2nd Annual Learning & Student Analytics Conference

Deploying Artificial Intelligence to Improve Learning while Ensuring Privacy
#LSAC2018

University of Amsterdam / TIB Hannover

Vrije Universiteit Amsterdam / SURF

October 22-23, 2018

Location: REC-A Building, Nieuwe Achtergracht 166,

1018 WV Amsterdam, The Netherlands




Scope of the conference

The aim of the Learning & Student Analytics Conference (LSAC) 2018 is to bring together researchers and practitioners from a number of disciplines (e.g. education, artificial technology, computer science, management, psychology, economics, IT security), organisational and national policy makers, educational practitioners, students, and employers, 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.

This year the conference programme will give particular attention to learning practices, emerging themes, and case studies centered around Artificial Intelligence (AI). Therefore academics and practitioners alike, who are interested in topics such as self-regulated learning, the incorporation into the domain of learning analytics of novel data sources (e.g., job market data or social media), privacy and ethics, and data security, should consider submitting an abstract and attending this event.

Artificial Intelligence (AI) has been identified as a disruptive force that will impact many areas of society, including education. Indeed, AI will impact every aspect across all sectors of education, ranging from pedagogy, teaching, and learning, to curriculum design or from traditional curriculum based formal education to highly personalized informal learning approaches. Involving researchers, educators, and policy makers in enabling valid, reliable, and ethical AI driven educational tools and interventions is critical. Tomorrow’s educational leaders will require strong AI literacy and related skills to ensure that systems that are deployed maximise the benefits to learners, educational institutions, and society.

The interdisciplinary field of Learning Analytics has started to explore how controlled and open AI applications can benefit education and learning; often involving multimodal sources of learner data. The practical significance of developing an interdisciplinary perspective at different levels of stakeholders is corroborated by recent findings on large scale implementations of analytics in education. It is clear that the implementation of technical, behavioural, economic, and pedagogical insights into educational interventions are critical to rigorous scientific evaluation. Emerging results indicate that developing actionable interventions that scale (even with rich individual learner and learning design data), is complex, and requires substantial technological, pedagogical, and organisational expertise, and training. In addition, such policies also needs to strike a balance between student privacy and what is in the best academic interests of learners and/or institutions; adding another significant layer of complexity to the effective implementation of Learning Analytics.





Many stakeholders are thus involved in -or affected by- AI and Learning Analytics, but often without being aware of it, making sustainable scaled implementation of AI and resulting learning analytics interventions in practice a challenging endeavour at best. These stakeholders include educational managers, educational designers, educational policy makers both at the organisational and regional level, student associations, employment agencies, ethics boards, data governance centres, technologists, and so forth. There is a need to involve this wider stakeholder group in this discussion, as they have urgent and substantial claims in this emerging field.

The conference aims at stimulating discussion on these timely topics to discuss LA 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 the following three content blocks:

  1. Academic research: comprehensive evaluations of recent innovations in learning and student analytics.
    1. Theory (e.g. advances in theoretical understanding of learning and skill development)
    2. Data (e.g. innovations to operationalize, quantify and observe mechanisms of learning)
    3. Method (e.g. developments in approaches to evaluate the impact of AI and LA on learning)
  2. Policy debates: striking a balance between student privacy and data-driven quality improvements.
  3. Practitioner sessions:
    1. LA implementation (e.g. GDPR and privacy, informed consent)
    2. LA in education (e.g. multimodal data, moving beyond achievement data)
    3. LA in the job market (e.g. informal learning recommendations, skill-based matching)

Register and Abstracts




We are delighted to announce our keynote speakers:

More information about our speakers will be coming soon....

Keynote speaker 1:

Timothy A. McKay

Dr. McKay is Arthur F. Thurnau Professor of Physics, Astronomy and Education, and Principal Investigator of the Digital Innovation Greenhouse at the University of Michigan.

In education research, he works to understand and improve postsecondary student outcomes using the rich, extensive, and complex digital data produced in the course of educating students in the 21st century. He has pioneered systems such as ECoach, a computer tailored support system; REBUILD, a college-wide effort to increase usage of evidence-based methods in introductory STEM courses, and the Digital Innovation Greenhouse, an education technology accelerator within the U-M Office of Digital Education & Innovation.

Abstract

Equity in Education – Using Learning Analytics for Discovery, Exploration, and Experiment
Educational institutions hold equity as a central goal. Learning analytics can play a central role in achieving this goal. First, data are essential for probing equity. To provide an example, Tim will describe the discovery of a pattern of gendered performance differences in large foundational courses, both at Michigan and at an array of other Universities. Data can also help create solutions, as when we use tools like ECoach to personalize education. ECoach allows us both to learn more and to experiment with possible interventions. Research like this is essential for advancing equity. Data can also help to connect research and practice, providing the evidence necessary to motivate change, and creating the sociotechnical conditions for sustained reform.

Keynote speaker 2:

Ian Dolphin

Ian Dolphin is Executive Director of the Apereo Foundation, a US registered non-profit membership organization focused on the development of innovative open source software supporting higher education. He is formerly Head of eStrategy at The University of Hull in the UK, and International Director of the Jisc eFramework Initiative. Ian has broad experience of higher education IT at a national level in the UK and internationally, having served on a number of Jisc governance bodies, and on the boards of JASIG and the Sakai Foundation.

