The Learning & Student Analytics Conference (LSAC) 2019 aims at bringing together researchers from a number of disciplines (e.g. education, artificial intelligence, computer science, management, psychology, ergonomics, economics, IT security, data governance…), practitioners, students, policy makers and companies to share and discuss the latest research insights related to Learning Analytics. The conference further provides a platform for stakeholders to engage in critical conversations about current trends and policy requirements.
After two editions in the Netherlands, the 2019 edition of the conference moves to France and is organized at Loria, Université de Lorraine. This year the conference program will give particular attention to the crucial question of equity and ethics in higher education and will focus on individuals versus communities. Therefore, academics and practitioners alike, who are interested in such topics should consider submitting an abstract and attending this event.
The conference aims at stimulating discussion on these timely topics to discuss Learning Analytics applications aiming to visualise learning activities, access learning behaviour, predict student performance, individualize learning, evaluate social learning and improve learning materials and tools. The conference is structured around three tracks :
One of the conference topics is ethics. Indeed, because of the impact of its introduction into the educational environment, the sensitivity of the data on which it is based and the algorithms it uses, the LA do not escape from the ethic and moral issues.
Paul Ricoeur writes this : "It is by convention that I will reserve the term of "ethics" for the purpose of a life accomplished under the sign of the actions considered good, and that of "moral" for the obligatory side, marked by norms, obligations, prohibitions characterized by both a requirement of universality and an effect of constraint. "
Ethics by design is a general approach (found in the field of human organizations, such as digital design and specially in AI based approach) and which aims to answer globally issues related to both ethic and legal.
The following topics are also welcome :
Professor at University of Lorraine. Head of Loria KIWI Team.
Associate professor at University of Lorraine. Member of Loria KIWI Team.
Associate professor at University of Lorraine. Member of Loria KIWI Team.
Agathe Merceron is Professor of Computer Science at the Beuth University of Applied Sciences Berlin. She is the head of the Media Informatics Online Bachelor's and Master's degree programs. Her research interests lie in Technology Enhanced Learning with a focus on Educational Data Mining and Learning Analytics. She is nationally and internationally involved in these areas and has served as program-chair for both the international conference on Educational Data Mining (EDM) and the international conference of Learning Analytics and Knowledge (LAK); she is Associate Editor of the Journal of Educational Data Mining.
Learning analytics is defined as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs”. However, the results of our analyses are not perfect. Prediction of marks or of drop-out for example, has almost never an accuracy of 100%. Kitto, Buckingham Shaw and Gibson ask an important question: “It is usually assumed to be important that classifiers be accurate, as otherwise a student will be subjected to inappropriate interventions. However, such a position leaves us in a dilemma; are we to wait until perfect accuracy is achieved?” (LAK 2018). In this talk, I will present research work that is confronted with this dilemma.
Han van der Maas (1966) studied Psychology at the University of Amsterdam and received his Ph.D in 1993 for research on methods for the analysis of phase transitions in cognitive development. In 2005 he became professor and chair of Psychological Methods group at the University of Amsterdam. In 2009 he founded Oefenweb.nl, a spin-off company of the UvA-holding, selling a unique online adaptive learning and monitoring game platform for children. The data has been used for numerous publications and several dissertations. His research focuses on formalizing and testing psychological theories in areas such as cognition, expertise, development, attitudes and intelligence. His 2006 paper on the mutualism model of general intelligence was the start of the network psychometric approach that has become very popular in different areas of psychology.
We developed Math Garden in order to collect highly frequent and reliable data on children’s cognitive development. Participants do their daily practice in our online adaptive learning environment, meaning that they always do exercises adapted to their own ability level. This accurate adaptability was achieved by developing a novel adaptive algorithm based on the Elo rating system for chess, where item difficulties and student abilities are estimated on the fly.There are numerous benefits of this approach. First, it is relatively cheap to build such systems as item difficulties do not have to be pretested. Second, our learning analytics are based on the highest quality measurement scales available in educational measurement. Third, we create a win-win-win setup. Children like to play our games and practice relevant materials, teachers do not have to grade exercises and are provided with detailed learning analytics, and researchers get access to a rich database on cognitive development. Math Garden is used by about 200,000 children in the Netherlands. We collect about 1 million item responses per day, and we published over 30 peer-reviewed papers using Math Garden data. In this talk I will introduce the Math Garden approach, present results, and discuss new developments such as randomized double blind experiments and new learning analytics based on mouse tracking.
