KEYNOTE SPEAKERS
Title of keynote speech: Full Professor
Speaker: Jelena Jovanovic
Affiliation: University of Belgrade, Department of Software Engineering
Contact details: http://jelenajovanovic.net/
Title of keynote speech:Deciphering Learning Traces: Insights, Challenges, and Innovations in Learning Log Analytics
Abstract: The widespread adoption of technology-supported learning and e-learning environments has yielded a rich landscape of learning-related data. In response, the field of Learning Analytics (LA) has arisen, leveraging the troves of data emanating from learner and instructor engagement with digital learning systems and tools, to enhance learning experiences and outcomes. Central to LA inquiry is the utilization of logged learning data, a ubiquitous byproduct of digital learning environments characterized by its accessibility, cost-efficiency, and unobtrusiveness. Such data holds strong promise for informing evidence-based decision-making in educational settings. However, transforming raw log data into meaningful learning indicators presents distinct challenges. These involve segmenting event sequences into meaningful learning sessions, temporally aligning data from diverse sources, and deciding on how to analyze fleeting interactions, to name but a few. Additionally, extracting insights from identified patterns often necessitates assumptions about the learning process, introducing a degree of subjectivity. This talk will provide an overview of typical LA approaches to utilizing logged learning data and delve into the methodological intricacies associated with processing of learning logs. It will also explore emerging research directions aimed at mitigating these challenges. The ultimate objective is to present approaches for working with learning traces that foster a more robust and empirically validated educational inquiry and practice.
Biography:
Jelena Jovanovic is a Professor at the Department of Software Engineering, University of Belgrade, Serbia. She is also an Adjunct Professor in the Centre for the Science of Learning & Technology (SLATE) at University of Bergen, Norway, and an Adjunct Professor in the Centre for Learning Analytics at Monash (CoLAM), Monash University, Australia. Her current research focus is on the use of computational approaches, including statistical and machine learning methods and techniques, network analysis, and text analytics, towards better understanding and supporting learning, primarily self-regulated learning in higher education settings. She has extensive experience in working with log data from a variety of learning platforms and tools and using such data for identifying behavioral patterns indicative of the learners’ (cognitive and meta-cognitive) strategies. She has published numerous papers in high-ranked international peer-reviewed journals and conferences. Currently she serves as Advisor to the Editor-in-Chief of IEEE Transactions on Learning Technologies and Editorial Board member of Journal of Learning Analytics, Journal of Educational Data Mining, and Computers & Education: Artificial Intelligence.
Title of keynote speech: Senior Researcher
Speaker: Sonsoles López-Pernas
Affiliation: University of Eastern Finland
Contact details: https://sonsoles.me/
Title of keynote speech: Explainable AI in education: fairness, trust and bias
Abstract: Education research has witnessed many applications of artificial intelligence (AI) such as prediction of student success, recommendation of learning materials, or automated feedback generation. A common concern is that the algorithms behind these applications act as a black box whereby it is not clear why a certain result has been obtained. Therefore, students and teachers have difficulty interpreting and trusting —and therefore acting upon— the outcomes. Explainable AI (xAI) offers a family of techniques aimed at overcoming the opacity of AI models. During this presentation, we will see how different xAI methods can be applied to diverse scenarios as well as discuss how xAI can help address some of the ethical issues related to AI such as trust, fairness and bias.
Biography:
Senior Researcher at University of Eastern Finland since 2022, working at the Learning Analytics unit. I obtained my PhD in Telematics Engineering at Universidad Politécnica de Madrid (Spain). My research areas are game-based learning in engineering and computer science education, learning analytics, and data engineering, among others. I am skilled in quantitative methods that include machine learning, process and sequence mining, network analysis, complex event processing, and data visualization, which are proven by over 100 publications in the field of learning analytics and education technology as well as workplace achievements. I am an editor and author of the first methodological book on learning analytics “Learning Analytics Methods and Tutorials: A practical guide using R”, which has been made available as open access to facilitate newcomers to the field get acquainted with state-of-the-art quantitative methods. I have developed novel methods in the field of learning analytics such as VaSSTra, for the analysis of intensive longitudinal data. I have developed the first platform for the creation of educational escape rooms (Escapp), available as open source software, which is used by several international educational institutions for students to acquire hard and soft skills. I have published several other open access software projects for teaching, research, and professional use. I am open to collaboration in research, industry projects, and PhD supervision.