educational Data

Organizers: Nirmal Patel (Playpower Labs ), Derek Lomas (Delft University of Technology), Tirth Shah (Playpower Labs)

• Preface
  

• Exploratory Process Analysis of Teacher Learning of AI Integration through Collaborative Design (Short Paper)
  Xiaoyu Wan, Jingwan Tang, Xiaofei Zhou, Zhen Bai
• LOGANShiny: An app for illustrating process data analysis from international large-scale assessments (Short Paper)
  Denise Reis Costa
• seqClustR: An R Package for Sequence Clustering (Short Paper)
  Aditya Sharma
• Understanding Students’ Problem-Solving Processes via Action Sequence Analyses (Short Paper)
  Ruhan Circi, Manqian Liao, Chad Scott, Juanita Hicks
• Using Markov Transition Matrix to Analyze Parsons Puzzle Solutions (Short Paper)
  Amruth N. Kumar

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Workshop 3: The Second Workshop of The Learner Data Institute: Big Data, Research Challenges, & Science Convergence in Educational Data Science

Organizers: Vasile Rus (University of Memphis), Stephen E. Fancsali (Carnegie Learning)

• Preface
  

• The Learner Data Institute—Conceptualization: A Progress Report
  Vasile Rus, Stephen E. Fancsali, Philip Pavlik Jr., Deepak Venugopal, Arthur C. Graesser, Steve Ritter, Dale Bowman, The LDI Team
• Toward Scalable Improvement of Large Content Portfolios for Adaptive Instruction
  Stephen E. Fancsali, Hao Li, Steven Ritter
• ENGAGE: An API Capable Data Collection and Analysis System for Classroom Behavior
  Susan Elswick, William Hendrick, Laura Casey
• Neuro-Symbolic Models: A Scalable, Explainable Framework for Strategy Discovery from Big Edu-Data
  Deepak Venugopal, Vasile Rus, Anup Shakya
• The Nature of Achievement Goal Motivation Profiles: Exploring Situational Motivation in An Algebra-Focused Intelligent Tutoring System
  Leigh Harrell-Williams, Christian Mueller, Stephen Fancsali, Steven Ritter, Xiaofei Zhang, Deepak Venugopal
• Sentence Selection for Cloze Item Creation: A Standardized Task and Preliminary Results
  Andrew M. Olney

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Workshop 4: Workshop for Undergraduates in Educational Data Mining and Learning Engineering

Organizers: Ryan Baker (University of Pennsylvania), Caitlin Mills (University of New Hampshire), Ulrich Boser (The Learning Agency)

• Preface
  

• Using Course Evaluations and Student Data to Predict Computer Science Student Success
  Anlan Du, Alexandra Plukis, Huzefa Rangwala
• Ensemble Machine Learning System for Student AcademicPerformance Prediction
  Yinkai Wang, Aowei Ding, Kaiyi Guan, Yuanqi Du
• Synthetic Embedding-based Data Generation Methods for Student Performance
  Dom Huh, Huzefa Rangwala
• Promoting Self-regulated Learning in Online Learning by Triggering Tailored Interventions
  HaeJin Lee, Paul Hur, Suma Bhat, Nigel Bosch
• Transferring an existing gaming detection model to different system using semi-supervised approach
  Vedant Bahel, Seth A. Adjei, Ryan S. Baker
• Essay Revision and Corresponding Grade Change asCaptured by Text Similarity and Revision Purposes
  Sonia Cromp, Diane Litman
• A New Interpretation of Knowledge Tracing Models’ Predictive Performance in Terms of the Cold Start Problem
  Rohini Das, Jiayi Zhang, Ryan S. Baker, Richard Scruggs