This issue features seven articles that will be presented at
the 17th International Conference on
Educational Data Mining (EDM 2024) in the JEDM Journal Track at EDM 2024 that will take place
in Atlanta, USA, July 14-17, 2024. This issue features as well three regular
articles and three articles selected by the program chairs of the 16th International Conference on
Educational Data Mining (EDM 2023); these articles are extended versions of papers presented
at EDM 2022, and that could not be published in the corresponding special
section of the December Issue of JEDM in 2023.
For the tenth time, EDM 2024 will hold a Journal track
allowing papers submitted to JEDM to be presented at the conference. The JEDM
Track at EDM 2024 received 17 submissions, among which seven made it to the
final stage in time for publication in this issue. We are pleased to publish
the following works.
Yang Shi, Robin Schmucker,
Keith Tran, John Bacher, Kenneth
Koedinger, Thomas Price, Min Chi, and Tiffany Barnes address the knowledge
component (KC) attribution problem among computer programming students.
This refers to the difficulty in attributing student errors to specific KCs, as
programming requires students to practice several KCs at the same time. Their
supervised code2vec model trained on expert-defined KCs achieved AUCs of 75% or
higher. Subsequent experiments showed how the inclusion of submission
correctness and learning curves into the model can help with KC attribution.
The paper Automated
Evaluation of Classroom Instructional Support with LLMs and BoWs: Connecting
Global Predictions to Specific Feedback by Jacob Whitehill and Jennifer
LoCasale-Crouch compares the use of Large Language Models (LLMs) and Bag of
Words (BoW) models to analyze the presence of Instructional Support in
teachers’ speech and estimate a global CLassroom Assessment Scoring System
(CLASS) score. One finding is that “LLMs generally yield slightly greater
accuracy than BoW, though the best models often combined features extracted
from both LLM and BoW”.
In the paper, An
Approach to Improve k-Anonymization Practices in Educational Data Mining Frank Stinar, Zihan Xiong, and Nigel Bosch propose a framework to
k-anonymize students' data while preserving the utility of the dataset for data
mining tasks. Their evaluation shows that the downstream machine learning
accuracy is improved by 30.59% when using their framework over baseline data
anonymization.
The paper Exploring
the Impact of Symbol Spacing and Problem Sequencing on Arithmetic
Performance:
An Educational Data Mining Approach
by Avery Harrison Closser, Anthony F.
Botelho and Jenny Yun-Chen Chan revisit
an earlier study on manipulating the spacing between symbols in arithmetic
expressions and its effect on students’ performance. They find that problem composition and
problem order may have caused unintended effects in the earlier study and draw
methodological implications for educational researchers.
Kirk
Vanacore, Ashish Gurung, Adam Sales,
and Neil Heffernan build on previous
research to test various ways of proactively discouraging students from gaming
the system. In this study they compare the effects delaying hint access to the effects of gamification (not to be
confused with gaming the system). Specifically, they combine established
methods in a novel way, using a fully latent principal stratification for
causal inference to examine detectors of gaming the system.
John
Stamper, Steven Moore, Carolyn P.
Rose, Philip I. Pavlik, Jr., and
Kenneth Koedinger present LearnSphere, a web-based data
infrastructure that integrates previously siloed educational data and analytic
resources, one of which is DataShop. In an atmosphere that is increasingly
concerned about AI ethics, LearnSphere’s structure and in so doing shows how
this tool contributes to model transparency and explainability by enabling
reproducibility and replicability of experiments.
Md
Akib Zabed Khan and Agoritsa Polyzou discuss a recommendation system that helps
students manage their academic load. The system suggests courses from the
upcoming semester that are best suited to be taken together. The authors use
information from past students about co-taken courses as basis for establishing
the relationship between courses and their compatibility. The authors found
that this session-based method for arriving at recommendations outperforms
existing approaches based on course popularity, association, similarity, and
others.
Maria Mercedes T. Rodrigo (Chair
JEDM@EDM2024), Ateneo de Manila University, PHILIPPINES
Agathe Merceron (Chair
JEDM@EDM2024), Berlin University of Applied Sciences, GERMANY
Jaclyn Ocumpaugh (Chair
JEDM@EDM2024), University of Pennsylvania, USA
The Editor and Associate Editors would like to warmly thank
the editorial board and colleagues who kindly served as reviewers in 2023.
Their professionalism and support are much appreciated.
