Serious Games Analytics - Lecture at TU Darmstadt

© author(s) of these slides including research results from the KOM research network and TU Darmstadt; otherwise it is specified at the respective slide
Multimedia Communications Lab09.06.2015 | Serious Games – Serious Games Analytics| 9
Serious Games Analytics
Laila Shoukry, M.Sc.

KOM – Multimedia Communications Lab 2
Motivation and Definitions
 Learning Analytics
 Game Analytics
 Serious Games Analytics
Learning Analytics in Serious Games
 Modelling for LA in SG
 Choosing Data
 Capturing Data
 Aggregating Data
 Analysing Data
 Deploying Results
Summary
Agenda
csloh.com/research/v-lab/

Pop Quiz!
Pressenza.com

Pop Quiz!
echtlustig.com
Wikihow.com

Assessment of Learning
http://assessment.uconn.edu/

Assessment for/in/of Learning?
syllabus.bos.nsw.edu.au

Assessment for/in/of Learning?
Open Learner Model (OLM)
 Presenting to the learner
an understandable
visualization of his
current knowledge state
 Proven to improve
learning outcomes
Personalized Learning
 Tailoring to specific needs
and characteristics of
each learner
 Learning Style,
Strengths, Weaknesses,
Pace, Special Needs

What is Learning Analytics (LA)
edtechreview.in/
“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
“Learning Analytics is about collecting
traces that learners leave behind and
using those traces to improve
learning” Eric Duval

 More accessible, affordable and functional
Technology
 Devices able to collect, synthesize and
analyse massive amounts of data
 New Types of Data Available
 More insight into learning
Why Learning Analytics
mindedge.com
dailygenius.com

Learning Analytics (LA)
vs Educational Data Mining (EDM)

Learning Analytics Dimensions
(Greller & Drachsler, 2012)

Learning Analytics - Example
Einmaleins.tu-graz.at

Game Analytics
northeastern.edu
“All forms of business intelligence data, and
any kind of method, in game development
and research.” Anders Drachen 2012
Main Goals:
Monetization
and Improving
Gameplay
gamasutra.com

Game Analytics - Example
steampowered.com/status/ep2/ep2_stats.php

Goal of LA in Serious Games
openequalfree.org/gamification-versus-game-based-learning-
in-the-classroom/10082
Why Learning Analytics for Serious
Games (Game Based Learning)
 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

Learning Analytics
 “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
Game Analytics
 “All forms of business intelligence data, and any
kind of method, in game development and
research.” Anders Drachen 2012
 “Methods for learners' gameplay data collection,
analysis and visualization to be used in Serious
Games research.” Christian Loh 2014

Outline

Modelling for LA
Baalsrud et al. (2014)

Modelling for Learning Analytics in SG
 Competence-Based Knowledge Space Theory (CbKST)
 Requires learning domains to be modelled as a prerequisite competency
structure
 Inferring competence states  Learner Model
 Flow Theory
 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

Competence-Based Knowledge Space Theory
(CbKST)
 Knowledge Domain: set of skills relevant
for solving problems
 Competence State: subset of skills that
a learner has available
 Prerequisite Relation: capturing
prerequisites between sets of skills
 Competence Structure: collection of
competence states corresponding to the
prerequisite relation
Skill Assignments :
 associating to each problem those competence states that are sufficient
for solving it
 associating to each learning object the skills that it teaches
 prerequisite structure on the set of problems/learning objects
Css.uni-graz.at

Flow
Kiili et al. (2014)
Flow Theory , Csikszentmihalyi 1988,
Zone of Proximal Development, Vygotsky

Authoring
 Annotation of Situations
 Narrative, Gaming and Learning Context
 Appropriateness for Player Model
Run-Time
 Decision: How does a story continue?
Narrative Game-Based Learning Objects
(NGLOB) - Revisited
Göbel et al. (2010)

Evidence-Centered Design (ECD)

Outline

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

Serrano-Leguana et al. 2014
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

Outline

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
Noldus.com/facereader

Privacy Concerns
Collectiveevolution.com
Nutzungsbedingungen für E-Learnin an der HWR
Berlin auf der Lernplattform Moodle:
Art der gespeicherten Daten
Bei der Nutzung von Moodle werden Ihre Beiträge
und Aktivitäten in Protokolldateien des Webservers
sowie der Moodle-Software gespeichert, soweit das
für die individualisierten Funktionalitäten in Moodle
erforderlich ist. Weder Kursverantwortliche (z.B. in
der Rolle „Lehrende“), noch andere
Kursteilnehmende (z.B. in der Rolle „Studierende“)
haben Zugriff auf diese Nutzungsdaten.
Kursverantwortliche (in der Rolle „Lehrende“) haben
Zugriff auf sogenannte Aktivitätsübersichten zu
Zwecken der Lehrvermittlung, der Lehrorganisation
und der Lehrerfolgskontrolle im betreffenden Kurs.
Dargestellt werden hier persönliche Beiträge zu
Aktivitäten wie Foren, Wikis, Blogs oder Aufgaben.

Capturing Data – Example Code
Sample variable traces (Gleaner format)

Capturing Data – Example Code
Logging using Google Analytics

Outline

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

Outline

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
onlinelearninginsights.wordpress.com

Game Verbs
Gamedesign.glasslabgames.com

Outline

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

Visualization - Dashboards
Teachtown.com

StoryPlay Dashboard

GLEANER Dashboard

EngAGe Dashboard

Popular Analytics Tools
Piwik
Google Analytics
OpenSim Analytics for Virtual Worlds

Projects (some ongoing)
Lemo-projekt.de

Summary
Laila Shoukry, Stefan Göbel, Ralf Steinmetz:
Learning Analytics and Serious Games: Trends and Considerations.
In: SeriousGames '14 Proceedings of the ACM International Workshop on Serious Games,
p. 21-26, ACM MM’14, November 2014. ISBN 978-1-4503-3121-0.
http://dl.acm.org/citation.cfm?id=2656729.

Questions & Contact

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