Updated view of where we are, the real benefits, and the challenges to overcome when using learning analytics as educational technology.
Learn from early adopters and discover a set of strategies that will simplify the deployment of your learning analytics tool . Improve your student’s performance using predictive data analysis while making sure all the project stakeholders are aligned and support you on the long run.
Understanding how far an educational institution has gone on the implementation of learning analytics, may be achieved by using an “analytics maturity model”. There several models available. One of the most significant models comes from Gartner. Garner describes his analytics maturity model by multiple dimensions: Time perspective covered: past, present or future Human input required to support decision
Our adaptive learning and e-Learning analytics product, IADLearning, got featured on an interview at E-LEARN magazine given by our Research and Development Director, José Antonio Omedes. On the interview, titled “Putting Heart and Intelligence into e-Learning” different issues, impacting the current development of e-Learning technology, are discussed. In particular, Jose Antonio refers to the the following relevant
The term e-Learning Analytics (e-LA) refers to the set of techniques aimed to extract useful information from existing online education datasets. The final goals of E-Learning Analytics fall within one the following categories: Educational: Targeting to improve online education impact and student’s performance, such us: Reducing students’ drop outs Improving students’ understanding and learning Deciding
The term adaptive e-Learning refers to a set of techniques oriented to offer online students a personal and unique experience, with the final goal of maximizing their performance. Adaptive e-Learning is based on the principle that every student is unique and has a different background, educational needs, learning style, etc. The objective of Adaptive e-Learning