Workshops & Tutorials

All workshops and tutorials will take place in the city center of Leuven. Every workshop and tutorial is fully catered with morning, lunch and afternoon breaks. To apply to participate in a workshop, please see the details and deadline for submission requested by the workshop organizers (links below). The universal deadline for workshop application was set at January 31, but individual workshops may choose to accept applications after this date. Tutorials do not require an application. You can register for workshops and tutorials through the conference registration form.

Workshop 1: 1st International Workshop on Discourse-Centric Learning Analytics

  • Abstract:

Written discourse is a major class of data that learners produce in online environments, arguably the primary class of data that can give us insights into deeper learning and higher order qualities such as critical thinking, argumentation, mastery of complex ideas, empathy, collaboration and interpersonal skills. It is central to the collaborative and social learning that takes place online and there is a correspondingly significant literature on discourse analysis for online learning/CSCL. Computational linguistics research has developed a rich array of automated tools for machine interpretation of human discourse, but work to develop these tools in the context of learning is at a relatively early stage. Moreover, there is a significant difference between the use of such tools to assist researchers in discourse analysis, and their deployment on platforms in order to provide meaningful analytics for learners and educators. A major class of learning analytic will emerge at the intersection of research into learning dynamics, deliberation platforms, and computational linguistics. What will make these learning analytics, as opposed to research that sits in any of the above categories, will be their use to generate information displays that help learners and/or educators to understand where significant discourse patterns are happening and that support interventions to improve discourse for learning.

Workshop 2: 2nd International Workshop on Teaching Analytics

  • Abstract:

The core problem that this workshop series on “Teaching Analytics”( addresses is that in comparison with most other professionals from whom clients expect rapid decisions in a dynamically changing environment, presently teachers often do not get the information they need for decision making in a timely fashion and in a meaningful and actionable format. Teaching Analytics is conceived as a subfield of learning analytics that focuses on teachers’ professional practices with visual analytics methods and tools. Teaching analytics methods and tools aim to develop innovative solutions to assist and augment teachers’ dynamic diagnostic decision-making in the classrooms of the 21st century. Workshop submissions should be concerned with teachers’ use of visual analytics methods and tools in some shape or form. No requirements are placed on contexts such as high-performance classrooms or big data volumes or real-time decision-making.

Workshop 3: Analytics on Video-Based Learning

  • Abstract:

The International Workshop on Analytics on Video-based Learning (WAVe2013) aims to connect research efforts on Video-based Learning with Learning Analytics to create visionary ideas and foster synergies between the two fields. The main objective of WAVe is to build a research community around the topical area of Analytics on video-based learning. In particular, WAVe aims to develop a critical discussion about the next generation of analytics employed on video learning tools, the form of these analytics and the way they can be analyzed in order to help us to better understand and improve the value of video-based learning. WAVe is based on the rationale that by combining and analyzing learners’ interactions with other available data obtained from learners, new avenues for research on video-based learning have emerged. Specifically, WAVe aims to provide an environment where participants will get opportunities to: develop their research skills; increase their knowledge base; collaborate with others in their own and complementary research areas; and discuss their own work.

Workshop 4: Learning Object Analytics for Collections, Repositories & Federations

  • Abstract:

A large number of curated digital collections containing learning resources of a various kind has emerged in the last year. These include referatories containing descriptions for resources in the Web (as MERLOT), aggregated collections (as Organic.Edunet), concrete initiatives as Khan Academy, repositories hosting and versioning modular content (as Connexions) and meta-aggregators (as Globe and Learning Registry). Also, OpenCourseware and other OER initiatives have contributed to making this ecosystem of resources richer. Very interesting insights can be extracted when studying the usage and social data that are produced within the learning collections, repositories and federations. At the same time, concerns for the quality and sustainability of these collections have been raised, which has lead to research on quality measurement and metrics. The Workshop attempts to bring studies and demonstrations for any kind of analysis done on learning resource collections, from an interdisciplinary perspective. We consider digital collections not as merely IT deployments but as social systems with contributors, owners, evaluators and users forming patterns of interactions on top of portals or through search systems embedded in other learning technology components. This is in coherence of considering these social systems under a Web Science approach (

Tutorial 1: Learning Curve Analysis using DataShop

  • Abstract:

Learning curve analysis is one way to show learning within educational systems. A learning curve visualizes changes in student performance over time. In this tutorial we will teach the attendees how to perform learning curve analysis on log data. We will explain in detail how to create different types of learning curves, what the curves can show, and how they can be used to improve instruction. Attendees will be given the opportunity to work hands on with actual data to fit student models to data in order to create accurate models for prediction. These models will be based on the Additive Factor Model (AFM), which uses a set of customized Item-Response models to predict how a student will perform for skills in the instruction over opportunities to learn these skills.

