Introduction to the Proceedings


For this 15th iteration of the International Conference on Educational Data Mining (EDM 2022), the conference returned to England, this time to Durham, with an online hybrid format for virtual participation as well. EDM is organized under the auspices of the International Educational Data Mining Society. The conference, held July 24th through 27th, 2022, follows fourteen previous editions (fully online in 2021 and 2020, Montréal 2019, Buffalo 2018, Wuhan 2017, Raleigh 2016, Madrid 2015, London 2014, Memphis 2013, Chania 2012, Eindhoven 2011, Pittsburgh 2010, Cordoba, 2009 and Montréal 2008).

The theme of this year’s conference is Inclusion, Diversity, Equity, and Accessibility (IDEA) in EDM Research and Practice. This theme emphasizes the importance of considering and broadening who is included – or not included – in EDM, and why. Furthermore, the theme speaks to the importance of IDEA considerations in all stages of the research process, from participant recruitment and selection, data collection, methods, analysis, results, to the application of research results in the future. The conference features three invited talks: Jennifer Hill, Professor of Applied Statistics at New York University, USA; René Kizilcec, Assistant Professor of Information Science at Cornell University, USA; and Judy Robertson, Professor of Digital Learning at the University of Edinburgh, Scotland. As in the past few years of EDM, this year’s conference also includes an invited keynote talk by the 2021 winner of the EDM Test of Time Award. The talk is delivered by Tiffany Barnes, Distinguished Professor of Computer Science at North Carolina State University, USA.

This year’s EDM conference continued the double-blind review process that started in 2019. The program committee was once again extended, this time using an interest survey process, to better reflect the community presenting works and to keep the review load for each member manageable. EDM received 90 submissions to the full papers track (10 pages), of which 26 were accepted (28.9%), while a further 12 were accepted as short papers (6 pages) and 14 as posters (4 pages). There were 56 submissions to the short paper track, of which 17 were accepted (30.4%) and a further 20 were accepted as posters. The poster and demo track itself accepted 10 contributions out of 20 submissions.

The EDM 2022 conference also held a Journal of Educational Data Mining (JEDM) Track that provides researchers a venue to deliver more substantial mature work than is possible in a conference proceeding and to present their work to a live audience. The papers submitted to this track followed the JEDM peer review process. Five papers were submitted and two papers are featured in the conference’s program.

The main conference invited contributions to an Industry Track in addition to the main track. The EDM 2022 Industry Track received six submissions of which four were accepted. The EDM conference also continues its tradition of providing opportunities for young researchers to present their work and receive feedback from their peers and senior researchers. The doctoral consortium this year features nine such presentations.

In addition to the main program, there are six workshops and tutorials: Causal Inference in Educational Data Mining (Third Annual Half-Day Workshop), 6th Educational Data Mining in Computer Science Education (CSEDM) Workshop, FATED 2022: Fairness, Accountability, and Transparency in Educational Data, The Third Workshop of The Learner Data Institute: Big Data, Research Challenges, & Science Convergence in Educational Data Science, Rethinking Accessibility: Applications in Educational Data Mining, and Tutorial: Using the Open Science Framework to promote Open Science in Education Research.

We thank the sponsors of EDM 2022 for their generous support: DuoLingo, ETS, Durham University Department of Computer Science, and Durham University School of Education. We are also thankful to the senior program committee and regular program committee members and reviewers, without whose expert input this conference would not be possible. Finally, we thank the entire organizing team and all authors who submitted their work to EDM 2022. And we thank EasyChair for their infrastructural support.

Antonija Mitrovic University of Canterbury Program Chair
Nigel Bosch University of Illinois Urbana–Champaign Program Chair
Alexandra I. Cristea Durham University General Chair
Chris Brown Durham University General Chair

July 23nd, 2022 Durham, England, UK.