Andrew Cormak's current role is to keep Jisc technologies and Janet Network connected organisations informed about the legal, policy and security issues around networks and networked services.
Celia Zolinski is currently working at the Sorbonne Right School and at the Sorbonne Juridic Research Institute. Her research themes fall below :
Claudio Cimelli is head of the digital projects incubation mission (Numéri'lab) since 2014. Previously, he was coordinator of the national digital dialogue (from November 2014 to May 2015). He pilots programs and research / study actions of the Directorate of Digital Education (French ministry of education) :
Vice-Rector of CNED, Jérôme Villot has stared his teaching career in 1999. From 2006 to 2015 he led the management of several ESCEM’scourses and programs. He has developed and deployed innovative pedagogical methods, in particular the adaptive method of anticipatory pedagogy introduced in 2008 on the Bachelor program. In 2014, he became ESCEM’s Director of Programs and Education. At the same time, he continued to carry out teaching activities, especially in economics and mathematics. Since 2004, he has also been involved in consulting in business creation and development. Jérôme Villot was born in 1972. As deputy director at the CNED since 2016, Jérôme Villot has led and implemented the school's core tasks : distance learning, massively, with a focus on the success of learners. The aim is to improve, professionalize and evolve this activity to adapt it to a profoundly changing context.
In parallel with a doctorate with Prof. Guy Pujolle, Julien participated, as responsible for research and development, then assistant technical director, to the creation and development of the startup UCOPIA, become leader in France and Europe in its field. Julien is currently Technical Director (CTO) of Nomad Education, a free mobile application that assists middle school students and students towards success and helps them become actors in their future. He is also in charge of new projects such as Adaptive Learning, supported by the BPI.
Graduate of ESSEC in 2007, Clémence began her career in digital marketing at Lagardère and joined the world of start-ups. After 10 years of experience in the areas of e-commerce, sustainable development and health, she wants to engage in a project with real social impact. She joined Nomad Education in 2018, as Marketing and Communication Director, to help grow this Education project.
Ivanna is a 3rd year student at the University of Amsterdam, Netherlands. She is heavily involved with university life, as a student assistant for one of the courses and was part of the student council. She is interested in education and possibilities that modern technologies can offer in this field.
Azim Roussanaly is an associate professor in computer science at Université de Lorraine. His research activities focus on data, personal data and privacy, when storing and managing data.
Thomas Toulotte is a PhD student in Law at Université Versailles Saint Quentin. He is specialised in digital law in the frame of Learning Analytics.
Dr. Agathe Merceron is professor of Computer Science at Beuth University of Applied Sciences. She is responsible for the on-line degrees Bachelor and Master "Computer Science and Media". Her current research interests include Information Systems and Knowledge Management, application to E-learning, Technology Enhanced Learning, Educational Data Mining and Learning Analytics.
Vanda Luengo is a full Professor from the Campus Pierre et Marie Curie Sorbonne Université. She is part of the LIP6 MOCAH team.
María Jesús Rodríguez-Triana received her PhD for her thesis on learning design and learning analytics applied to computer-supported collaborative learning. In 2014, she joined the REACT group at École Polytechnique Fédérale de Lausanne (EPFL, Switzerland) as a postdoctoral fellow. Since 2016, she is also a senior researcher at the Centre of Excellence in Educational Innovation of Tallinn University (Estonia). She has been involved in educational research projects dealing with the application of learning analytics in virtual learning environments to support teacher professional developement and to reinforce different pedagogical approaches.
