Dragan Gasevic
Carolyn Rose
ABSTRACT
Learning analytics (LA) as a field remains in its infancy. Many of the techniques now prominent from practitioners have been drawn from various fields, including HCI, statistics, computer science, and learning sciences. In order for LA to grow and advance as a discipline, two significant challenges must be met: 1) development of analytics methods and techniques that are native to the LA discipline, and 2) practitioners in LA to develop algorithms and models that reflect the social and computational dimensions of analytics. This workshop introduces researchers in learning analytics to machine learning (ML) and the opportunities that ML can provide in building next generation analysis models.
Index Terms
- Learning analytics and machine learning
Recommendations
Learning analytics: envisioning a research discipline and a domain of practice
LAK '12: Proceedings of the 2nd International Conference on Learning Analytics and KnowledgeLearning analytics are rapidly being implemented in different educational settings, often without the guidance of a research base. Vendors incorporate analytics practices, models, and algorithms from datamining, business intelligence, and the emerging "...
Learning analytics in a seamless learning environment
LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge ConferenceThis paper describes seamless learning analytics methods of VASCORLL (Visualization and Analysis System for COnnecting Relationships of Learning Logs). VASCORLL is a system for visualizing and analyzing the learning logs collected by the seamless ...
Learning analytics for workplace and professional learning
LAK '16: Proceedings of the Sixth International Conference on Learning Analytics & KnowledgeRecognizing the need for addressing the rather fragmented character of research in this field, we have held a workshop on learning analytics for workplace and professional learning at the Learning Analytics and Knowledge (LAK) Conference. The workshop ...
Comments