The Learning Analytics Processor (LAP) is software to manage a learning analytics analytics workflow. Typically, this type of workflow is referred to as a pipeline and consists of three distinct phases: input, model execution, and output. The pipeline is build using an open architecture which exposes output from the pipeline via a collection of web service APIs. The LAP is a general purpose tool designed to meet the need for scaling-up learning analytics from manually-driven processes to automation of the routine technical tasks. The essential purpose of the LAP is to streamline data pre-processing, predictive model use, and results post-processing to make this a more efficient and reliable process. It is configurable, not tied to particular data sources, and agnostic as to the way the results of the predictive model are used.
Currently, the LAP supports the Marist College Open Academic Analytics Initiative Early Alert and Risk Assessment model but development of additional models as well as feature and scalability enhancements are underway.
The LAP arose out of the Open Academic Analytics Initiative (OAAI), led by Marist College (USA), being conceived of to automate the processing pipeline which OAAI demonstrated. It is currently work in progress, being an incubation project in the Apereo Foundation, and is under development by Unicon and Marist, having been selected in a competitive tendering process as a component for the Jisc Effective Learning Analytics pilots.
[This synopsis originally coded by the LAEP project - Learning Analytics for European educational Policy https://laepanalytics.wordpress.com/]