Organizing Committee

General Chairs

Program Chairs

Workshop & Tutorial Chairs

Industry Track Chairs

Doctoral Consortium Chairs

JEDM Track Chairs

Poster & Demo Track Chairs

Publication/Proceedings Chairs

Accessibility Chairs

Diversity, Equity, and Inclusion Chair

Online/Hybrid Experience Chairs

Publicity and Sponsorship Chair

Web Chair

Local Organisers

IEDMS Officers

Tiffany Barnes, President North Carolina State University, USA
Mingyu Feng, Treasurer WestEd, USA

IEDMS Board of Directors

Rakesh Agrawal Data Insights Laboratories, USA
Ryan Baker University of Pennsylvania, USA
Michel Desmarais Polytechnique Montréal, Canada
Neil Heffernan Worcester Polytechnic Institute, USA
Kenneth Koedinger Carnegie Mellon University, USA
Luc Paquette University of Illinois Urbana–Champaign, USA
Anna Rafferty Carleton College, USA
Mykola Pechenizkiy Eindhoven University of Technology, Netherlands
Kalina Yacef University of Sydney, Australia

Senior Program Committee

Agathe Merceron Beuth University of Applied Sciences Berlin
Alex Bowers Columbia University
Andrew Lan University of Massachusetts at Amherst
Andrew M. Olney University of Memphis
Anna Rafferty Carleton College
Caitlin Mills University of New Hampshire
Collin Lynch North Carolina State University
Cristobal Romero Department of Computer Sciences and Numerical Analysis
Dragan Gasevic Monash University
Gautam Biswas Vanderbilt University
Irena Koprinska The University of Sydney
James Lester North Carolina State University
Jesus G. Boticario UNED
Jill-Jênn Vie Inria
John Stamper Carnegie Mellon University
Jonathan Rowe North Carolina State University
José González-Brenes Chegg
Justin Reich Massachusetts Institute of Technology
Kasia Muldner Carleton University
Kristy Elizabeth Boyer University of Florida
Luc Paquette University of Illinois Urbana–Champaign
Martina Rau University of Wisconsin - Madison
Michel Desmarais Polytechnique Montréal
Min Chi BeiKaZhouLi
Mingyu Feng WestEd
Neil Heffernan Worcester Polytechnic Institute
Niels Pinkwart Humboldt-Universität zu Berlin
Noboru Matsuda North Carolina State University
Philip I. Pavlik Jr. University of Memphis
Radek Pelánek Masaryk University Brno
Roger Azevedo University of Central Florida
Ryan Baker University of Pennsylvania
Sebastián Ventura University of Cordoba
Shaghayegh Sahebi University at Albany - SUNY
Sidney D’Mello University of Colorado Boulder
Stefan Trausan-Matu University Politehnica of Bucharest
Stephan Weibelzahl Private University of Applied Sciences Göttingen
Stephen Fancsali Carnegie Learning, Inc.
Steven Ritter Carnegie Learning, Inc.
Vanda Luengo Sorbonne Université - LIP6
Vincent Aleven Carnegie Mellon University
Zach Pardos University of California, Berkeley

