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 Lab 09.06.2015 | 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


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

Leas-box.eu 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



GLEANER 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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