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Curtin University is a trademark of Curtin University of Technology
CRICOS Provider Code 00301J
SeGAH 2017
Analytic and St...
Greater Curtin @ Perth
Learning Futures
 Develops strategic innovations that advance the
mission of the university
 Builds human and technologi...
Curtin Institute for Computation
Education Theme:
Build computational
skills across the
university
UNESCO Chair of
Data Science in Higher Education
Learning & Teaching
Aim of the Chair
To advance global knowledge, practice and policy
concerning the application of data science in the
transf...
Objectives of the UNESCO Chair
 Data Science
Collaboration
 Professional Learning
 Expand Open Education
 Ethical and ...
Today’s Agenda
 What do Serious Games teach?
 Data challenges of new psychometrics
 Learning analytics
 Educational re...
What do Serious Games teach?
A Series of Interesting Decisions Wrapped
with Fun and Competition
Immersive affordances
 Research-based learning progres...
Today’s Agenda
 What do Serious Games teach?
 Challenges of new psychometrics
 Learning analytics
 Educational researc...
About that data…
Why New Psychometrics
There is a need for new frameworks,
concepts and methods for measuring
what someone knows and can do...
New Psychometric Landscape
 A “do over” for performance
assessment
 New ways of performing & new
methods of data capture...
New Space for Performance
 Unfold in time
 Cover a multivariate space of possible actions
 Assets contain both intangib...
Performance Space Features
 Unconstrained complex multidimensional stimuli and
responses
 Dynamic adaptation of items to...
New Analysis Perspectives
 Hypothetico-deductive methods cannot induce or discover
a new hypothesis or rule to explain pa...
Types of Evidence
 Intentional (e.g. constructed responses, whole documents,
short answers, writing, speaking, tool utili...
Analysis Concepts
 Segmentation of time into events, slices, episodes, activity
segments or n-grams, which are atomistic ...
Adaptive Performance Assessments
 Assessments can now be fully embedded and
synonymous with adaptive changes in the digit...
Analysis Challenges of a Multidimensional
Landscape
 Time (e.g. historical preconditions, longitudinal
data, recurring pa...
Today’s Agenda
 What do Serious Games teach?
 Data challenges of new psychometrics
 Learning analytics
 Educational re...
Learning Theory Framework
for Software Agents
Content
that adapts
to group
and
individual
profiles
Agents for
selecting,
p...
Learning analytics
 Educational data mining (EDM) refers to the process of
extracting useful information out of a large c...
Learning analytics
 Learning analytics use static and dynamic information
about learners and learning environments - asse...
Types of Analytics
The different types of analytics can be thought of as a continuum
with increasing value and difficulty....
Today’s Agenda
 What do Serious Games teach?
 Data challenges of new psychometrics
 Learning analytics
 Educational re...
The Vees (there are a few more but these will do)
Another view of the process
What is Complexity?
 A characteristic of an agent or system
(complexity is not a thing, it exists via relationships)
 Co...
Today’s Agenda
 What do Serious Games teach?
 Data challenges of new psychometrics
 Learning analytics
 Educational re...
34
Blackboard
StudentOne
Online Library
Cluster Method of Data Integration for Insight
Condense, classify and map
hypothes...
• A unit of one – the student
• Micro-segmentation is appropriate
• Techinique is Kohonen or Self Organising
Map (SOM) thr...
Student A Semantic Network
Analysis
Student C
Semantic Network
Analysis
Semantics
Structure
Surface Matching
Structural
Matching
Concept Matching
Propositional
Matching
Balanced
Semantic Matchin...
Today’s Agenda
 What do Serious Games teach?
 Data challenges of new psychometrics
 Learning analytics
 Educational re...
Learning for Tomorrow
Technology
Project SummariesCurtin University is a trademark of
Curtin University of Technology
CRIC...
Build a Distributed Learning Analytics
Capacity
(Ifenthaler, 2015)
THANK YOU
david.c.gibson@curtin.edu.au
Learning Futures @ Curtin University
Inspiring and supporting
innovation, excellenc...
