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15th EDM 2022: Durham, UK
- Antonija Mitrovic, Nigel Bosch, Alexandra I. Cristea, Chris Brown:
Proceedings of the 15th International Conference on Educational Data Mining, EDM 2022, Durham, UK, July 24-27, 2022. International Educational Data Mining Society 2022
Full Papers
- Qinjin Jia, Mitchell Young, Yunkai Xiao, Jialin Cui, Chengyuan Liu, M. Parvez Rashid, Edward F. Gehringer:
Insta-Reviewer: A Data-Driven Approach for Generating Instant Feedback on Students' Project Reports. - Benjamin Paaßen, Malwina Dywel, Melanie Fleckenstein, Niels Pinkwart:
Sparse Factor Autoencoders for Item Response Theory. - Ethan Prihar, Manaal Syed, Korinn Ostrow, Stacy T. Shaw, Adam Sales, Neil T. Heffernan:
Exploring Common Trends in Online Educational Experiments. - Aqil Zainal Azhar, Avi Segal, Kobi Gal:
Optimizing Representations and Policies for Question Sequencing using Reinforcement Learning. - Yang Shi, Min Chi, Tiffany Barnes, Thomas W. Price:
Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks. - Tristan Maidment, Mingzhi Yu, Nikki G. Lobczowski, Adriana Kovashka, Erin Walker, Diane J. Litman, Timothy Nokes-Malach:
Building a Reinforcement Learning Environment from Limited Data to Optimize Teachable Robot Interventions. - Jiayi Zhang, Juliana Ma. Alexandra L. Andres, Stephen Hutt, Ryan S. Baker, Jaclyn Ocumpaugh, Caitlin Mills, Jamiella Brooks, Sheela Sethuraman, Tyron Young:
Detecting SMART Model Cognitive Operations in Mathematical Problem-Solving Process. - Jia Xu, Tingting Wei, Pin Lv:
SQL-DP: A Novel Difficulty Prediction Framework for SQL Programming Problems. - Vinitra Swamy, Bahar Radmehr, Natasa Krco, Mirko Marras, Tanja Käser:
Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs. - Vinthuy Phan, Laura Wright, Bridgette Decent:
Addressing Competing Objectives in Allocating Funds to Scholarships and Need-based Financial Aid. - Mengxue Zhang, Sami Baral, Neil T. Heffernan, Andrew S. Lan:
Automatic Short Math Answer Grading via In-context Meta-learning. - Yingbo Ma, Gloria Ashiya Katuka, Mehmet Celepkolu, Kristy Elizabeth Boyer:
Investigating Multimodal Predictors of Peer Satisfaction for Collaborative Coding in Middle School. - Megan Caruso, Candace E. Peacock, Rosy Southwell, Guojing Zhou, Sidney D'Mello:
Going Deep and Far: Gaze-based Models Predict Multiple Depths of Comprehension During and One Week Following Reading. - Yuyang Nie, Helene Deacon, Alona Fyshe, Carrie Demmans Epp:
Predicting Reading Comprehension Scores of Elementary School Students. - Nathan L. Henderson, Halim Acosta, Wookhee Min, Bradford W. Mott, Trudi Lord, Frieda Reichsman, Chad Dorsey, Eric N. Wiebe, James C. Lester:
Enhancing Stealth Assessment in Game-Based Learning Environments with Generative Zero-Shot Learning. - Jade Maï Cock, Mirko Marras, Christian Giang, Tanja Käser:
Generalisable Methods for Early Prediction in Interactive Simulations for Education. - Wei Qiu, S. Supraja, Andy W. H. Khong:
Toward Better Grade Prediction via A2GP - An Academic Achievement Inspired Predictive Model. - Afrizal Doewes, Akrati Saxena, Yulong Pei, Mykola Pechenizkiy:
Individual Fairness Evaluation for Automated Essay Scoring System. - Gio Picones, Benjamin Paaßen, Irena Koprinska, Kalina Yacef:
Combining domain modelling and student modelling techniques in a single automated pipeline. - Zhiqiang Cai, Cody Marquart, David W. Shaffer:
Neural Recall Network: A Neural Network Solution to Low Recall Problem in Regex-based Qualitative Coding. - Nathan Ong, Jiaye Zhu, Daniel Mossé:
Towards Including Instructor Features in Student Grade Prediction. - Yun Huang, Steven Dang, J. Elizabeth Richey, Michael W. Asher, Nikki G. Lobczowski, Danielle R. Chine, Elizabeth A. McLaughlin, Judith M. Harackiewicz, Vincent Aleven, Kenneth R. Koedinger:
