Neil Heffernan, Goran Radanovic, Anna Rafferty and Adish Singla
This workshop aims to bring together researchers and practitioners interested in the broad areas of reinforcement learning (RL) and education (ED). The workshop will focus on two thrusts: Exploring how we can leverage recent advances in RL methods to improve the state-of-the art technology for ED. Identifying unique challenges in ED that are beyond the current methodology, but can help nurture technical innovations and next breakthroughs in RL.
Causal questions--what works, for whom, when, and why--are central to learning sciences and policy, and the the interface between causal inference and the data and methods of EDM is an exciting, crucial, under-explored area of research. This workshop is intended to raise awareness of the ubiquity and importance of causal questions in EDM, some of the exciting methods available to address those questions, and some of the open questions of causal inference in EDM. It will include invited discussions of ongoing projects addressing causal questions, and short talks about relevant work in progress, including work in any stage of development.
To implement EDM at scale, we need an ecosystem of researchers, online platforms, and educators working together in the development process of all education tools. This is more important than ever as COVID-19 has forced instruction online: many educational technologies are becoming increasingly data rich, but currently do not take advantage of the science of learning or have an approach for iterative development. Closing the loop between educational data mining researchers, platforms, and practitioners is more crucial than ever. Talent will be key to the future of the closing of this loop, and to help develop the interest of young people in EDM as a field, this workshop will focus on undergraduates who are doing work in educational data mining.
This workshop aims to bring together researchers using different methods to analyze process aspects of educational data. The ubiquity of temporal student data has enabled us to apply many different process analysis methods to uncover educational processes hidden within the data. The choice of the process analysis method often depends on the research questions and the form of the data, and they vary widely among researchers. Through this workshop, we are creating an event where researchers can share the process analysis methods they use, and discover what other methods are being used in the community. We believe that this sharing of knowledge will empower our community to discover novel insights from educational data.
The Second Workshop of the Learner Data Institute (LDI) builds on the success of last year's virtual workshop and seeks to bring together researchers working across disciplines on data-intensive research of interest to the educational data science and educational data mining communities. In addition to welcoming work describing mature, data-intensive or “big data” research and emerging work-in-progress that spans traditional academic disciplines, the workshop organizers welcome case studies of interdisciplinary research programs and projects, including case studies of learning engineering efforts pursued by universities, learning technology providers, and others (both successful and unsuccessful), as well as position papers on important challenges for researchers harnessing “big data” and crossing disciplinary boundaries as they do so.
Bita Akram, Thomas Price, Yang Shi, Peter Brusilovsky, Sharon Hsiao
The objective of this workshop is to facilitate a discussion among our research community around Educational Data Mining (EDM) and AI in Computer Science Education. The workshop is meant to be an interdisciplinary event. Researchers, faculty and students are encouraged to share their data mining approaches, methodologies and experiences where AI is transforming the way students learn Computer Science (CS) skills.