"There are three main aspects to the work in AFEL: 1- The underlying, theoretical cognitive model for online learning, 2- the technical framework for capturing, processing and (visually) analysing data related to online learning, and 3- the integration of those two aspects into a set of usable tools for online learners.
The AFEL project relies on the co-evolution model where learning and knowledge construction happen as a result of the interaction between the cognitive system of the learner and the social system in which they operate. On this basis, an entity model was created that encapsulates the interaction between different actors in online learning, supporting data capture. The co-evolution model also represents a basis to study progress in online learning, including, besides the classical view of learning as integrating new ""topical"" knowledge, aspects related to the complexity of the activities being performed or the variety of views they might encapsulate. Specific studies are therefore being conducted in order to investigate those aspects (including studies of the ""echo chamber"" effect in online communities). As a side effect of the theoretical work in the project being at the basis of the technical work, an internal ""glossary"" was created to include shared definitions of the common concepts being manipulated in the project. The model, glossary and learning from the project have not only driven the design of the AFEL tools and studies, they are also being taken forward in further research on informal learning through online resources.
A key challenge for the AFEL project was the need for a technological infrastructure capable of collecting, processing and analysing large amounts of data from distributed, heterogeneous sources. AFEL created a robust and highly transferable data platform: A software architecture to collect, store, process and make data available from various sources of data about learning activities and resources. This architecture implements a plugeable pipeline to collect large, high-velocity online activity data, enrich those data to support classification, analysis and recommendation, compute indicators of learning and recommendations, and package the data for delivery to various interfaces, including the AFEL mobile application and visual analytics dashboard. This platform has demonstrated its ability to support capturing everyday learning activities through evaluation studies in the context of the Didactalia platform, and in the more challenging context of open web browsing, where large amounts of online activity data was collected and process for a long period of time. This has allowed us to demonstrate its transferrability and to clearly scope its exploitation, as part of GNOSS services and products, as well as as an add-on to other online learning platforms.
The overall goal of the work in AFEL was the creation of a set of tools that can demonstrably help online learners in keeping track of their learning activities and be more effective in achieving their learning goals. Those tools have been built through multiple iterations, leading to a validated toolkit that has been shown to be usable in the context of learning through an online platform. Those include a mobile learning applications supporting users in monitoring their learning activities, a recommender engine that can be flexibly adapted to provide recommendations from various sources of learning material, and a visual analytics dashboard which provide and explorable view over the learner's data, which can be further customised by the learners themselves. As for the data platform on which they rely, those tools can be transferred to a variety of online learning platform, and customised/re-combined depending on the specific scenario and requirements of the learners/learning platform. They have been integrated in the product/service offering of GNOSS and will be exploited through deployments supporting organisations relying on other le"