Program Committee

Abhinava Barthakur University of South Australia
Aditi Mallavarapu University of Illinois Chicago
Ahmad Mel Ghent University
Ali Darvishi The University of Queensland
Amal Zouaq Polytechnique Montréal
Amelia Zafra Gómez Department of Computer Sciences and Numerical Analysis
Anis Bey Université Paul Sabatier
Anna Finamore Universidade Lusófona
Anthony F. Botelho Worcester Polytechnic Institute
April Murphy Carnegie Learning, Inc.
Aïcha Bakki Le Mans University
Beverly Park Woolf University of Massachusetts at Amherst
Bita Akram North Carolina State University
Buket Doğan Marmara Üniversitesi
Carol Forsyth Educational Testing Service
Chris Piech Stanford University
Clara Belitz University of Illinois Urbana–Champaign
Claudia Antunes Instituto Superior Técnico - Universidade de Lisboa
Costin Badica University of Craiova
Craig Zilles University of Illinois Urbana–Champaign
Cynthia D’Angelo University of Illinois Urbana–Champaign
David Pritchard Massachusetts Institute of Technology
Destiny Williams-Dobosz University of Illinois Urbana–Champaign
Diego Zapata-Rivera Educational Testing Service
Donatella Merlini Università di Firenze
Ean Teng Khor Nanyang Technological University
Eliana Scheihing Universidad Austral de Chile
Ella Haig School of Computing, University of Portsmouth
Emily Jensen University of Colorado Boulder
Erik Hemberg ALFA
Feifei Han Griffith University
Frank Stinar University of Illinois Urbana–Champaign
Giora Alexandron Weizmann Institute of Science
Guanliang Chen Monash University
Guojing Zhou University of Colorado Boulder
Hannah Valdiviejas University of Illinois Urbana–Champaign
Hassan Khosravi The University of Queensland
Hatim Lahza The University of Queensland
Howard Everson SRI International
Ivan Luković University of Novi Sad
Jeremiah Folsom-Kovarik Soar Technology, Inc.
Jia Zhu Florida International University
Jiangang Hao Educational Testing Service
Jihyun Park Apple, Inc.
Jina Kang University of Illinois Urbana–Champaign
JooYoung Seo University of Illinois Urbana–Champaign
Jose Azevedo Instituto Politécnico do Porto
José Raúl Romero University of Cordoba
Joshua Gardner University of Washington
Juho Leinonen University of Helsinki
Julien Broisin University of Toulouse
Julio Guerra University of Pittsburgh
Jun-Ming Su National University of Tainan
Keith Brawner United States Army Research Laboratory
Khushboo Thaker University of Pittsburgh
Lan Jiang University of Illinois Urbana–Champaign
Ling Tan Australian Council for Educational Research
LuEttaMae Lawrence University of California Irvine
Mar Perez-Sanagustin Pontificia Universidad Católica de Chile
Marcus Specht Delft University of Technology
Marian Cristian Mihaescu University of Craiova
Martin Hlosta Swiss Distance University of Applied Sciences
Matt Myers University of Delaware
Mehmet Celepkolu University of Florida
Michelle Banawan Arizona State University
Mirko Marras École Polytechnique Fédérale de Lausanne (EPFL)
Nathan Henderson North Carolina State University
Nathaniel Blanchard Colorado State University
Nicholas Diana Colgate University
Olga C. Santos aDeNu Research Group (UNED)
Patrick Donnelly Oregon State University Cascades
Paul Hur University of Illinois Urbana–Champaign
Paul Salvador Inventado California State University Fullerton
Paul Stefan Popescu University of Craiova
Paul Wang Georgetown University
Paulo Carvalho Carnegie Mellon University
Pedro Manuel Moreno-Marcos Universidad Carlos III de Madrid
Phillip Grimaldi Khan Academy
Prateek Basavaraj University of Central Florida
Rémi Venant Le Mans Université - LIUM
Rene Kizilcec Cornell University
Renza Campagni Università degli Studi di Firenze
Roger Nkambou Université du Québec À Montréal (UQAM)
Scott Crossley Georgia State University
Sébastien Iksal Université du Mans
Sébastien Lallé The University of British Columbia
Sergey Sosnovsky Utrecht University
Shahab Boumi University of Central Florida
Shalini Pandey University of Minnesota
Shitian Shen North Carolina State University
Solmaz Abdi The University of Queensland
Sotiris Kotsiantis University of Patras
Spyridon Doukakis Ionian University
Sreecharan Sankaranarayanan National Institute of Technology Karnataka, Surathkal
Stefan Slater University of Pennsylvania
Stephen Hutt University of Pennsylvania
Tanja Käser École Polytechnique Fédérale de Lausanne (EPFL)
Teresa Ober University of Notre Dame
Thomas Price North Carolina State University
Tounwendyam Frédéric Ouedraogo Université Norbert Zongo
Tuyet-Trinh Vu Hanoi University of Science and Technology
Vanessa Echeverria Escuela Superior Politécnica del Litoral
Vasile Rus The University of Memphis
Victor Menendez-Dominguez Universidad Autónoma de Yucatán
Violetta Cavalli-Sforza Al Akhawayn University, Morocco
Vladimir Ivančević University of Novi Sad
Wenbin Zhang Carnegie Mellon University
Yang Jiang Educational Testing Service
Yang Shi North Carolina State University
Yi-Jung Wu University of Wisconsin - Madison
Yingbin Zhang University of Illinois Urbana–Champaign
Yomna M.I. Hassan Misr International University
Zhuqian Zhou Teachers College, Columbia University





Durham University Department of Computer Science
Durham University School of Education


Atom Bank

Best Paper Selection

The program committee chairs discussed and nominated four full papers and four short papers for best paper and best student paper awards, based on reviews, review scores, and meta-reviews. The papers and reviews (both anonymous) were then sent to a best paper award committee who ranked the papers. The highest-ranked paper was awarded the best paper award, while the next-highest ranked paper with a student first author was awarded the best student paper award.

