Learning & Student Analytics Conference
from 22 OCT 2018 until 23 OCT 2018
Learning & Student Analytics Conference (LSAC) 2018 brings together researchers and practitioners, organisational and national policy makers, educational practitioners, students, and employers, to discuss the latest research insights related to Learning Analytics. It further provides a platform for stakeholders to engage in critical conversations.
- 22 Oct 2018
- UvA-REC-A building, Amsterdam
- 120 euro
- Prior knowledge needed?
- Subject type
- Policy-related topic
- Meeting type
Central theme: Artifical Intelligence
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.
Keynote speaker Timothy McKay
Timothy A. McKay is 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.
The conference is structured around the following 3 content blocks:
1. Academic research: comprehensive evaluations of recent innovations in learning and student analytics.
- Theory (e.g. advances in theoretical understanding of learning and skill development)
- Data (e.g. innovations to operationalize, quantify and observe mechanisms of learning)
- 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:
- Learning Analytics implementation (e.g. GDPR and privacy, informed consent)
- Learning Analytics in education (e.g. multimodal data, moving beyond achievement data)
- Learning Analytics in the job market (e.g. informal learning recommendations, skill-based matching)