Analytic and strategic challenges of serious games
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Analytic and strategic challenges of serious games

How higher education learning and teaching can learn from serious game developers. Keynote at the 5th annual SeGAH conference concurrent with WWW 2017 held in Perth, Western Australia

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Analytic and strategic challenges of serious games

  1. 1. Curtin University is a trademark of Curtin University of Technology CRICOS Provider Code 00301J SeGAH 2017 Analytic and Strategic Challenges of Serious Games
  2. 2. Greater Curtin @ Perth
  3. 3. Learning Futures  Develops strategic innovations that advance the mission of the university  Builds human and technological capacity  Leads and manages early stage innovation projects : formal and informal learning innovations, pathways & partnerships, UniReady, learning analytics  Promotes faculty-based research and continuous improvement using learning analytics.
  4. 4. Curtin Institute for Computation
  5. 5. Education Theme: Build computational skills across the university
  6. 6. UNESCO Chair of Data Science in Higher Education Learning & Teaching
  7. 7. Aim of the Chair To advance global knowledge, practice and policy concerning the application of data science in the transformation of higher education learning and teaching toward improved personalization, access and effectiveness of education for all.
  8. 8. Objectives of the UNESCO Chair  Data Science Collaboration  Professional Learning  Expand Open Education  Ethical and Social Impacts  Multicultural & Interdisciplinary Research  Open Assessment Resources
  9. 9. Today’s Agenda  What do Serious Games teach?  Data challenges of new psychometrics  Learning analytics  Educational research with big data  Methods of educational data science  What higher education can learn
  10. 10. What do Serious Games teach?
  11. 11. A Series of Interesting Decisions Wrapped with Fun and Competition Immersive affordances  Research-based learning progressions  Epistemic challenges and experiences  Unobtrusive assessment and immediate feedback Which can teach-train-reinforce  Embodied Intelligence – Heuristics - Strategic thinking  Social Interaction Skills – Communication & Collaboration
  12. 12. Today’s Agenda  What do Serious Games teach?  Challenges of new psychometrics  Learning analytics  Educational research with big data  Methods of educational data science  What higher education can learn
  13. 13. About that data…
  14. 14. Why New Psychometrics There is a need for new frameworks, concepts and methods for measuring what someone knows and can do based on game interactions and artifacts created during serious play Why? Ubiquitous, unobtrusive, interactive big data created by people working in digital media performance spaces
  15. 15. New Psychometric Landscape  A “do over” for performance assessment  New ways of performing & new methods of data capture, analysis and display  Complex tasks create evidence: higher order thinking (e.g. decision sequences) physical performances demonstrating skills emotional responses
  16. 16. New Space for Performance  Unfold in time  Cover a multivariate space of possible actions  Assets contain both intangible (e.g. value, meaning, sensory qualities, and emotions) and tangible components (e.g. media, materials, time and space) NOTE: Asset utilization during performance provides evidence of what a user knows and can do
  17. 17. Performance Space Features  Unconstrained complex multidimensional stimuli and responses  Dynamic adaptation of items to user, which entails interactivity and dependency  Nonlinear behaviors with both temporal and spatial components NOTE: Higher order and creative thinking is supported in such a space
  18. 18. New Analysis Perspectives  Hypothetico-deductive methods cannot induce or discover a new hypothesis or rule to explain particular observations  Thus the need for data mining, network analysis, and probability-based methods to augment IRT  Themes: Data-driven Science & Complexity GOAL: To characterize and understand high-resolution multidimensional time-based data
  19. 19. Types of Evidence  Intentional (e.g. constructed responses, whole documents, short answers, writing, speaking, tool utilization, action sequences, utilization of help or scaffolds)  Unintentional (e.g. gestures, utterances, eye movements, affective states, time taken to respond)
  20. 20. Analysis Concepts  Segmentation of time into events, slices, episodes, activity segments or n-grams, which are atomistic points for aggregation  Segments must be recognizable in relationship to some structure of meaning – attributes in some frame  Components of event structure are identified as situations eliciting action-product learning trajectories made by users.