Best paper committee

Best paper nominees

(Full) Jiayi Zhang, Juliana Ma. Alexandra L. Andres, Stephen Hutt, Ryan S. Baker, Jaclyn Ocumpaugh, Caitlin Mills, Jamiella Brooks, Sheela Sethuraman and Tyron Young. Detecting SMART Model Cognitive Operations in Mathematical Problem-Solving Process

(Full) Vinthuy Phan, Laura Wright and Bridgette Decent. Addressing Competing Objectives in Allocating Funds to Scholarships and Need-based Financial Aid

(Full) Yuyang Nie, Helene Deacon, Alona Fyshe and Carrie Demmans Epp. Predicting Reading Comprehension Scores of Elementary School Students

(Full) Guojing Zhou, Robert Moulder, Chen Sun and Sidney K. D’Mello. Investigating Temporal Dynamics Underlying Successful Collaborative Problem Solving Behaviors with Multilevel Vector Autoregression

(Short) Lea Cohausz. Towards Real Interpretability of Student Success Prediction Combining Methods of XAI and Social Science

(Short) Juan Sanguino, Ruben Manrique, Olga Mariño, Mario Linares and Nicolas Cardozo. Log mining for course recommendation in limited information scenarios

(Short) Zhikai Gao, Bradley Erickson, Yiqiao Xu, Collin Lynch, Sarah Heckman and Tiffany Barnes. Admitting you have a problem is the first step: Modeling when and why students seek help in programming assignments

(Short) Anaïs Tack and Chris Piech. The AI Teacher Test: Measuring the Pedagogical Ability of Blender and GPT-3 in Educational Dialogues


Deep Down, Everyone Wants to be Causal

Jennifer Hill, Professor of Applied Statistics at New York University, USA

Most researchers in the social, behavioral, and health sciences are taught to be extremely cautious in making causal claims. However, causal inference is a necessary goal in research for addressing many of the most pressing questions around policy and practice. In the past decade, causal methodologists have increasingly been using and touting the benefits of more complicated machine learning algorithms to estimate causal effects. These methods can take some of the guesswork out of analyses, decrease the opportunity for “p-hacking,” and may be better suited for more fine-tuned tasks such as identifying varying treatment effects and generalizing results from one population to another. However, should these more advanced methods change our fundamental views about how difficult it is to infer causality? In this talk, I will discuss some potential advantages and disadvantages of using machine learning for causal inference and emphasize ways that we can all be more transparent in our inferences and honest about their limitations.

Beyond Algorithmic Fairness in Education: Equitable and Inclusive Decision-Support Systems

René Kizilcec, Assistant Professor of Information Science at Cornell University, USA

Advancing equity and inclusion in schools and universities has long been a priority in education research. While data-driven predictive models could help improve social injustices in education, many studies from other domains suggest instead that these models tend to exacerbate existing inequities without added precautions. A growing body of research from the educational data mining and neighboring communities is beginning to map out where biases are likely to occur, what contributes to them, and how to mitigate them. These efforts to advance algorithmic fairness are an important research direction, but it is critical to also consider how AI systems are used in educational contexts to support decisions and judgements. In this talk, I will survey research on algorithmic fairness and explore the role of human factors in AI systems and their implications for advancing equity and inclusion in education.

No data about me without me: Including Learners and Teachers in Educational Data Mining

Judy Robertson, Professor of Digital Learning at the University of Edinburgh, Scotland

The conference theme this year emphasises the broadening of participation and inclusion in educational data mining; in this talk, I will discuss methodologies for including learners and teachers throughout the research process. This involves not only preventing harm to young learners which might result from insufficient care when processing their data but also embracing their participation in the design and evaluation of educational data mining technologies. I will argue that even young learners can and should be included in the analysis and interpretation of data which affects them. I will give examples of a project in which children have the role of data activists, using classroom sensor data to explore their readiness to learn.

Test of Time Award: Compassionate, Data-Driven Tutors for Problem Solving and Persistence

Tiffany Barnes, Distinguished Professor of Computer Science at North Carolina State University, USA

