{"id":310,"date":"2025-02-05T07:32:33","date_gmt":"2025-02-05T07:32:33","guid":{"rendered":"https:\/\/educationaldatamining.org\/edm2025\/?page_id=310"},"modified":"2025-08-04T06:42:18","modified_gmt":"2025-08-04T06:42:18","slug":"edm-2025-speakers","status":"publish","type":"page","link":"https:\/\/educationaldatamining.org\/edm2025\/edm-2025-speakers\/","title":{"rendered":"EDM 2025 Speakers"},"content":{"rendered":"\n

Keynote Speakers<\/h2>\n\n\n\n
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Jill Burstein<\/h4>\n\n\n\n

Principal Assessment Scientist
Duolingo<\/p>\n<\/div>\n\n\n\n

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Title: Growing with AI: A Dynamic Ecosystem for Digital Learning and Assessment<\/strong><\/p>\n\n\n\n

Abstract:<\/strong> The broader \u201cwhy<\/em>\u201d in educational data mining is to enhance education with an eye toward improving learner and test-taker experiences and success. To do this, our communities use generative AI, classical AI\/machine learning, and statistical and psychometric methods to model learner and test-taker data (such as, data from student-tutor dialogs, writing responses, and clickstream data). Patterns that emerge from the data enable us, for example, to better understand learning behaviors, predict academic performance, and create adaptive learning and assessment experiences that foster student and test-taker success. Thinking about our research, it is important to ask: What is my \u201cwhy\u201d? And, how does my work create impact within a dynamic learning and assessment ecosystem that includes task design, measurement, security, learner and test-taker experience, and human-centered, responsible AI?<\/em> Drawing from my assessment background, I will present a theoretical assessment ecosystem, highlighting key components, leverage points, and a human-centered AI approach for accurate and responsible data-driven outcomes. Using concrete examples drawn from language assessment, the talk will examine the intersection of assessment theory and practice.  Expanding on this, I will discuss how to adapt the ecosystem for learning, and the importance of the ecosystem as a growth mechanism that supports continuous AI growth and use in digital learning and assessment research and systems development. The talk will offer a perspective about how the ecosystem can help us effectively implement our \u201cWhy?<\/em>\u201d,  producing theoretically sound, ethical, and impactful learning and assessment research and systems.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Chris Piech<\/h4>\n\n\n\n

Assistant Professor of Computer Science Education
Stanford University<\/em><\/p>\n<\/div>\n\n\n\n

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Title<\/strong>: The Next Educational Revolution: Grand Challenges for Learning @ Scale in the Generative AI Era<\/strong><\/p>\n\n\n\n

Abstract<\/strong>: The community working on learning at scale has made tremendous progress over the last decade, successfully achieving many of our previously stated grand challenges. As we enter the Generative AI era, what new ambitious milestones should we shoot for in order to make progress towards the joyful, high-quality education at scale for all learners? This talk will explore several potential objectives, including scaling human teaching, developing effective generative AI tools, reaching new heights in student understanding, and addressing a major persistent constraint: student motivation.<\/p>\n<\/div>\n<\/div>\n\n\n\n

Presentation in collaboration with Learning@Scale<\/a><\/p>\n\n\n\n

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Prof. Ram Kumar Educational Data Mining Test of Time Award Talk<\/a><\/strong><\/h2>\n\n\n\n
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Ryan S. Baker<\/h4>\n\n\n\n

Professor of education and computer science<\/em>
University of Pennsylvania, USA<\/em><\/p>\n<\/div>\n\n\n\n

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Abstract. <\/em><\/strong>In 2012, we presented one of the first papers demonstrating sensor-free affect detection. This paper established that affect detection was possible using just the interaction logs from a digital learning platform; and it validated the detectors using unseen data from humans conducting classroom observations, establishing a paradigm for the development and validation of these types of EDM models. Since then, dozens of affect detectors have been developed by scholars around the world, providing increasingly high-quality measurements of students’ boredom, frustration, confusion, and engaged concentration\/flow, as well as occasionally other affective states such as anxiety and delight. The resultant detectors have been used in a large range of studies investigating affect, from fine-grained studies on affect dynamics, to research on the interaction between affect and behavior or other processes such as self-regulated learning, to research on the longitudinal implications of negative affect.
And yet, unlike other EDM algorithms and models — unlike, say, knowledge tracing or dropout  prediction — affect detectors are generally not in scaled deployment in real-life learning technologies.     Individual papers have demonstrated the potential of affect detection for benefiting learners, but the adaptivity or reporting demonstrated in these papers has not made it into the ongoing functionality of learning systems used at scale.
In this talk, I will consider why that might have been the case so far, and ways that we could expand affect detection to large-scale deployment in education, particularly in this era of generative artificial intelligence, and the new generation of adaptive learning systems that are today emerging. <\/p>\n<\/div>\n<\/div>\n\n\n\n

For the 2024 Prof. Ram Kumar Educational Data Mining Test of Time Award winner<\/strong> <\/p>\n\n\n\n

Towards Sensor-Free Affect Detection in Cognitive Tutor Algebra
Ryan S.J.d. Baker, Sujith M. Gowda, Michael Wixon, Jessica Kalka, Angela Z. Wagner, Aatish Salvi, Vincent Aleven, Gail W. Kusbit, Jaclyn Ocumpaugh, and Lisa Rossi <\/em><\/p>\n\n\n\n

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