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Keynote Lectures

Learning Environments with Conversational Agents
Art Graesser, University of Memphis, United States

Implementing Learning Analytics
Barbara Wasson, University of Bergen, Norway

 

Learning Environments with Conversational Agents

Art Graesser
University of Memphis
United States
 

Brief Bio
Art Graesser is professor emeritus in the Department of Psychology and the Institute of Intelligent Systems at the University of Memphis, and Honorary Research Fellow at University of Oxford. His research is in discourse processing, cognitive science, and education. He has developed software in learning, language, and discourse technologies, including systems that hold a conversation in natural language with computer agents (AutoTutor) and that analyze text on multiple levels of language and discourse (Coh-Metrix). He served as editor of Discourse Processes and Journal of Educational Psychology, as president of Society for Text and Discourse and International Society for Artificial Intelligence in Education, and on four panels with the National Academy of Sciences and four OECD expert panels on problem solving (PIAAC 2011, 2021; PISA 2012, 2015). 


Abstract
Computer agents can participate in conversations with students, help them learn, and assess their progress in acquiring knowledge and skills. The design of an agent (e.g., talking head, chatbot, avatar) requires a research team with expertise in artificial intelligence, computational linguistics, psychology, discourse processes, and data mining in addition to education.  This presentation describes some learning environments with computer agents that target different adult populations.  These systems implement the eight learning affordances in the digital age that were identified in the 2018 edition of How People Learn of the National Academy of Sciences, Engineering, and Medicine: interactivity, adaptivity, feedback, choice, nonlinear access, linked representations, open-ended learner input, and communication with others. Some systems have three-party conversations, called trialogues, where two agents (such as a tutor and a peer) interact with the adult. The adult populations have ranged from struggling adult readers who need to improve their comprehension skills to advance their careers (AutoTutor-ARC) to high ability sailors in the Navy learn electronics (ElectronixTutor).  This presentation addresses a fundamental question: When does it make sense (or NOT) to include conversational agents in a learning environment?  



 

 

Implementing Learning Analytics

Barbara Wasson
University of Bergen
Norway
 

Brief Bio
Professor Dr. Barbara Wasson is Director of the Centre for The Science of Learning and Technology (SLATE), the Norwegian national centre for learning analytics. She was one of the founders of Kaleidoscope, a European Network of Excellence on Technology Enhanced Learning. Wasson is/has been PI for numerous national and international projects, including the Erasmus+ project DALI: Data Literacy for Citizens. Currently she is a member of the Norwegian Ministry of Education’s Expert group on Learning Analytics, the Council of Europe Expert Group on AI in Education, and Norway’s representative to the European School Net’s Data Interest Group. Her research interests include learning analytics, AI and education, learning games, e-assessment, teacher inquiry, and data literacy.


Abstract
Implementing Learning Analytics in Education
After 12 years Learning Analytics and Knowledge is a thriving research field, but still has to make a major impact on education. Why is it so hard to implement learning analytics in education?  Through reflections on research to practice, and examples  from my own experience in Norway I will address the complex challenges (e.g., law, ethics, technological, cultural, and pedagogical) that are met when scaling up the use of learning analytics in education. 



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