Half an Hour,
Nov 07, 2019
Summary of a talk by Baltasar Fernandex Manjon at CELDA 2019
ie.ft.com/uber-game
Serious games
- - 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'
- Fake news, trolls, e-influencers
http://play.centerforgamescience.com
http://centerforgamescience.org
http://www.re-mission2.org
- - Has been formally evaluated
Citizen science
- - Uses games for crowdsourcing
- Also:
- - Educational versions of commercial games
Do serious games actually work?
- - 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
Learning analytics
- - Long and Siemens
Game Analytics
- - 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
Business analytics
- - 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
Game Learning Analytics (GLA)
- - Learning analytics applied to serious games
- - Collect, analyze and visualize
Uses of GLA
- - Game testing – eg., how many finish, avg. time to completion
- - Game deployment in class – tools for teachers, eg. 'stealth' student evaluation
- - Formal game evaluation
RAGE – game analytics (using xAPI)
Beaconing – game deployment
GLA or Informagic?
- - 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
GLA structure
- - Need to be based on learning objective
- - Based on traces + analysis
- - Different levels of design – LAM
Experience API
- - 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
Game trackers / Analytics frameworks as open code
Systematization of Analytics Dashboards
- - 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
- - uAdventure tool (on top of Unity)
- - game development platform
- - includes analytics
Overview of research – 87 papers
- - GLA purposes – mostly assessment, n-game behavious; little on interventions\techniques: 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
Research questions
- - 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
Case Study
- - Game on Madrid Metro used with Down Syndrome students
Case
- - Connectado – high school cyberbullying
- - Some minigames you can never win
- - Result – increase in cyberbullying perception
Simva
- - Tool used for scientific validation of serious games
- - Goal: to simplify the validation and deployment
Mentions
- Game Learning Analytcs for Evidence=Based Serious Games, Feb 13, 2020, - CELDA 2020 – Cognition and Exploratory Learning in Digital Age, Feb 13, 2020
, - , Feb 13, 2020
, - Bad News, Feb 13, 2020
, - eUCM Development Community · GitHub, Feb 13, 2020
