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Game learning analtytics is not informagic educon 2018

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This presentation present Game Learning applied to Serious Games, that is Game Learning Analytics (GLA). To be effective GLA a Learning Analytics Model is needed to know what information to track and how to interpret and visualize that data to get insight about the educational process. There is an unrealistic expectation that with no model and only based on shallow interaction information you can provide an adequate feedback. This is unrealistic and what we call informagic! We present some details of our GLA work done on H2020 EU projects RAGE and BEACONING
This work is presented as part of the work done on the eMadrid NoE and with the support from Telefonica-Complutense Chair on Digital Education and Serious Games

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Game learning analtytics is not informagic educon 2018

  1. 1. Game Learning Analytics is not informagic! Iván Pérez-Colado, Cristina Alonso-Fernández, Manuel Freire, Iván Martínez-Ortiz, Baltasar Fernández-Manjón EDUCON 2018, Tenerife, Spain
  2. 2. Game Learning Analytics Learning Analytics collect interaction information from e-learning system to get insight and improve the educational process Game Learning Analytics is LA applied to Serious Games to collect, analyze and visualize information from players interactions for the different stakeholders (teacher, student, developer, manager)
  3. 3. Learning Analytics Model (LAM) Game Learning Analytics needs a model to drive the analysis of the inputs at different levels to better understand learners and to improve educational outcomes For LAMs to provide insight into learning, it is necessary to clearly establish: - requirements to fulfill a LAM definition - realistic expectations of what the outcomes can be Too often, users expect GLA with deep meaning based on shallow interaction data, assuming the GLA system can infer the game educational design This is not Game Learning Analytics but an unrealistic expectation (informagic).
  4. 4. LAM in a Game Learning Analytics platform LAM is the model that drive and details how information should be tracked, collected, aggregated and reported to a Learning Analytics System (LAS).
  5. 5. Learning Analytics Model (LAM) LAMs must provide an executable definition of: ● What: the data the system gathers, manages and uses for analysis ● How: the way in which the analysis is performed on the collected data ● Who: the stakeholders targeted by the analysis LAMs interplay with the analytics system and the main stakeholders. Blue boxes -> mechanism (LAS) Red boxes -> policy (LAM)
  6. 6. Learning Analytics Model (LAM) Several steps to define a LAM, involving stakeholders in charge of the definitions. Each step corresponds to an activity that provides a result to fulfill that step in the LAM. Default LAM available, but particular LAMs need to be created for new games.
  7. 7. Steps to define a LAM 1. Learning goals to be achieved in the game. 2. Game goals (e.g. tasks, levels) Correspondence between learning & game goals. 3. Traces to be sent by the game (follow some standard) 4. Analysis model defined how traces should be analyzed & interpreted. 5. Visualizations game-dependent to extend the default LAM.
  8. 8. Meta - Learning Analytics Model LAMs are designed for individual games. When several games are connected, we need a larger structure to deal with multi-scale games: meta-LAMs that stitch together the individual game LAMs into a larger whole. e.g. geolocalized game launching minigames A complete Meta-LAM definition requires: ● the hierarchy between the games ● the information flow from one level to another Meta-LAM allows to cover progress and completion in the whole structure to show global information.
  9. 9. Meta-LAM structure proposal Meta-LAM can be defined for general structures of games (e.g. trees). We propose a structure based on the IMS Simple Sequencing specification as described in SCORM 2004 4th Edition Sequencing and Navigation (SN). Learning activities (with associated LAMs) correspond to nodes in the Activity Tree (meta-LAM). Activities allow to define completion and mastery conditions. Rollup rules define how information is passed from children to parent nodes.
  10. 10. LAM and Meta-LAM in EU H2020 BEACONING Meta-LAM proposal considered for the H2020 EU BEACONING Project. Hierarchical structure of games and mini-games. Learning designers define GLPs as tree of missions, quests and activities.
  11. 11. Data Collection Experience API for Serious Games Profile (xAPI-SG): standard interactions model implemented in xAPI with ADL. The model allows tracking of all in-game interactions as xAPI traces (statements). It also simplifies data sharing. To use the xAPI-SG Profile, several open-source tracker implementations are available. https://github.com/e-ucm/unity-tracker
  12. 12. Data Analysis and Visualization xAPI traces allows for: - a default set of analyses and visualizations - for different stakeholders (e.g. teachers, developers). Visualization for specific games can be developed based on game-specific LAMs. Correct/incorrect answers per question (alternative in xAPI-SG). Progress per player in each task / level (completable in xAPI-SG).
  13. 13. Conclusions ● Learning Analytics Models describe and drive how interaction data from serious games is gameplay is to be collected, analyzed and displayed. ● The information extracted is essential to provide feedback to different stakeholders (e.g. teachers). ● Effective LAMs should be based on the learning design and provide a process establishing clear definitions and expected outputs. ● LAMs are applied for a single game. More complex structures require meta-LAMs to define how information is passed between levels. ● Next steps: - improve and provide more examples of LAMs to simplify adoption - test meta-LAM structure in large scale experiments
  14. 14. Thank you! Baltasar Fernandez-Manjon @baltaFM balta@fdi.ucm.es Code: https://github.com/e-ucm www.e-ucm.es

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