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Keynote Lectures

Inquiry Learning with Online Laboratories. Which Factors Influence its Success and What Can We Expect from AI?
Ton de Jong, University of Twente, Netherlands

Available Soon
Erin Walker, University of Pittsburgh, United States

Available Soon
Katrien Verbert, KU Leuven, Belgium

Educational Data Mining and Learning Analytics
Cristobal Romero Morales, University of Cordoba, Spain

 

Inquiry Learning with Online Laboratories. Which Factors Influence its Success and What Can We Expect from AI?

Ton de Jong
University of Twente
Netherlands
 

Brief Bio
Ton de Jong holds a chair in Instructional Technology. He specializes in inquiry learning (mainly in science domains) supported by technology. He was coordinator of eight EU projects including the 7th framework Go-Lab project on learning with online laboratories in science and its H2020 follow-up project Next-Lab (see www.golabz.eu). Currently is on the editorial board of eight journals. He has published three papers in Science. He is AERA and ISLS fellow and was elected member of the Academia Europaea in 2014. For more info see: http://users.edte.utwente.nl/jong/Index.htm.


Abstract
Active or engaged learning is currently getting much attention because it engages students and also has proven to be a very effective form of science learning. Inquiry learning with online laboratories fits very well with this approach of active learning. Online laboratories have a number of practical advantages but they also enable the use of online tools that support students in the inquiry process. Several factors determine the success of online labs for science learning including student’s academic level and the prevalence of instructional guidance. In this presentation I will use the Go-Lab ecosystem (www.golabz.eu) as a showcase of how online labs and inquiry tools can be combined and I will sketch some future developments in which AI may help to increase the effectiveness of this instructional guidance. 



 

 

Keynote Lecture

Erin Walker
University of Pittsburgh
United States
 

Brief Bio
Dr. Erin Walker is a tenured Associate Professor at the University of Pittsburgh, with joint appointments in Computer Science and the Learning Research and Development Center. She completed her PhD in 2010 in Human-Computer Interaction from Carnegie Mellon University, and was subsequently awarded a Computing Innovations Postdoctoral Fellowship. In 2013, she became faculty in the School of Computing, Informatics, and Decision Systems Engineering at Arizona State University, and moved to the University of Pittsburgh in 2019. Her research spans human-computer interaction, artificial intelligence, and educational technology. Her current focus is two-fold: Examine how artificial intelligence techniques can be applied to support social human-human and human agent learning interactions, and use educational data mining approaches to develop deeper understanding of learners’ cognitive, metacognitive, and motivational states in the use of learning technologies. Her work has resulted in over ten journal articles and thirty peer-reviewed full conference papers, including a best paper award at Creativity and Cognition, best young researcher’s track paper award at AIED, best paper nominations at ITS and AIED, and a best technology design nomination at CSCL.


Abstract
Available Soon



 

 

Keynote Lecture

Katrien Verbert
KU Leuven
Belgium
 

Brief Bio
Available Soon


Abstract
Available Soon



 

 

Educational Data Mining and Learning Analytics

Cristobal Romero Morales
University of Cordoba
Spain
 

Brief Bio
Cristóbal Romero (http://www.uco.es/~in1romoc/) 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/domain. He has published more than 150 papers in books, journals and conferences, 50 of which have been published in Thomson-Reuters/Claryvate Analytics Impact Factor (IF) journals and some of them are highly cited EDM (Educational Data Mining) surveys/reviews papers. He is the co-editor of several special issues and two books regarding EDM. He was a founding officers of the international EDM society and he has served in the program committee of a great number of international conferences about education, personalization artificial intelligence and data mining. He was the 2020 WINNER of The Prof. Ram Kumar Educational Data Mining Test of Time Award and he was included in 2020 in the 100,000 top-scientists list developed by University of Stanford across all scientists and scientific disciplines.


Abstract
This talk is a comprehensible and general introduction to Educational Data Mining and Learning Analytics and how they have been applied over educational data. In the last decade, this research area has evolved enormously and a wide range of related terms such as Academic Analytics, Institutional Analytics, Teaching Analytics, Data-Driven Education, Data-Driven Decision-Making in Education, Big Data in Education, and Educational Data Science. This talk provides the current state of the art, the key concepts, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods and techniques, the main educational objectives, and the future trends in this research area.
More info in: https://onlinelibrary.wiley.com/doi/abs/10.1002/widm.1355



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