The Prof. Ram Kumar Educational Data Mining Test of Time Award

Created by a generous donation of the Prof. 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 International Conference on Educational Data Mining, or an 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. Current committee members are ineligible to receive the award, but former committee members are eligible to receive the award.

Award winners receive a prize of $2,000 and free registration to attend and present an award talk at the International Conference on Educational Data Mining.

2020 Test of Time Award

Data Mining Algorithms to Classify Students: from 2008 to 2021

Pr. Cristóbal Romero / University of Córdoba

Abstract: This Talk is about predicting or classifying student’s performance starting from student’s usage/interaction data with learning environments. This is one of the most important tasks in Educational Data Mining (EDM) and Learning Analytics (LA) research communities. The first part of the talk describes the original paper presented in EDM’08 in Montreal as full paper. It compares different data mining algorithms provided by Keel DM software for classifying students based on both students’ usage data in 7 Moodle courses and the final marks obtained in the corresponding Cordoba University exams. The second part of the talk describes new research lines and improvements from 2008 to the present (2021): other DM tools/software and frameworks, other classification methods/algorithms, meta-learning for parameter tuning/algorithm selection, other evaluation metrics and statistical tests, the Baker LAP criteria, early warning prediction and more data from Multisource, Multimodal and Smart learning.

Bio: Cristóbal Romero is Full Professor at the University of Córdoba in Spain and member of KDIS (Knowledge Discovery and Intelligent Systems) research group and Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI). His main research interests are the application of data mining/learning analytics and artificial intelligence techniques to educational data/environment/domain. He has published more than 150 papers in books, journals and conferences, 50 of which have been published in Thomson-Reuters Impact Factor (IF) journals and some of them are important EDM (Educational Data Mining) surveys/reviews. He was also the co-editor of several special issues and two books regarding EDM specific topics. He was a founding officers of the international EDM society, associate editor of the IEEE Transaction on Learning Technologies journal and he has served in the program committee of a great number of international conferences about education, personalization artificial intelligence and data mining.

Cristobal Romero gave the award talk at the 14th Educational Data Mining Conference (EDM2021). (Link to award talk video)

2021 Test of Time Award

The Q-matrix Method: Mining Student Response Data for Knowledge

Pr. Tiffany Barnes / North Carolina State University

Abstract: Although many talented researchers have created excellent tools for computer-assisted instruction and intelligent tutoring systems, creating high-quality, effective, scalable but individualized tools for learning at a low cost is still an open research challenge. Many learning tools create complex models of student behavior that require extensive time on the part of subject experts, as well as cognitive science researchers, to create effective help and feedback strategies. In this research, we propose a different approach, called the q-matrix method, where data from student behavior is “mined” to create concept models of the material being taught. These models are then used to both understand student behavior and direct learning paths for future students. We describe the q-matrix method and present preliminary results that imply that the method can effectively predict which concepts need further review.

Tiffany Barnes will give the award talk at the 15th Educational Data Mining Conference (EDM 2022). (Link to Paper)