• –         Have been used successfully in many domains – medicine, military
• –         But low adoption in mainstream education
• –         So we say we’re working in ‘game-like simulation’

• –       Has been formally evaluated

• –         Uses games for crowdsourcing
• Also:
• –         Educational versions of commercial games

• –         Very few sg have been formally evaluated
• –         Evaluation could be as expensive as producing the game  – difficult to get funding
• –         It is difficult to deploy the game in the classroom

• –         Long and Siemens

• –         Application of analytics to game dev and research
• –         Telemetry – info obtained at a distance
• –         Game metrics – interpretable measures of data related to games
• –         Mostly used for commercial purposes; proprietary

• –         From what happened, to why it happened, to what will happen, to how I can make it happen
• –         Ie., hindsight – insight – foresight
• –         Needs all the dat
• –         Now being used in MOOCs, because they have so much data

• –         Learning analytics applied to serious games
• –         Collect, analyze and visualize

• –         Game testing – eg., how many finish, avg. time to completion
• –         Game deployment in class – tools for teachers, eg. ‘stealth’ student evaluation
• –         Formal game evaluation

• –         Informagic – false expectations of gaining full insight on the game educational experience based on shallow data
• –         Need to set realistic expectations – most of the games are black boxes

Minimum Requirements for GLA

• –         Need access to what’s going on during the game
• –         Need access to the game ‘guts’, or the game must communicate
• –         Need to understand the meaning of the data – access to developers
• –         Also must consider ethics of data collection
• o   Are user informed?
• o   Is data anonymized
• o   Note: GDPR – creates an overhead load

• –         Need to be based on learning objective
• –         Based on traces + analysis
• –         Different levels of design – LAM

• –         New defacto standard, becoming an IEEE standard
• –         e-UCM group in collaboration with ADL for profile for serious games (xAPI-SG)
• –         xAPI-SG defines a set of verbs, activity types, and extensions

• –         Provided analytics uses xAPI-SG, dashboards do not require additionalconfiguration
• –         You can also do real-time analytics and warnings – more complex to do
• –         We were surprised to find how hard it is to make a visualization understandable by the average teacher – eg. Teacher interprets difficulty as ‘you are in Facebook’

• –         uAdventure tool (on top of Unity)
• –         game development platform
• –         includes analytics

• –         GLA purposes – mostly assessment, n-game behavious; little on interventionstechniques: mostly classical linear analytics, clusters; neural nets not broadly applied
• –         Stakeholders – teachers came third; not widely deployed
• –         Focus – to teach, most domains math and science, small sample sizes
• –         Assessment – mostly pre-post assessments
• –         Method – 2 steps – game validation phases, game deployment phase

• –         Can we predict student knowledge after playing the game
• o   With/without pretest
• o   Can we use for evaluation?
• –         Need to have greater student numbers for analysis to be useful
• –         Result – using naïve bayes – yes, we can predict student outcomes
• –         Not sure about use for evaluation

• –         Game on Madrid Metro used with Down Syndrome students

• –         Connectado – high school cyberbullying
• –         Some minigames you can never win
• –         Result – increase in cyberbullying perception

• –         Tool used for scientific validation of serious games
• –         Goal: to simplify the validation and deployment