All workshops will be located at the University of British Columbia – Buchanan B Building. Please see the Workshop Maps (pdf format).
The event is fully catered with morning, lunch and afternoon breaks. SoLAR representatives will assist with directions and coordinating shared taxis from the Westin to UBC on 29th April from 8am. A taxi ride will cost approximately $30-$40. If you would prefer other public transport options please ask the concierge or the SoLAR representatives.
All workshop registrations have now closed.
Workshop 1 (Full day 9am – 4pm): 1st International Workshop on Learning Analytics with the Web of (Linked) Data (#LALD)
Workshop 2 (Full day 9am – 4pm): Connecting Levels and Methods of Analysis in Networked Learning Communities
Workshop 3 (Half day 9am – 12pm): Learning Analytics and Higher Education: Ethical Perspectives
Workshop 4 (Half day 1pm – 4pm): Where Learning Analytics Meets Learning Design
Workshop 1 (Full day): 1st International Workshop on Learning Analytics with the Web of (Linked) Data (#LALD)
Facilitators Hendrik Drachsler (Open University of the Netherlands), Wolfgang Reinhardt (University of Paderborn), Stefan Dietze (LS3 Research Center), Mathieu d’Aquin (The Open University), Wolfgang Greller (Open University of the Netherlands), Jelena Jovanovich (University of Belgrade), Abelardo Pardo (University Carlos III of Madrid), Katrien Verbert (KU Leuven)
Abstract The main objective of the 1st International Workshop on Learning Analytics with the Web of (Linked) Data (#LALD) workshop is to connect the research efforts on LinkedData and Learning Analytics to create visionary ideas about how the synergy of Web of Data and Learning Analytics can transform and support TEL processes and applications. Therefore, the workshop will explore, collect and present datasets for TEL to discuss Learning Analytics approaches which make use of the Web of Data. During the workshop, an overview of available educational datasets will be given. The participants will have the opportunity to present own datasets or dataset descriptions, show their own data products and tools, and discuss major challenges to collect, use and share educational datasets and their products. Different promising initiatives and solutions for the above mentioned challenges will be presented.
Further details http://lald.linkededucation.org
Workshop 2 (Full day): Connecting Levels and Methods of Analysis in Networked Learning Communities
Facilitators Dan Suthers (University of Hawaii, USA), Ulrich Hoppe (University of Duisburg-Essen, Germany), Maarten de Laat (Open University of the Netherlands), Simon Buckingham Shum (The Open University, UK).
Abstract This workshop is designed for researchers and practitioners interested in empirical-analytical studies of collaborative and networked learning in socio-technical networks, using data-intensive computational methods of analysis. We particularly reach out to researchers who seek to understand how learning takes place in the interplay between individual, small group and collective levels of agency in ‘online’ or information and communication technology-mediated settings. The workshop will address how integrating multiple methods of analysis reaches to practical questions of integrating computational tools. The interaction between analysis methods and techniques and models and approaches to theory-building is also of high interest. Activities will begin with intensive pre-workshop interactions, including the exchange of data sets and tools. Please refer to the workshop website for planned activities and participation requirements.
Further details http://engaged.hnlc.org/story_comments/list/17
Workshop 3 (Half day): Learning analytics and higher education: ethical perspectives
Facilitators Fenella Galpin and Sharon Slade (The Open University, UK)
Abstract This half-day workshop will engage participants in an exploration of some of the ethical complexities introduced by using learning analytics to categorise and predict student cohorts and behaviours. Such concerns might include intrusion, disruption, privacy, confidentiality, consent and data protection.
Higher education (HE) institutions may hope that learning analytics will facilitate clearer and simpler means of understanding and driving student engagement and performance. It is clear, however, that generalisations may introduce additional risks which then determine and limit how HE institutions behave toward and react to the student – both as individuals and as members of differing cohorts. What are the rights of the student to remain an individual? Should the student have an awareness of their own label?
A wide range of issues and consequences relating to the use of learning analytics will be explored from different stakeholder perspectives.
Further details http://www.open.ac.uk/personalpages/s.slade/index.html
Workshop 4 (Half day): Where Learning Analytics Meets Learning Design
Facilitators Lori Lockyer (University of Wollongong) & Shane Dawson (University of British Columbia)
Abstract The wealth of data available through student management systems and eLearning systems has the potential to provide faculty with important, just-in-time information that may allow them to positively intervene with struggling students and/or enhance the learning experience during the delivery of a course. This information might also facilitate post-delivery review and reflection for faculty who wish to revise course design and content. But to be effective, this data needs to be appropriate to the context or pedagogical intent of the course – this is where learning analytics meets learning design.
In this workshop, participants will
- Further develop their understanding of learning design and learning analytics
- Explore how learning analytics that can support faculty during course delivery and for course review
- Identify the types of learning analytics that can be useful for different learning designs
This workshop will involve:
- brief overview presentations about learning design and learning analytics
- participant brainstorm session about what learning analytics might support faculty during (1) course delivery and/or (2) course review
- participant groups (2 to 3 participants each) working with specific learning designs to identify relevant learning analytics