Learning Analytics Reference Model
Future Research Directions in Learning Analytics
The paper "Learning Analytics: Challenges and Future Research Directions" has been published in the e-learning and education journal (eleed).
The paper presents a revised version of the learning analytics reference model and highlights future research directions in learning analytics. These include:
- Open Learning Analytics
- Big Learning Analytics
- Learning Analytics in Open Learning Environments (e.g. MOOCs)
- Mobile Learning Analytics
- Context Modeling
- Privacy-Aware Learning Analytics
- Personalized Learning Analytics
- Lifelong Learner Modeling
- Learning Analytics for Open Assessment
- Embedded Learning Analytics
- Learning Analytics Design Patterns
- Learning Analytics Evaluation
In recent years, learning analytics (LA) has attracted a great deal of attention in technology-enhanced learning (TEL) research as practitioners, institutions, and researchers are increasingly seeing the potential that LA has to shape the future TEL landscape. Generally, LA deals with the development of methods that harness educational data sets to support the learning process. This paper provides a foundation for future research in LA. It provides a systematic overview on this emerging field and its key concepts through a reference model for LA based on four dimensions, namely data, environments, context (what?), stakeholders (who?), objectives (why?), and methods (how?). It further identifies various challenges and research opportunities in the area of LA in relation to each dimension.
Chatti, M. A., Lukarov, V., Thüs, H., Muslim, A., Yousef, A. M. F., Wahid, U., Greven, C., Chakrabarti, A., Schroeder, U. (2014). Learning Analytics: Challenges and Future Research Directions. eleed, Iss. 10.