Inquiry Learning with Online Laboratories: Which Factors Influence Its Success and What Can We Expect from AI?
Ton de Jong, University of Twente, Netherlands
Context-Sensitive Technology Supports for Social Learning
Erin Walker, University of Pittsburgh, United States
Designing Learning Analytics Dashboards: Lessons Learned
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.
Context-Sensitive Technology Supports for Social Learning
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
Individuals participate in learning activities within a broader social context that spans interactions with peers, mentors, teachers, and parents. However, many intelligent technologies that support learning focus on a learner as an individual actor rather than a member of a community. In addition, these technologies can be highly prescriptive in the ways that they expect learners to interact with content, limiting learner agency and ignoring the varied ways that students might naturally engage within a learning space. In this talk, I will present a vision for a new type of intelligent technology that supports social learning in ways that are sensitive to the learner's individual preferences and broader context. Using specific technology-supported collaborative learning projects from my lab as examples, I will explore the unique ability of technology to provide personalized social experiences that allow for learner agency.
Designing Learning Analytics Dashboards: Lessons Learned
Katrien Verbert
KU Leuven
Belgium
Brief Bio
Katrien Verbert is an Associate Professor at the Augment research group of KU Leuven. She obtained a doctoral degree in Computer Science in 2008 at KU Leuven, Belgium. She was a postdoctoral researcher of the Research Foundation – Flanders (FWO) at KU Leuven. She was an Assistant Professor at TU Eindhoven, the Netherlands (2013 –2014) and Vrije Universiteit Brussel, Belgium (2014 – 2015). Her research interests include visualisation techniques, recommender systems, explainable AI, and visual analytics, and the use of these techniques in learning analytics applications. She has been involved in several European and Flemish projects on these topics, including the EU ROLE, STELLAR, STELA, ABLE, LALA, PERSFO, Smart Tags and BigDataGrapes projects. She is also involved in the organisation of several conferences and workshops (general co-chair IUI 2021, program chair LAK 2020, general chair EC-TEL 2017, program chair EC-TEL 2016, workshop chair EDM 2015, program chair LAK 2013 and program co-chair of the EdRecSys, VISLA and XLA workshop series, DC chair IUI 2017, DC chair LAK 2019).
Abstract
In this talk, I will present our work on learning analytics dashboards that aim to support students, teachers and study advisors. These dashboards visualise traces of learning activities, in order to promote awareness, reflection and sense-making. In recent years, we also increasingly employ machine learning models to make predictions and personalise content delivery in such dashboards, and we focus particularly on how to explain such models to end-users. In this talk, I will present lessons learned from the user-centred design and evaluation of these dashboards and explanations to support blended learning, group work, and communication between students and study advisors, as well as future research challenges.
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