Tiffany Barnes, North Carolina State
University, USA
François Bouchet, Université Pierre et Marie Curie, FRANCE
Michel Desmarais, Polytechnique Montréal, CANADA
Stephen E. Fancsali, Carnegie Learning, Inc., USA
Dragan Gasevic, Monash University, AUSTRALIA
Neil Heffernan, Worcester Polytechnic Institute, USA
Jelena Jovanovic, University of Belgrade, SERBIA
Kenneth R. Koedinger, Carnegie Mellon University, USA
Irena Koprinska, University of Sydney, AUSTRALIA
Andrew Lan, University of Massachusetts at Amherst, USA
Collin F. Lynch, North Carolina State University, USA
Bruce M. McLaren, Carnegie Mellon University, USA
Caitlin Mills, University of Minnesota, USA
Tanja Mitrovic, University of Canterbury, NEW ZEALAND
Jack Mostow, Carnegie Mellon University, USA
Zachary A. Pardos, University of California at Berkeley, USA
Philip Irvin Pavlik Jr., University of Memphis, USA
Niels Pinkwart, Humboldt-Universität zu Berlin, GERMANY
Kaśka Porayska-Pomsta, University College London, UNITED KINGDOM
Carolyn Penstein-Rosé, Carnegie Mellon University, USA
Maria Mercedes T. Rodrigo, Ateneo de Manila University,
PHILIPPINES
Cristobal Romero, University of Cordoba, SPAIN
Elizabeth Rowe, TERC, USA
Amal Zouaq, Polytechnique Montréal, CANADA
Deepak Agarwal, Khan Academy, INDIA
Juan Miguel Andres-Bray, McGraw Hill Education, USA
Michelle Banawan, Asian Institute of Management, PHILIPPINES
Johannes Berens, University of Wuppertal, GERMANY
Anthony Botelho, University of Florida, USA
Javier Bravo Agapito, Universidad Complutense de Madrid,
SPAIN
Carrie Demans Epp, University of Alberta, CANADA
Luke Eglington, Amplify Education Inc., USA
Lief Esbenshade, Google, LLC, USA
Jibril Frej, EPFL, SWITZERLAND
April Galyardt, University of Georgia, USA
Josh Gardner, University of Washington, USA
Ilya Goldin, Phenom People, USA
Qiwei He, Georgetown University, USA
Martin Hlosta, Fernfachhochschule, SWITZERLAND
Paul Hur, University of Illinois Urbana–Champaign, USA
Stephen Hutt, University of Denver, USA
Paul Inventado, California State University Fullerton, USA
Qinjin Jia, North Carolina State University, USA
Weijie Jiang, University of California, Berkeley, USA
Song Ju, North Carolina State University, USA
Mohammad Khajah, Kuwait Institute for Scientific Research,
KUWAIT
Sebastien Lalle, Sorbonne University, FRANCE
Renza Campagni, University of Florence, ITALY
Amar Lalwani, IIIT-Bangalore, INDIA
Morgan Lee, Worcester Polytechnic Institute, USA
Qi Liu, University of Science and Technology of China, CHINA
Xuesong Lu, East China Normal University, CHINA
Christopher J. MacLellan, Georgia Institute of Technology,
USA
Varun Mandalapu, University of Maryland Baltimore County,
USA
Rubén Manrique, Universidad de los Andes, COLOMBIA
Roberto Martinez-Maldonado, University of Melbourne,
AUSTRALIA
Mirko Marras, University of Cagliari, ITALY
Tanya Nazaretsky, EPFL, SWITZERLAND
Jaclyn Ocumpaugh, University of Pennsylvania, USA
Benjamin Paassen, University Bielefeld, GERMANY
Shalini Pandey, Facebook, USA
Seyed Parsa Neshaei, EPFL, SWITZERLAND
Philip Irvin Pavlik Jr., University of Memphis, USA
Radek Pelanek, Masaryk University Brno, CZECH REPUBLIC
Juan Pinto, University of Illinois Urbana–Champaign, USA
Agoritsa Polyzou, Florida International University, USA
Ethan Prihar, EPFL, SWITZERLAND
Eloi Puertas, Universidad de Barcelona, SPAIN
Emanuel Queiroga, Instituto Federal Sul-rio-grandense,
BRASIL
Bahar Radmehr, EPFL, SWITZERLAND
Adam C. Sales, Worcester Polytechnic Institute, USA
Alexander Scarlatos, University of Massachusetts - Amherst,
USA
Sabine Seufert, University St. Gallen, SWITZERLAND
Yang Shi, North Carolina State University, USA
Antonette Shibani, "University of Technology, Sydney", AUSTRALIA
Heiner Stuckenschmidt, University of
Mannheim, GERMANY
S Supraja, Nanyang Technological University, SINGAPORE
Vinitra Swamy, EPFL, SWITZERLAND
Ange Tato, École de Technologie Supérieure, CANADA
Stefan Trausan-Matu, University Politehnica of Bucharest and
Institute of Artificial Intelligence of the Romanian Academy, ROMANIA
Maomi Ueno, University of Electro-Communications, Chofugaoka, JAPAN
Kirk Vanacore, Worcester Polytechnic Institute, USA
Kerstin Wagner, Berlin University of Applied Sciences,
GERMANY
Tianqi Wang, University at Buffalo, USA
Jacob Whitehill, Worcester Polytechnic Institute, USA
Renzhe Yu, Columbia University, USA
Jifan Yu, Tsinghua University, CHINA
Amelia Zafra Gómez, University of Córdoba, SPAIN
Guojing Zhou, University of Colorado Boulder, USA
Jia Zhu, "Zhejiang Normal University, Jinhua, China", CHINA
Agathe Merceron (Editor), Berlin
University of Applied Sciences, GERMANY
Ryan
S. Baker (Associate Editor), University of Pennsylvania, USA
Min Chi (Associate Editor), North
Carolina State University, USA
Andrew M. Olney (Associate Editor
and Editor 2017-2021), University of Memphis, USA
Luc Paquette (Associate Editor),
University of Illinois at Urbana-Champaign, USA
Anna Rafferty (Associate Editor),
Carleton College, USA
Olga C. Santos (Associate Editor),
Universidad Nacional de Educación a Distancia, SPAIN
Kalina
Yacef (Associate Editor and Founding Editor, 2009-2013), University of Sydney,
AUSTRALIA