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:
More information about our speakers will be coming soon....
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
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.
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:
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
We expect the following structure for the short paper:
All abstracts will go through a peer-review process.
Lecturer in Data Science