Title: Computational methods for modeling and analysis of learning: Opportunities and Challenges for Artificial Intelligence in Education and Learning Analytics.

Abstract: The adoption of technology in education provides us with numerous tools and methods to support learning and teaching. Data richness offers great potential for testing analytical approaches to inform new or revisited learning paradigms. These developments allow us to employ computational approaches, such as machine learning and data mining, to address fundamental pedagogical challenges: how to guide students adaptively and provide appropriate support to facilitate learning. Most importantly, we have the possibility to document and visualize complex human processes, provide evidence of learning, and support established pedagogical theories by exploring and analyzing data traces of human practice.

Despite the potential, research in Artificial Intelligence in Education and Learning Analytics has also identified open challenges: the disconnect between learning theories and computational analytics and the threats to learners’ agency and autonomy that may occur from over-monitoring, over-scripting, and micromanaging. In my talk, I will discuss how we can bridge the gap between pedagogical theory and modern, data-driven practice while safeguarding human values and rights. I will show how we can use a machine-learning approach to model educational psychology concepts while promoting learners’ self-efficacy. Finally, I will reflect on the theoretical and practical implications of computational analytics for monitoring and guiding complex human activities, and I will elaborate on why this is a great time to move towards models that facilitate seamless transitions between formal, informal, and impromptu learning.

Bio: IRENE-ANGELICA CHOUNTA holds a Professorship on Computational Methods in Modeling and Analysis of Learning Processes at the Department of Computer Science and Applied Cognitive Science, UNIVERSITY OF DUIVERSITY-ESSEN. Her research focuses on computational learning analytics for technology-enhanced learning, student modeling and Artificial Intelligence in Education.

IRENE was born in Athens, Greece and studied Electrical and Computer Engineering at the University of Patras, where she also obtained her PhD in Human-Computer Interaction and Educational Technologies. She has worked as a postdoctoral researcher at the Human-Computer Interaction Institute at Carnegie Mellon University (Pittsburgh, USA) with Prof. Bruce McLaren on student modeling for Intelligent Tutoring Systems (ITS) and at the Department of Computational and Cognitive Sciences, University of Duisburg-Essen (Germany) with Prof. Ulrich H. Hoppe on learning analytics to scaffold communication in MOOCs, to support learning activities through online labs and to foster creativity and reflection in maker events, such as hackathons. From 2018 to 2021, she was an Assistant Professor of Learning Analytics at the University of Tartu (Estonia) where she was awarded a personal startup research grant by the Estonian Research Council (PSG286) for her work on “Combining Machine-learning and Learning Analytics to provide personalized scaffolding for computer-supported learning activities.”

Title: Things AI Doesn’t Change in Learning

Abstract: Upon encountering a large language model like ChatGPT, it’s easy to say, “This changes everything.” Indeed, it changes a lot. At this conference, a majority of presentations discuss AI applications to learning; and that’s appropriate for a conference on “Advanced Learning Technologies.” This talk is a beneficial complement to the pervasive AI conversations. What hasn’t changed in the last 12 months? What challenges beyond AI still deserve our attention?

When we place an AI behind the screen it’s easy to forget that there’s a real intelligence in front of the screen. Sometimes the impressive results from AI are simply because the AI is better informed than the person. An AI with poor information will deliver poor results; “Garbage-in, garbage-out.” Whether AI or human intelligence is the consumer, collecting and delivering quality data is essential. Given a high-quality curriculum and a set of objectives, a learner may be able to trace a better learning map than the AI and they will likely be more motivated to pursue a path of their design than one given by an automaton. So, creating good curricula, including learning objectives, competencies, and frameworks, remains a critical task. Evidence shows that helping learners find meaning and purpose keeps them motivated. Learning tools can help students develop metacognitive skills. Finally, providing frequent and rapid feedback drives learning success. Advanced Learning Technology can and should address these learning inputs with limited distraction from AI.

Bio: BRANDT REDD is a visiting professor of Information Technology and Cybersecurity at BRIGHAM YOUNG UNIVERSITY. Concurrently he is pursuing a PhD in Computing at the University of Utah. He occasionally consults for student assessment organizations. And he serves as CTO for MatchMaker Education Labs.

Professor REDD previously served as CTO at the Smarter Balanced Assessment Consortium and, prior to that, as Senior Technology Officer for Education at the Bill & Melinda Gates Foundation.

RANDT REDD is an experienced CTO, Entrepreneur, and Software Engineer with more than 25 years’ experience leading the IT teams at startup organizations. He is a recognized authority in Learning Technology Strategy with expertise in student assessment, personalized learning, competency-based learning, applied cryptography, databases, data security, networking, and data standards. He is the past secretary to the IEEE Learning Technology Standards consortium and continues to serve with the leadership board. He created and coordinates the EdMatrix.org directory of learning technology standards.

His experience includes serving as CTO, chief scientist, senior software engineer, and DBA. He co-founded two successful software companies, Agilix Labs and Folio Corporation. He was the first Chief Scientist at Ancestry.com. He is co-inventor of three patents. He has a BS in computer science and an MBA, both from BYU.

Title: Searching as a learning process – What am I looking for?

Abstract: Learning can happen with an intentional purpose, when the learner is enrolled in a formal education, for instance, to develop competencies for her career. Learning can also happen while the learner just gets caught by her curiosity and gets immersed on a journey for more understanding of something, even though the purpose may not be clear. I’ve been researching learning content, learning objects, learning resources, learning activities, and learning design for a while as my background was both in databases and learning technologies. I have also explored web technologies (social and semantic web), collaborative learning, analytics, learner motivation/engagement and systems thinking. In the last seven years, I focused my research on searching as a learning process, exploring searching tools and behaviors and their relationships with learning processes. In this talk, I will explore the learning characteristics that could be developed to improve the search tools to better support learning. I will also bring out experience and open issues on the learners’ search behavior, how to deal with the information complexity and critical thinking in searching as a learning process. Finally, I will bring a reflection on the mission of being an educator and a human being in a world of illusions, i.e., of generative information and disinformation.

Bio: SEAN WOLFGAND MATSUI SIQUEIRA is a Full Professor at the Department of Applied Informatics, FEDERAL UNIVERSITY OF THE STATE OF RIO DE JANEIRO(UNIRIO), Brazil. He holds a M.Sc. (1999) and a Ph.D. (2005) in Computer Science, both from the Pontifical Catholic University of Rio De Janeiro(PUC-Rio), Brazil. He teaches courses in Information Systems, and sociotechnical issues related to the digital world, digital transformation and digital humanities. His research interests include searching as learning; education and learning technologies; social technologies; digital transformation; information systems fundamentals and theories; and technology and philosophy. He has experience in the Computer Science area, with a focus on Web Science, Information Systems and Technology Enhanced Learning. He has participated in some international research projects and has written more than 130 papers for conferences, journals, and book chapters. He was the coordinator of the Graduate Program in Information Systems at UNIRIO from July 2012 to September 2014, the coordinator of the Program Committees of the Brazilian Symposium on Information Systems (SBSI 2015 and SBSI 2021), the Brazilian Symposium on Computers and Education (SBIE 2012 and SBIE 2014), member of the steering committee of the Brazilian Congress of Computers and Education (CBIE 2015), co-editor-in-chief of the iSYS: Brazilian Journal of Information Systems (2012-2016) as well as the Brazilian Journal on Computers in Education (2016-2018), and member of the special committee on Computers in Education (CEIE) (2014-2018, 2021) and of the special committee on Information Systems (CESI) (2015-2022), both from the Brazilian Computer Society.