GLEANER is now RAGE Analytics
(Feb 2015) The experience creating GLEANER has been used to create RAGE Analytics. The development of GLEANER has ceased and continues in the RAGE Analytics GitHub repo
GLEANER is a Learning Analytics framework focused on improving the data analysis processes over educational data, with a special focus on highly interactive contents.
This framework eases the analysis and interpretation of the data generated by highly interactive contents (including, but not limited to serious games). It resolves significant challenges in deciding which data should be captured, how this data should be clustered and analyzed and which analysis models can yield adequate and trustworthy assessment results. There are still, however, some outstanding questions regarding how to find out which visualization models are more effective.
The core GLEANER component is a backend service capable of collecting Game and Learning Analytics data from different game sources. The collection is performed though an open API that can be called from any game engine capable of sending HTTP requests (an open source generic library component is also provided).
Both the API and the server configuration process are detailed in the technical documentation page.
GLEANER is an ongoing research project aiming to unleash the potential of Game and Learning Analytics and to support the next generation of serious games. As such, we are open to all kinds of feedback, and would be open to supporting you in creating your own GLEANER-aware games or your own GLEANER server deployments.
All the background and rationale behind GLEANER is further explained in some recent academic articles:
- Ángel Serrano-Laguna, Javier Torrente, Pablo Moreno-Ger, Baltasar Fernández-Manjón (2014): Application of Learning Analytics in Educational Videogames. Entertainment Computing, Elsevier (in press, early access available).
- Ángel Serrano-Laguna, Baltasar Fernández-Manjón (2014): Applying learning analytics to simplify serious games deployment in the classroom. Proceedings of the 2014 IEEE Global Engineering Education Conference (EDUCON) Pages 872-877.
- Ángel del Blanco, Ángel Serrano-Laguna, Manuel Freire, Iván Martínez-Ortiz, Baltasar Fernández-Manjón (2013): E-Learning Standards and Learning Analytics. Can Data Collection Be Improved by Using Standard Data Models?. In Proceedings of the IEEE Engineering Education Conference (EDUCON), pp 1255-1261, Berlin, Germany, March 13-15.
- Ángel Serrano-Laguna, Javier Torrente, Pablo Moreno-Ger, Baltasar Fernández-Manjón (2012): Tracing a little for big Improvements: Application of Learning Analytics and Videogames for Student Assessment. In proceedings of VS-GAMES conference 2012, pp 203-209, October 29-31 2012, Genoa, Italy.
You can download GLEANER using the links in the right side bar. You can also get the full source code from our GitHub project, and even contribute if you have any great ideas (let us know so we can credit you!).
After downloading, you will want to check the technical documentation page for additional information on the API and how to install the service.
Finally, you may also want to check our game-side reference implementation: GLEANER tracker.
GLEANER is an ongoing research projectunded by the European Commission through the 7th Framework Programme, as part of the GALA Network of Excellence (FP7-ICT-2009-5-258169).