Item Response Theory-Based Gaming Detection. - Mohammad Amin Samadi, Jacqueline G. Cavazos, Yiwen Lin, Nia Nixon:
Exploring Cultural Diversity and Collaborative Team Communication through a Dynamical Systems Lens. - Guher Gorgun, Seyma Nur Yildirim-Erbasli, Carrie Demmans Epp:
Predicting Cognitive Engagement in Online Course Discussion Forums. - Guojing Zhou, Robert Moulder, Chen Sun, Sidney D'Mello:
Investigating Temporal Dynamics Underlying Successful Collaborative Problem Solving Behaviors with Multilevel Vector Autoregression. - Rosy Southwell, Samuel L. Pugh, E. Margaret Perkoff, Charis Clevenger, Jeffrey B. Bush, Rachel Lieber, Wayne H. Ward, Peter W. Foltz, Sidney D'Mello:
Challenges and Feasibility of Automatic Speech Recognition for Modeling Student Collaborative Discourse in Classrooms.
Short Papers
- Jeffrey Matayoshi, Eric Cosyn, Hasan Uzun:
Does Practice Make Perfect? Analyzing the Relationship Between Higher Mastery and Forgetting in an Adaptive Learning System. - Alireza A. Namanloo, Julie Thorpe, Amirali Salehi-Abari:
Improving Peer Assessment with Graph Neural Networks. - Shamya Karumbaiah, Jiayi Zhang, Ryan Baker, Richard Scruggs, Whitney L. Cade, Margaret Clements, Shuqiong Lin:
Using Neural Network-Based Knowledge Tracing for a Learning System with Unreliable Skill Tags. - Ariel Blobstein, Kobi Gal, David R. Karger, Marc T. Facciotti, Hyunsoo Gloria Kim, Jumana Almahmoud, Kamali Sripathi:
#lets-discuss: Analyzing Student Affect in Course Forums Using Emoji. - Jihyun Rho, Martina A. Rau, Barry Vanveen:
Investigating Growth of Representational Competencies by Knowledge-Component Model. - Yang Zhi-Han, Shiyue Zhang, Anna N. Rafferty:
Adversarial bandits for drawing generalizable conclusions in non-adversarial experiments: an empirical study. - Lea Cohausz:
Towards Real Interpretability of Student Success Prediction Combining Methods of XAI and Social Science. - Benedikt Fein, Isabella Graßl, Florian Beck, Gordon Fraser:
An Evaluation of code2vec Embeddings for Scratch. - Hagit Gabbay, Anat Cohen:
Investigating the effect of Automated Feedback on learning behavior in MOOCs for programming. - Julian Langenhagen:
Data-driven goal setting: Searching optimal badges in the decision forest. - Chengyuan Liu, Jialin Cui, Ruixuan Shang, Yunkai Xiao, Qinjin Jia, Edward F. Gehringer:
Improving problem detection in peer assessment through pseudo-labeling using semi-supervised learning. - Nathan Levin, Ryan Baker, Nidhi Nasiar, Stephen Fancsali, Stephen Hutt:
Evaluating Gaming Detector Model Robustness Over Time. - Yiqiu Zhou, Jina Kang:
Characterizing joint attention dynamics during collaborative problem-solving in an immersive astronomy simulation. - Sahan Bulathwela, Meghana Verma, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor:
Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments. - Maximilian Jahnke, Frank Höppner:
Is there Method in Your Mistakes? Capturing Error Contexts by Graph Mining for Targeted Feedback. - Juan Camilo Sanguino, Rubén Manrique, Olga Mariño, Mario Linares, Nicolás Cardozo:
Log mining for course recommendation in limited information scenarios. - Paul Hur, Haejin Lee, Suma Bhat, Nigel Bosch:
Using Machine Learning Explainability Methods to Personalize Interventions for Students. - Jiawei Li, S. Supraja, Wei Qiu, Andy W. H. Khong:
Grade Prediction via Prior Grades and Text Mining on Course Descriptions: Course Outlines and Intended Learning Outcomes. - Adish Singla, Nikitas Theodoropoulos:
From Solution Synthesis to Student Attempt Synthesis for Block-Based Visual Programming Tasks. - Yuancheng Wang, Nanyu Luo, Jianjun Zhou:
Mining Assignment Submission Time to Detect At-Risk Students with Peer Information. - Frederik Baucks, Laurenz Wiskott:
Simulating Policy Changes in Prerequisite-Free Curricula: A Supervised Data-Driven Approach. - Renu Balyan, Tracy Arner, Karen Taylor, Jinnie Shin, Michelle P. Banawan, Walter L. Leite, Danielle S. McNamara:
Modeling One-on-one Online Tutoring Discourse using an Accountable Talk Framework. - Katerina Christhilf, Natalie Newton, Reese Butterfuss, Kathryn S. McCarthy, Laura K. Allen, Joseph P. Magliano, Danielle S. McNamara:
Using Markov Models and Random Walks to Examine Strategy Use of More or Less Successful Comprehenders. - Yo Ehara:
No Meaning Left Unlearned: Predicting Learners' Knowledge of Atypical Meanings of Words from Vocabulary Tests for Their Typical Meanings. - Jillian Christine Johnson, Andrew Olney:
Using community-based problems to increase motivation in a data science virtual internship. - Zhikai Gao, Bradley Erickson, Yiqiao Xu, Collin F. Lynch, Sarah Heckman, Tiffany Barnes:
Admitting you have a problem is the first step: Modeling when and why students seek help in programming assignments. - M. Parvez Rashid, Yunkai Xiao, Edward F. Gehringer:
Going beyond "Good Job": Analyzing Helpful Feedback from the Student's Perspective. - Anaïs Tack, Chris Piech:
The AI Teacher Test: Measuring the Pedagogical Ability of Blender and GPT-3 in Educational Dialogues. - Yuheng Li, Mladen Rakovic, Boon Xin Poh, Dragan Gasevic, Guanliang Chen:
Automatic Classification of Learning Objectives Based on Bloom's Taxonomy.
Posters
- Alireza Ahadi, Kirsty Kitto, Marian-Andrei Rizoiu, Katarzyna Musial:
Skills Taught vs Skills Sought: Using Skills Analytics to Identify the Gaps between Curriculum and Job Markets. - Emiko Tsutsumi, Yiming Guo, Maomi Ueno:
DeepIRT with a Hypernetwork to Optimize the Degree of Forgetting of Past Data. - Lan Jiang, Nigel Bosch:
Mining and Assessing Anomalies in Students' Online Learning Activities with Self-supervised Machine Learning. - Benjamin Paaßen, Christina Göpfert, Niels Pinkwart:
Faster Confidence Intervals for Item Response Theory via an Approximate Likelihood Profile. - Armando M. Toda, Ana C. T. Klock, Filipe Dwan Pereira, Luiz A. L. Rodrigues, Paula Toledo Palomino, Vinícius Lopes, Craig D. Stewart, Elaine H. T. Oliveira, Isabela Gasparini, Seiji Isotani, Alexandra I. Cristea:
Towards the understanding of cultural differences in between gamification preferences: A data-driven comparison between the US and Brazil. - Annika Lindh, Keith Quille, Aidan Mooney, Kevin Marshall, Katriona O'Sullivan:
Supervised Machine Learning for Modelling STEM Career and Education Interest in Irish School Children. - Niall Twomey, Sarah McMullan, Anat Elhalal, Rafael Poyiadzi, Luis M. Vaquero:
Equitable Ability Estimation in Neurodivergent Student Populations with Zero-Inflated Learner Models. - Sebastian Tschiatschek, Maria Knobelsdorf, Adish Singla:
Equity and Fairness of Bayesian Knowledge Tracing. - Vitória Guardieiro, Marcos M. Raimundo, Jorge Poco:
Analyzing the Equity of the Brazilian National High School Exam by Validating the Item Response Theory's Invariance. - Mariah Bradford, Paige Hansen, J. Ross Beveridge, Nikhil Krishnaswamy, Nathaniel Blanchard:
A deep dive into microphone hardware for recording collaborative group work. - Carol Forsyth, Jesse R. Sparks, Jonathan Steinberg, Laura McCulla:
Linguistic Profiles of Students Interacting with Conversation-Based Assessment Systems. - Yueqi Wang, Zachary A. Pardos:
Does chronology matter? Sequential vs contextual approaches to knowledge tracing. - Frank Stinar, Nigel Bosch:
Algorithmic unfairness mitigation in student models: When fairer methods lead to unintended results. - Gary Weiss, Joseph Denham, Daniel D. Leeds:
The Impact of Semester Gaps on Student Grades. - Gary Weiss, Erik Brown, Michael Riad-Zaky, Ruby Iannone, Daniel D. Leeds:
Assessing Instructor Effectiveness Based on Future Student Performance. - Niels Seidel:
Modeling study duration considering course enrollments and student diversity. - Daniel D. Leeds, Cody Chen, Yijun Zhao, Fiza Metla, James Guest, Gary Weiss:
Generalized Sequential Pattern Mining of Undergraduate Courses. - Bin Tan, Maria Cutumisu:
Employing Tree-based Algorithms to Predict Students' Self-Efficacy in PISA 2018. - Zitong Zhao, Pan Deng, Jianjun Zhou:
Identifying Longitudinal Attendance Patterns through Student Subpopulation Distribution Comparison. - Jih Soong Tan, Ian K. T. Tan, Lay-Ki Soon, Huey Fang Ong:
Improved Automated Essay Scoring using Gaussian Multi-Class SMOTE for Dataset Sampling. - Ryan Hodgson, Jingyun Wang, Alexandra I. Cristea, Fumiko Matsuzaki, Hiroyuki Kubota:
A Topic-Centric Crowdsourced Assisted Biomedical Literature Review Framework for Academics. - Kerstin Wagner, Agathe Merceron, Petra Sauer, Niels Pinkwart:
Personalized and Explainable Course Recommendations for Students at Risk of Dropping out. - Aubrey Condor, Zachary A. Pardos:
A deep reinforcement learning approach to automatic formative feedback. - Rabin Banjade, Priti Oli, Lasang Jimba Tamang, Vasile Rus:
Preliminary Experiments with Transformer based Approaches To Automatically Inferring Domain Models from Textbooks. - Jinnie Shin, Okan Bulut, Wallace N. Pinto Jr.:
E-learning Preparedness: A Key Consideration to Promote Fair Learning Analytics Development in Higher Education. - Raysa Rivera-Bergollo, Sami Baral, Anthony Botelho, Neil T. Heffernan:
Leveraging Auxiliary Data from Similar Problems to Improve Automatic Open Response Scoring. - Qiao Zhang, Zeyu Chen, Natasha Lalwani, Christopher MacLellan:
Modifying Deep Knowledge Tracing for Multi-step Problems. - Huanyi Chen, Paul A. S. Ward:
Clustering Students Using Pre-Midterm Behaviour Data and Predict Their Exam Performance. - Boxuan Ma, Gayan Prasad Hettiarachchi, Yuji Ando:
Format-Aware Item Response Theory for Predicting Vocabulary Proficiency. - Shravya Bhat, Huy Anh Nguyen, Steven Moore, John C. Stamper, Majd Sakr, Eric Nyberg:
Towards Automated Generation and Evaluation of Questions in Educational Domains. - Peide Zhu, Claudia Hauff, Jie Yang:
MOOC-Rec: Instructional Video Clip Recommendation for MOOC Forum Questions. - Ting Long, Yunfei Liu, Weinan Zhang, Wei Xia, Zhicheng He, Ruiming Tang, Yong Yu:
Automatical Graph-based Knowledge Tracing. - Alexander Scarlatos, Christopher Brinton, Andrew S. Lan:
Process-BERT: A Framework for Representation Learning on Educational Process Data. - Luyao Peng, Chengzhi Wei:
Online Item Response Theory (OIRT) - Tracking Student Abilities in Online Learning System. - Cristóbal Romero, Wilson Chango, Rebeca Cerezo:
Looking for the best data fusion model in Smart Learning Environments for detecting at risk university students. - Marie-Luce Bourguet, Yushan Li:
Distance measure between instructor-recommended and learner's learning pathways. - Aaron Haim, Neil T. Heffernan:
Student Perception on the Effectiveness of On-Demand Assistance in Online Learning Platforms. - Zilong Pan, Min Liu:
Theory-Informed Problem-Solving Sequential Pattern Visualization. - Ralucca Gera, D'Marie Bartolf, Simona Tick, Akrati Saxena:
CHUNK Learning: A Tool that Supports Personalized Education. - Michael Smalenberger, Kelly Smalenberger:
Do College Students Learn Math the Same Way as Middle School Students? On the Transferability of Findings on Within-Problem Supports in Intelligent Tutoring Systems. - Hiroaki Kawashima:
Comparison of Learning Behaviors on an e-Book System in 2019 Onsite and 2020 Online Courses. - Juyeong Song, Kisu Yang, Hyeji Jang, Hyo-Jeong So:
Recommendation System of Mobile Language Learning Applications: Similarity versus Diversity in Learner Preference. - Meng Cao, Philip I. Pavlik:
A Variant of Performance Factors Analysis Model for Categorization. - Yo Ehara:
Selecting Reading Texts Suitable for Incidental Vocabulary Learning by Considering the Estimated Distribution of Acquired Vocabulary.