Tutorial 2: Using Linked Data in Learning Analytics

  • Abstract

Linked Data is a set of principles and technologies aimed at using the architecture of the web to share, expose and integrate data in a global, collaborative space. This tutorial intends to provide Learning Analytics practitioners with the basic knowledge and skills required to exploit the new possibilities offered by linked data, especially through exploring the wealth of data sources already available in the linked data cloud. We will therefore introduce the basic technologies and practices generally associated with Linked Data, including graph-­based data modelling with RDF and relevant vocabularies, data discovery on the linked data cloud and the use of linked data endpoints (with SPARQL). Since the focus of the tutorial is on the concrete use of these technologies and practices within a Learning Analytics scenario, a large part of the sessions will be dedicated to hands-­on exercises with data and use cases of relevance to Learning Analytics.

In addition, the tutorial will be used as a channel to present initial outcomes of the LinkedUp project, like the LinkedUp data pool and the LinkedUp Evaluation Framework. Participants to the tutorial will be encouraged to push further their ideas regarding the possible applications of Linked Data in Learning analytics scenarios through collaborating with members of LinkedUp and participating to the LinkedUp Challenge: the application development competition organized by the project. These particular activities will be concretely materialized through the inclusion as key sessions in the tutorial of activities around the LinkedUp­‐supported “LAK Data Challenge”, as well as interactive brainstorming sessions around possible use cases for linked data in Learning Analytics scenarios, and their possible realisation.

Tutorial 3: Computational Methods and Tools for Social Network Analysis of Networked Learning Communities

  • Half day (April 9, 2013)
  • Website:
  • Tutorial organizers:
    •  Andreas Harrer, Tilman Göhnert, Alejandra Martínez Monés, Christophe Reffay
  • Abstract

Social Networks are a phenomenon that manifests itself in everyday life, but increasingly especially in computer mediated interaction and communication systems. Social Software has been brought to use in learning scenarios in various variants, such as discussion forums, group chats, or sharing of resources and links, and also including very recent trends such as MOOCs (Massive Open Online Courses). The social network data created by the usage of these collaborative learning tools are a rich data source for the analysis of interaction behaviour, emergence of group structures, types of learners, and resulting artefacts. Thus, methods for the analysis of networked learning communities can be perceived as a specific and particularly well-developed facet of Learning Analytics.
A multitude of computational methods, tools, and architectures have been developed in recent years to support the analysis of network structures. Still, for the analyst/researcher with little background in computational methods it is yet a major challenge to make use of the plethora of methods and tools available, and especially to re-create analysis processes across different datasets. The goal of this tutorial is to make the participants familiar with some established computational methods of Social Network Analysis and existing tools/workbenches to facilitate practitioners and researchers in conducting Learning Analytics of networked learning communities, including the networks between actors and artifacts. This will make it easier to establish research designs across different datasets and make them re-usable with much more ease than is currently the practice.

Co-located workshop:  Open Discovery Space workshop on Communities Analytics through the Open Socially Empowered e-Learning Portal

  • April 9, 2013
  • More information: ODS LAK WorkshopV2 
  • Workshop organizers:
    •  Sofoklis Sotiriou, Nikolas Athanasiadis, Christian M. Stracke, Psycharis Sarantos, Argiris Tzikopoulos
  • Abstract:

The aim of this workshop is to explore opportunities to contribute new empirical findings, theories, methods, and metrics for understanding how communities’ analytics work on the Open Socially Empowered e-Learning Portal of ODS ( through the use of multimodal data. The workshop will also highlight how analytics can contribute to improving pedagogical support and active involvement of users’ through assessment of new digital tools, teaching strategies, and curricula, while supporting the goal to transform the ability to identify and stimulate effective learning, enable more rapid feedback, and facilitate learning in more diverse contexts.

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