Determining how, when, and whether to provide personalized support is a well-known challenge called the assistance dilemma. A core problem in solving the assistance dilemma is the need to discover when students are unproductive so that the tutor can intervene. This is particularly challenging for open-ended domains, even those that are well-structured with defined principles and goals. In this talk, I will present a set of data-driven methods to classify, predict, and prevent unproductive problem-solving steps in the well-structured open-ended domains of logic and programming. Our approaches leverage and extend my work on the Hint Factory, a set of methods that to build data-driven intelligent tutor supports using prior student solution attempts. In logic, we devised a HelpNeed classification model that uses prior student data to determine when students are likely to be unproductive and need help learning optimal problem-solving strategies. In a controlled study, we found that students receiving proactive assistance on logic when we predicted HelpNeed were less likely to avoid hints during training, and produced significantly shorter, more optimal posttest solutions in less time. In a similar vein, we have devised a new data-driven method that uses student trace logs to identify struggling moments during a programming assignment and determine the appropriate time for an intervention. We validated our algorithm’s classification of struggling and progressing moments with experts rating whether they believe an intervention is needed for a sample of 20% of the dataset. The result shows that our automatic struggle detection method can accurately detect struggling students with less than 2 minutes of work with 77% accuracy. We further evaluated a sample of 86 struggling moments, finding 6 reasons that human tutors gave for intervention from missing key components to needing confirmation and next steps. This research provides insight into the when and why for programming interventions. Finally, we explore the potential of what supports data-driven tutors can provide, from progress tracking to worked examples and encouraging messages, and their importance for compassionately promoting persistence in problem solving.

JEDM Presentations

Empirical Evaluation of Deep Learning Models for Knowledge Tracing: Of Hyperparameters and Metrics on Performance and Replicability

Sami Sarsa Aalto University, Finland
Juho Leinonene Aalto University, Finland
Arto Hellas Aalto University, Finland

New knowledge tracing models are continuously being proposed, even at a pace where state-of-the- art models cannot be compared with each other at the time of publication. This leads to a situation where ranking models is hard, and the underlying reasons of models’ performance – be it architectural choices, hyperparameter tuning, performance metrics, or data – is often underexplored. In this work, we review and evaluate a body of deep learning knowledge tracing (DLKT) models with openly available and widely-used data sets, and with a novel data set of students learning to program. The evaluated knowledge tracing models include Vanilla-DKT, two Long Short-Term Memory Deep Knowledge Trac- ing (LSTM-DKT) variants, two Dynamic Key-Value Memory Network (DKVMN) variants, and Self- Attentive Knowledge Tracing (SAKT). As baselines, we evaluate simple non-learning models, logistic regression and Bayesian Knowledge Tracing (BKT). To evaluate how different aspects of DLKT models influence model performance, we test input and output layer variations found in the compared models that are independent of the main architectures. We study maximum attempt count options, including filtering out long attempt sequences, that have been implicitly and explicitly used in prior studies. We contrast the observed performance variations against variations from non-model properties such as randomness and hardware. Performance of models is assessed using multiple metrics, whereby we also contrast the im- pact of the choice of metric on model performance. The key contributions of this work are the following: Evidence that DLKT models generally outperform more traditional models, but not necessarily by much and not always; Evidence that even simple baselines with little to no predictive value may outperform DLKT models, especially in terms of accuracy – highlighting importance of selecting proper baselines for comparison; Disambiguation of properties that lead to better performance in DLKT models including metric choice, input and output layer variations, common hyperparameters, random seeding and hard- ware; Discussion of issues in replicability when evaluating DLKT models, including discrepancies in prior reported results and methodology. Model implementations, evaluation code, and data are published as a part of this work.

Latent Skill Mining and Labeling from Courseware Content

Noboru Matsuda North Carolina State University, USA
Jesse Wood North Carolina State University, USA
Raj Shrivastava North Carolina State University, USA
Machi Shimmei North Carolina State University, USA
Norman Bier Carnegie Mellon University, USA

A model that maps the requisite skills, or knowledge components, to the contents of an online course is necessary to implement many adaptive learning technologies. However, developing a skill model and tagging courseware contents with individual skills can be expensive and error prone. We propose a technology to automatically identify latent skills from instructional text on existing online courseware called SMART (Skill Model mining with Automated detection of Resemblance among Texts). SMART is capable of mining, labeling, and mapping skills without using an existing skill model or student learning (aka response) data. The goal of our proposed approach is to mine latent skills from assessment items included in existing courseware, provide discovered skills with human-friendly labels, and map didactic paragraph texts with skills. This way, mapping between assessment items and paragraph texts is formed. In doing so, automated skill models produced by SMART will reduce the workload of courseware developers while enabling adaptive online content at the launch of the course. In our evaluation study, we applied SMART to two existing authentic online courses. We then compared machine-generated skill models and human-crafted skill models in terms of the accuracy of predicting students’ learning. We also evaluated the similarity between machine-generated and human-crafted skill models. The results show that student models based on SMART-generated skill models were equally predictive of students’ learning as those based on human-crafted skill models – as validated on two OLI courses. Also, SMART can generate skill models that are highly similar to human-crafted models as evidenced by the normalized mutual information (NMI) values.