Doctoral Consortium
- Ethan Prihar, Alexander Moore, Neil T. Heffernan:
Identifying Explanations Within Student-Tutor Chat Logs. - Marcus Messer:
Detecting When a Learner Requires Assistance with Programming and Delivering a Useful Hint. - Arwa Al Saqaabi, Craig D. Stewart, Eleni C. Akrida, Alexandra I. Cristea:
A Paraphrase Identification Approach in Paragraph length texts. - Daevesh Kumar Singh, Ramkumar Rajendran:
Investigating learners' Cognitive Engagement in Python Programming using ICAP framework. - Sami Baral:
Improving Automated Assessment and Feedback for Student Open-responses in Mathematics. - Minghao Cai, Carrie Demmans Epp:
Modeling Cognitive Load and Affect to Support Adaptive Online Learning. - Jonathan Young, Sue Black, Alexandra I. Cristea, Ryan Hodgson, Cristina Todor:
Using AI, ML and Sentiment Analysis to Increase Diversity and Equity in Technology Training and Careers. - Mahbubul Hasan, Lih-Yuan Deng, John Sabatini, Dale Bowman, Ching-Chi Yang, John Hollander:
Effect of Q-matrix Misspecification on Variational Autoencoders (VAE) for Multidimensional Item Response Theory (MIRT) Models Estimation. - Miguel Portaz, Olga C. Santos:
Towards Personalised Learning of Psychomotor Skills with Data Mining.
Industry Track
- Jeffrey Matayoshi, Hasan Uzun, Eric Cosyn:
Using a Randomized Experiment to Compare the Performance of Two Adaptive Assessment Engines. - Robby Robson, Benjamin Goldberg, Shelly Blake-Plock, Cliff Casey, William Hoyt, Mike Hernandez, Fritz Ray:
Mining Artificially Generated Data to Estimate Competency. - Stephen Fancsali, April Murphy, Steven Ritter:
"Closing the Loop" in Educational Data Science with an Open Source Architecture for Large-Scale Field Trials. - Phillip Grimaldi, Kodi Weatherholtz, Kelli Millwood Hill:
Estimating the causal effects of Khan Academy Map Accelerator across demographic subgroups.
Workshop/Tutorial Abstracts
- Vasile Rus, Stephen Fancsali:
The Third Workshop of the Learner Data Institute: Big Data, Research Challenges, \& Science Convergence in Educational Data Science. - Collin F. Lynch, Mirko Marras, Mykola Pechenizkiy, Anna N. Rafferty, Steven Ritter, Vinitra Swamy, Renzhe Yu:
FATED 2022: Fairness, Accountability, and Transparency in Educational Data. - Bita Akram, Thomas W. Price, Yang Shi, Peter Brusilovsky, Sharon I-Han Hsiao:
6th Educational Data Mining in Computer Science Education (CSEDM) Workshop. - Juanita Hicks, Ruhan Circi, Burhan Ogut, Michelle Yin, Darrick Yee:
Rethinking Accessibility: Applications in Educational Data Mining (Canceled). - Adam Sales, Neil T. Heffernan:
Causal Inference in Educational Data Mining. - Stacy T. Shaw, Adam Sales:
Using the Open Science Framework to promote Open Science in Education Research.
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