Presentation for Serious Games Workshop SG'14 in ACM Multimedia 2014, Orlando, Florida Paper: http://dl.acm.org/citation.cfm?id=2656729

1. © author(s) of these slides including research results from the KOM research network and TU Darmstadt; otherwise it is specified at the respective slide
4-Nov-14
Prof. Dr.-Ing. Ralf Steinmetz
KOM - Multimedia Communications Lab
ACM Multimedia 2014
Learning Analytics and Serious Games: Trends and Considerations
ACM Multimedia Serious Games Workshop Nov. 2014
Laila Shoukry, M.Sc.

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Outline

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What is Learning Analytics
http://edtechreview.in/event/87-webinar/835-can-learning-analytics-enable-personalized-learning
“Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.” George Siemens 2011

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Motivation
http://www.openequalfree.org/gamification-versus-game- based-learning-in-the-classroom/10082
Why Learning Analytics for Serious Games
•Evaluation of Serious Games
•Justifying expense in learning contexts
•Objective and cost-effective approach
•Evaluation with Serious Games
•Provide a big amount of gameplay data
•Interactive and engaging nature  Stealth Assessment
•Enable insight about learner attributes and learning progress

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Outline

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Modelling for Learning Analytics in SG
https://www.linkedin.com/pulse/article/20140320222540- 1265384-show-what-you-know-the-future-of- competency-based-learning
•Competence-Based Knowledge Space Theory (CbKST)
•Requires learning domains to be modelled as a prerequisite competency structure
•Inferring knowledge states
•Narrative Game-Based Learning Objects (NGLOB)
•Additionally considers player type and narrative aspects
•Triple vector: Narrative Context, Gaming Context, Learning Context
•Evidence-Centered Design (ECD)
•Competency Model, Evidence Model and Action Model
•Open Learner Model (OLM)
•Presenting to the learner an understandable visualization of his current knowledge state
•Proven to improve learning outcomes

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Outline

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Choosing Data for Learning Analytics in SG
Depends on learning goals, setting, tasks, game genre, mechanic and platform
•Intensive vs. Extensive Data
•Extensive Data: for Higher Quantity
•Intensive Data: for Higher Quality
•Single-Player vs. Multiplayer
•Multiplayer:
•additional social component
•Data fed into social network analysis to identify aspects of collaborative learning
•Generic vs. Game-Specific Traces
•Generic:
•Identify strengths and weaknesses of learning games
•Compare different learning games
•Game-Specific:
•Designing games „with analytics in mind“
•More tailored to invidiual games
StoryPlay Learning Analytics Tool

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Outline

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Capturing Data for Learning Analytics in SG
Depends on data modalities and interactions
•Activity logs
•Widely employed
•Records interaction data in form of log files
•Multimodal Learning Analytics
•Includes biometric data and other multimodal data for assessing motivation, fun and collaboration aspects in learning settings
•Introduces its own challenges for aligning data
•Mobile and Ubiquitous Learning Analytics
•Data of mobile learners, suitable for mobile games
•Interaction with mobile devices
•Considering contextual information

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Outline

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Aggregating Data for Learning Analytics in SG
Depends on data sources and sample size
•Extensive Data  Aggregation accross Users
•Log data joined into central database after preprocessing using session identifiers
•Log files generated on all machines should use same data format
•Need for standardized xml formats
•„Aggregation Model“: using semantic rules to map game actions or states to meaningful expressions under which similar events are grouped
•Intensive Data  Aggregation accross Modalities
•Multimodal Data Synchronization needed for observing behavior accross MM data channels
•Some tools exist: Replayer, ChronoVis
ChronoViz.com

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Outline

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Analyzing Data for Learning Analytics in SG
Depends on learning context and application
•By instructor
•This step is not done by the system but instructor intervenes according to visualized statistics
•Automatic Analysis
•For intelligent tutoring systems and adaptive Serious Games
•Measures to be derived:
•Gaming: general in-game performance, in-game learning, in-game strategies, player type
•Learning: general traits and abilities of the learner, general knowledge, situation-specific state, learning behaviors, learning outcomes
•Rules governing the interpretation of in-game sources of evidence to infer competencies
•Algorithms applied during learning sessions to update competency models
•Data Mining and Machine Learning approaches can be used for identifying solution strategies, error patterns and player goals

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Outline

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Deploying Results for Learning Analytics in SG
Depends on learning context and application
•Visualization
•visualizations of narrative structure, player model and skill tree
•graphs, Hasse Diagrams, Heat Maps
•for games, a special need for real-time operation, extensibility and interoperability
•Adaptation
•macro-adaptivity: system responds by choosing the appropriate next learning object or narrative event
•micro-adaptivity: adjusting aspects within a learning task like task diffculty or feedback type

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Popular Analytics Tools
Piwik
Google Analytics
OpenSim Analytics for Virtual Worlds

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Questions & Contact

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Websites
http://www.zoodles.com http://www.google.com/analytics http://piwik.org/ http://secondlife.com/ http://opensimulator.org/