General architecture of a Game Learning Analytics platform Sample Clauses

General architecture of a Game Learning Analytics platform. Note the Learning Analytics Model (lower left). Image from (Baalsrud Xxxxx et al., 2015) The system, developed as part of the H2020 RAGE Project, is being reused, improved and extended as part of the H2020 BEACONING Project. Therefore, the system will be updated as work continues. For the latest version, please refer to the official open-source entry-point repository in GitHub: xxxxx://xxxxxx.xxx/e-ucm/rage-analytics. To adequately identify the features that have been developed for BEACONING, and because the analytics software is open source, we are considering the use of sponsoring features, as it is common in open source projects. This will be reflected in repositories that have more than one sponsor by changing its documentation (README.md file) to identify features and their sponsors. Repositories that specifically belong to one project will not need to be changed. With this approach, rage-analytics which had the RAGE H2020 project as main sponsor until BEACONING, will eventually become eUCM-analytics. Complete information on the system deployment process, status, use and updates can be found in the official GitHub Wiki page: xxxxx://xxxxxx.xxx/e-ucm/rage-analytics/wiki. Please refer to this wiki page for the last up-to-date documentation of the system. Figure 2 shows an overview of the Learning Analytics Model: while a student plays a game or minigame, the embedded tracker will send the corresponding traces (as xAPI statements) to the analytics system for analysis and result visualization. Game-independent analyses and visualizations are provided by default for teachers and game developers. If configured, adaptive components like game-dependent analysis and visualizations can be deployed, as well as warnings and alerts for specific situations (using the students’ analytics API).
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