Abstract

The Case for Open Learning Analytics
Despite the broad acceptance of Learning Analytics, the topic remains controversial in higher education. Ian Dolphin will locate learning analytics within the broader development of big data, and present perspectives on some of the controversy surrounding learning analytics drawn from early and existing practice. Drawing on the work of the open source Apereo analytics community, Ian will outline several dimensions of open practice that can help overcome faculty and student resistance to the practical deployment of analytics in the service of learning.





#LSAC2018 results from the past 7 days.

Registration

Registration is possible from June, 2018.

The full registration costs for the 2-day LSAC conference will be 120 euro. This includes the conference dinner, lunches and refreshments during the meeting. For PhD-students, a reduced fee of 80 euro applies. For non-PhD students, a special ticket (not including conference dinner) of 25 euro is available, conditional upon being able to present a valid student ID card at the conference’s registration desk.

Call for Volunteers!

We are also in search of a handful of dedicated student volunteers to help us out on the days of the conference. Are you interested in volunteering at this event –and in exchange be able to attend parts of the conference yourself- please email us at LSAC.FGB@vu.nl (please include your name, university, program, student ID and a short motivation on why you’d like to volunteer as well as your availability).

Registration and payment: LSAC 2018 Tickets



Call for Abstracts

The organisers welcome extended abstracts (max 750 words) for the academic research parallel sessions and for the applied sessions. The practitioner’s sessions focus on practical problems, solutions and innovations related to the aforementioned categories. The academic submissions should be state-of-the-art learning and student analytics research.
All submissions should follow this template.

All abstracts will go through a blind peer-review process.

Leibniz University Hannover (LUH) will publish all abstracts accepted to the conference. The LUH repository is an Open Access repository for members and alumni of Leibniz Universität Hannover. Several types of publications may be published here: articles, books, proceedings, reports, theses etc. All publications will be freely accessible, permanently available (guaranteed long-term preservation) and get a DOI. The repository is search engine optimized for Google resp. GoogleScholar plus all items can be retrieved via the OAI-interface (http://www.repo.uni-hannover.de/oai/request?verb=Identify ). The metadata is also included in BASE, OpenAIRE and the TIB-Portal. An overview about the repository is also available online: http://www.repo.uni-hannover.de/page/about?locale-attribute=en





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Call for Proposal Hackathon

You have an opportunity to submit proposals for research questions to be addressed and explored at Hack@LSAC18 the first of a yearly hackathon event of the Learning & Student Analytics Conference (LSAC) conference. By linking to other Learning Analytics Hackathons we intend to amplify messages around actionable research themes.



Topics

The purpose of submitting a paper proposal is to describe an open research question relevant to the conference and therefore to Hack@LSAC18. We are not looking for finished work. We are not even looking for started work. We are looking for challenges that can be the starting points for tasks to be completed at the Hackathon. Each paper should therefore briefly provide some context for the proposed research question, refer to previous research, explain the objectives that the author(s) wish(es) to be achieved through exploring this question and describe the potential impact on practices, tool support, and research in learning analytics. Examples of possible topics, taken from the outcomes of the LAK18 hackathon include:

  • Personal analytics supporting self-directed learning
  • Goal setting and analytics
  • Playgrounds for data literacy
  • Student facing Open APIs
  • Infrastructure-integrated approaches for the joint exploitation of distributed data sources, including synthetic data
  • Analytics beyond user-computer interaction data
  • Risk mitigation during deployment

Submission

We are looking for short papers following this template with a length of 2 to 3 pages including references. The paper’s purpose is to set a research question(s) that are relevant for the hackathon, provide context through the referencing of previous research and explaining the targets you wished achieved and the potential impact on open source software, standards, best practices.
We expect the following structure for the short paper:

  1. Abstract
  2. Introduction: Setting context and a brief literature review and Research Question(s)
  3. Impact: Including the targets and potential effect on Learning Analytics practices
  4. Acknowledgements
  5. References

All abstracts will go through a peer-review process.

  • Submission deadline hackathon: 1 September 2018
  • Submission link: https://easychair.org/conferences/?conf=lsac2018 (hackathon track)
  • Submission template: view template
  • Notification of acceptance to hackathon: 15 September 2018
  • Registration starts: June, 2018
  • Register and payment: LSAC 2018 Tickets
  • Conference: October 22-23, 2018
  • Hackathon: October 24-25, 2018
  • Hackathon website: lsac2018.org


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Location

The address of the REC-A building is:
Nieuwe Achtergracht 166
1018 WV Amsterdam

More info Wi-Fi account: UvA guests

Hotel Information

For hotels, the UvA usually recommends these: Volkshotel, the Bridge Hotel, Casa 400 and Hampshire hotels.


Organising and Programme committee

Dr. Gábor Kismihók
TIB Hannover

Alan Berg
Dr. Stefan T. Mol
University of Amsterdam

Prof. Anne Boyer
University of Lorraine





Dr. Ilja Cornelisz
Dr. Chris van Klaveren
Vrije Universiteit Amsterdam - Amsterdam Center for Learning Analytics

Prof. Dr. Anwar Osseyran
SURF


Programme committee

Matthew Forshaw
Lecturer in Data Science
Newcastle University

Contact: LSAC.FGB@vu.nl


Supporting Partners



Sponsors