{"id":220,"date":"2026-06-27T15:52:14","date_gmt":"2026-06-27T15:52:14","guid":{"rendered":"https:\/\/educationaldatamining.org\/edm2026\/?page_id=220"},"modified":"2026-06-28T06:06:57","modified_gmt":"2026-06-28T06:06:57","slug":"test-of-time-award","status":"publish","type":"page","link":"https:\/\/educationaldatamining.org\/edm2026\/test-of-time-award\/","title":{"rendered":"Test of Time Award"},"content":{"rendered":"\n

The Prof. Ram Kumar Educational Data Mining Test of Time Award<\/h2>\n\n\n\n

Created by a generous donation of the\u00a0Prof. Ram Kumar Memorial Foundation<\/a>, the Prof. Ram Kumar Educational Data Mining Test of Time Award is given annually to a paper published in the Journal of Educational Data Mining, the\u00a0International Conference on Educational Data Mining,\u00a0or\u00a0an Educational Data Mining workshop prior to that conference. Papers must have been published at least 8 years prior to receiving the award. Papers are awarded by a committee of leaders in the field, selected by the Board of Directors of the International Educational Data Mining Society.<\/p>\n\n\n\n

2025 Award Winner<\/h2>\n\n\n\n

Metrics for Evaluation of Student Models<\/a>,
By Radek Pel\u00e1nek<\/h3>\n\n\n\n

Initially published in\u00a0Journal of Educational Data Mining (Vol. 7, No 2., 2015)<\/a><\/em><\/p>\n\n\n\n

Abstract.<\/strong> Researchers use many different metrics for evaluation of performance of student models. The aim of this paper is to provide an overview of commonly used metrics, to discuss properties, advantages, and disadvantages of different metrics, to summarize current practice in educational data mining, and to provide guidance for evaluation of student models. In the discussion we mention the relation of metrics to parameter fitting, the impact of student models on student practice (over-practice, under-practice), and point out connections to related work on evaluation of probability forecasters in other domains. We also provide an empirical comparison of metrics. One of the conclusion of the paper is that some commonly used metrics should not be used (MAE) or should be used more critically (AUC).<\/p>\n\n\n\n

Special Panel Discussion | EDM 2026<\/h2>\n\n\n\n

In place of a presentation from the 2025 EDM Test of Time Award winner, EDM 2026 will host a panel discussion of the impactful work to both reflect on the impact that this work has had on the field and how it continues to shape and inform current research on and related to student modeling. <\/p>\n\n\n\n

Invited Panelists<\/h3>\n\n\n\n
\"\"<\/figure>
\n

Gautam Biswas<\/strong> is a Cornelius Vanderbilt Professor of Engineering and a Professor of Computer Science and Electrical and Computer Engineering. He is also a Senior Research Scientist at the Vanderbilt Institute for Software Integrated Systems. Prof. Biswas conducts research in AI and machine learning, focusing on AI in Education, Learning Analytics, and the Learning Sciences. His work on open-ended STEM+C learning environments includes developing multimodal analytics to understand students\u2019 self-regulated and socially shared regulation of learning behaviors and creating intelligent agents to help them advance their learning. Recently, he has analyzed multimodal data from AR training environments to assess teamwork and performance. He received the EDM 2023 Prof. Ramkumar Award for a method to identify student learning patterns. He is a co-PI on NSF\u2019s ENGAGE AI and IES\u2019s GENIUS centers and has won multiple Best Paper awards. He is a Life Fellow of the IEEE Computer Society, the Prognostics and Health Management Society, and the Asia Pacific Society for Computers in Education.<\/p>\n<\/div><\/div>\n\n\n\n

\"\"<\/figure>
\n

Nigel Bosch<\/strong>\u00a0is an Associate Professor of Information Sciences and Educational Psychology at the University of Illinois Urbana\u2013Champaign. His research concerns machine learning approaches to measurement and the novel research directions that new types of measures enable. His work includes machine learning, algorithmic fairness, and human-centered computing, particularly in educational contexts. He is especially curious about measuring (and adapting to) the experiences of students as they develop self-perceptions of their own learning and ability. He was also Program Co-chair for the 2022 edition of the EDM conference, in addition to other roles in the EDM community.<\/p>\n<\/div><\/div>\n\n\n\n

\"\"<\/figure>
\n

Thomas\u00a0Christie\u00a0<\/strong>leads Renaissance Philanthropy’s Engineering Hub, which provides technical support and advice for mission-driven educational technology projects. Before Renaissance, he spent over 10 years in educational technology, applying ideas from cognitive science and AI for the benefit of students, and previously taught math in Santa Fe, New Mexico.\u00a0\u00a0Thomas\u00a0holds a Ph.D. in Cognitive Science from the University of Minnesota. He also earned a Master’s at St. John’s College’s Great Books program and a bachelor’s from Hendrix College<\/p>\n<\/div><\/div>\n\n\n\n

\"\"<\/figure>
\n

Tanja K\u00e4ser<\/strong> is an assistant professor at the EPFL School of Computer and Communication Sciences (IC) and head of the ML4ED laboratory. Her research lies at the intersection of machine learning, data mining, and education. She is particularly interested in creating accurate models of human behavior and learning.
Prior to joining EPFL, Tanja K\u00e4ser was a senior data scientist with the Swiss Data Science Center at ETH Zurich. Before that, she was a postdoctoral researcher with the AAALab at the Graduate School of Education of Stanford University.
Tanja K\u00e4ser received her PhD degree from the Computer Science Department of ETH Zurich. In her dissertation, completed at the Computer Graphics Laboratory, she focused on user modeling and data mining in education, which was honored with the Fritz Kutter Award 2015.<\/p>\n<\/div><\/div>\n\n\n\n

Time and Location<\/h2>\n\n\n\n

The Panel will be hosted on Wednesday, July 1, 2026 from 3:45p.m – 5:00 p.m. as part of the EDM 2026 Conference in the COEX Auditorium.<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

The Prof. Ram Kumar Educational Data Mining Test of Time Award Created by a generous donation of the\u00a0Prof. Ram Kumar Memorial Foundation, the Prof. Ram Kumar Educational Data Mining Test of Time Award is given annually to a paper published in the Journal of Educational Data Mining, the\u00a0International Conference on Educational Data Mining,\u00a0or\u00a0an Educational Data […]<\/p>\n","protected":false},"author":27,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-220","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/educationaldatamining.org\/edm2026\/wp-json\/wp\/v2\/pages\/220","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/educationaldatamining.org\/edm2026\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/educationaldatamining.org\/edm2026\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/educationaldatamining.org\/edm2026\/wp-json\/wp\/v2\/users\/27"}],"replies":[{"embeddable":true,"href":"https:\/\/educationaldatamining.org\/edm2026\/wp-json\/wp\/v2\/comments?post=220"}],"version-history":[{"count":5,"href":"https:\/\/educationaldatamining.org\/edm2026\/wp-json\/wp\/v2\/pages\/220\/revisions"}],"predecessor-version":[{"id":244,"href":"https:\/\/educationaldatamining.org\/edm2026\/wp-json\/wp\/v2\/pages\/220\/revisions\/244"}],"wp:attachment":[{"href":"https:\/\/educationaldatamining.org\/edm2026\/wp-json\/wp\/v2\/media?parent=220"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/educationaldatamining.org\/edm2026\/wp-json\/wp\/v2\/categories?post=220"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/educationaldatamining.org\/edm2026\/wp-json\/wp\/v2\/tags?post=220"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}