Assessing the impact of students’ video watching behavior on academic performance
The use of Learning Analytics to Explore Sequences of Self-Regulated Learning in MOOCs
Unsupervised Outlier Detection: students profiling in an effort to indicate learning problems in Higher Educational Institutions
A Learning Analytics project from a technical point of view
User-centered design applied to Learning Analytics Dashboard (LAD)
ADORE: Towards A Methodological Framework for Addressing Drop Out Rates in Higher Education
Interest, challenges and issues in a Learning Analytics approach for an academy
As vendors and teachers see it: Opportunities and challenges in the use of learning analytics
Lawful and ethical learning analytics by design
Requirement Analysis Towards Building a Personalized OER Recommender, based on Labour Market Information
Detection of learners at risk of failure in online professional training
Building a student effort dataset: what can we learn from behavioral and physiological data
Collecting Data on Student Workload Perception for Learning
Guidance Personalization in Serious Games for Attention Training
kTBS4LA : a platform for analyzing traces through visualization and interactive exploration
Towards a new form of process support in interactive systems
Exploring the engagement of Learning Analytics stakeholders at Higher Education Institutions (HEIs): A Template Analysis of the literature
Integration of Augmented Reality in Language Learning through the Concept of Imitating Mental Ability of Word Association (CIMAWA)
Extracting learning analytics from the data of a mental math game
Trying to improve e learning efficiency with automatic sentiment analysis to improve e learning efficiency with automatic sentiment analysis
Learning Analytics from a Student Perspective. Student preferences regarding use of data to goals and ambitions in learning
Hierarchical Partially Observable Markov Decision Processing Recommender System in E-learning for Long-term Goal
Adaptive learning and Xerte PROJECT UPDATE
The LOLA Project - Open Laboratory For Learning Analytics
Towards an automated Framework for benchmarking Learning Record Stores: Performance Requirements and Scalability
Data Flow Framework: A persona-based repository to modeling
The Learning & Student Analytics Conference (LSAC) 2019 will bring together researchers from several disciplines (e.g. education, artificial intelligence, computer science, management, psychology, ergonomics, economics, IT security, data governance, …) and a number of stakeholders (e.g. practitioners, students, teachers, managers, policy makers, companies, …) to share and discuss the latest research insights related to Learning Analytics. The conference will provide a key platform for stakeholders to engage in critical discussions about current trends and policy requirements.
The 2019 edition will take place in Nancy (France), at Loria, Université de Lorraine.
The main themes of this year’s conference are equity and ethics in higher education, with a specific focus on individuals versus communities.
The conference is structured around three tracks :
Works related to the following topics, but not limited to, are welcome :
The organisers welcome extended abstracts (maximum 750 words) for each of the sessions. All submissions should follow this template.
Some of these extended abstracts will be selected for an extended version and will be published in an open access.
This hackathon is open to all kind of profile. If you are interested in education, whether as a student, teacher, developer, designer or scientist from various horizons, we welcome your participation !
Please note that a certificate and a gift will be granted to each members of the winning team.
This hackathon aims at designing innovative prototypes and technical solutions for teachers, learners and academic staff by exploring and evaluating given learning datasets.
Participants register to the hackathon individually. We have scheduled this hackathon over two days. For the first half day, there is a period of orienting and introducing participants to the core themes. The staff will form teams of 4-5 people and give them access to one or more datasets on which they will work together. We will discuss progress regularly and at the end of the hackathon a jury will decide between teams, following specific grading criteria (Innovativeness of the idea / Originality, Social impact, Feasibility).
Hackathon attendees will be added to an online common working place which has been set up in order to preserve the outputs from the hackathon and distribute specific files.
Concerning deliverables, we expect a mock-up for each team, eventually a software and a presentation of the team proposal.
An effort will be made at the end of the hackathon to summarise the evidence and lessons learned which will be published if strong enough.
23 October - 18H : Hackathon Buffet at Loria
Ben Soussia Amal
PEREIRA FILHO José Arilton
Agathe Merceron, Beuth, Germany
Ilja Cornelisz, Vrije Universiteit Amsterdam, Netherlands
Chris van Klaveren, Vrije Universiteit Amsterdam, Netherlands
Gábor Kismihók, TIB Hannover, Germany
Ian Dolphin, Apereo
Alan Berg, University of Amsterdam , Netherlands
Stefan T. Mol, University of Amsterdam, Netherlands
Peggy Valcke, University of Amsterdam, Netherlands
Andrew Cormack, JISC
Celia Zolynski, Sorbonne Right School/Juridic Research Institute, France
Vanda Luengo, Paris Sorbonne, France
Maria Jesus Rodriguez Triana, Tallin University, Estonia
Hendrick Drachsler, LIRIE, Germany
Martin Hlosta, The Open University, UK