  21. 21. Adaptive Performance Assessments  Assessments can now be fully embedded and synonymous with adaptive changes in the digital learning environment  There is a thin line between formative and summative, primarily differentiated by the purposes and audiences of assessment
  22. 22. Analysis Challenges of a Multidimensional Landscape  Time (e.g. historical preconditions, longitudinal data, recurring patterns and autocorrelations)  Space (e.g. brain use patterns, neighbor effects, socioeconomic topologies)  Scale (e.g. neurons to social communities)  Dynamics (e.g. unique behavioral profiles even under highly similar conditions)
  23. 23. Today’s Agenda  What do Serious Games teach?  Data challenges of new psychometrics  Learning analytics  Educational research with big data  Methods of educational data science  What higher education can learn
  24. 24. Learning Theory Framework for Software Agents Content that adapts to group and individual profiles Agents for selecting, personalizin g, organizing & reusing Agents for translating, reformatting, time shifting, monitoring, summarizing Agents for critiquing, “just in time” feedback & adaptive testing Community AssessmentLearner Knowledge
  25. 25. Learning analytics  Educational data mining (EDM) refers to the process of extracting useful information out of a large collection of complex educational datasets (Romero, Ventura, Pechenizkiy, & Baker, 2011)  Academic analytics (AA) is the identification of meaningful patterns in educational data in order to inform academic issues (e.g., retention, success rates) and produce actionable strategies (e.g., budgeting, human resources) (Campbell, DeBlois, & Oblinger, 2010)
  26. 26. Learning analytics  Learning analytics use static and dynamic information about learners and learning environments - assessing, eliciting and analysing it - for real-time modelling, prediction, and optimisation of learning processes and learning environments (Ifenthaler, 2015)
  27. 27. Types of Analytics The different types of analytics can be thought of as a continuum with increasing value and difficulty. The type of analytics chosen will be dependent on the business value of the problem. Value Difficulty Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics What happened? Why did it happen? What will happen? What should I do?
  28. 28. Today’s Agenda  What do Serious Games teach?  Data challenges of new psychometrics  Learning analytics  Educational research with big data  Methods of educational data science  What higher education can learn
  29. 29. The Vees (there are a few more but these will do)
  30. 30. Another view of the process
  31. 31. What is Complexity?  A characteristic of an agent or system (complexity is not a thing, it exists via relationships)  Complicated, yes but more…  Surprising, yes but could be via simple rules…  Hovers (not sits) between chaos and order
  32. 32. Today’s Agenda  What do Serious Games teach?  Data challenges of new psychometrics  Learning analytics  Educational research with big data  Methods of educational data science  What higher education can learn
  33. 33. 34 Blackboard StudentOne Online Library Cluster Method of Data Integration for Insight Condense, classify and map hypotheses Surveys • eValuate • CASS • CEQ • School Classifications (constructed) Conduct analysis & interactively validate results Model Construction (Student Discovery Model) Analytical Data Set 10 Sources 300GB Data 12 billion data elements 51,181 Students 1273 Attributes 8 Clusters Voice of Business, Voice of Students and Voice of Data External Data Sets • Census • SES indexes • Geocoding
  34. 34. • A unit of one – the student • Micro-segmentation is appropriate • Techinique is Kohonen or Self Organising Map (SOM) through the Viscovery tool • SOM is a neural network • 1273 attributes viewable on the map • for each of the 51,181 Students in scope • Map built from 274 simultaneously considered attributes Attrition StudentsUnderlying statisticsClusters Map International Students Self-Organizing Map Model eValuate Sentiment + 1270 more attributes Blackboard Logins
  35. 35. Student A Semantic Network Analysis
  36. 36. Student C Semantic Network Analysis
  37. 37. Semantics Structure Surface Matching Structural Matching Concept Matching Propositional Matching Balanced Semantic Matching )( ief   2 !22 !    n n n )( iv ),,( kji vev   )(),(( ),,(),,,( .1.1, .2.2.2.1.1.1, mlBA kjikjiBA ees vevvevs       vi iu uv V i V    2 ,,  Gamma Matching Graphical Matching   ji ji eKSpTd , , (max (Ifenthaler, 2014; Ifenthaler & Pirnay-Dummer, 2014; Pirnay-Dummer, Ifenthaler, & Spector, 2010)
  38. 38. Today’s Agenda  What do Serious Games teach?  Data challenges of new psychometrics  Learning analytics  Educational research with big data  Methods of educational data science  What higher education can learn
  39. 39. Learning for Tomorrow Technology Project SummariesCurtin University is a trademark of Curtin University of Technology CRICOS Provider Code 00301J Page 9 The vision is for analytics to provide insight in domains that span the student experience: Leveraging internal knowledge and external datasets together to provide insight to economic, population and industry trends. Generating an understanding of students, their behaviours and experiences to better target, tailor and engage with them. Transformation of teaching & learning content in response to changes in student behaviour, desires and external factors. Measurement and rapid reaction to student interactions, leading to a dynamic adoption of best practice teaching and assessment techniques. Leverage of rich university datasets and analytical skillsets to promote depth and breadth of research, innovation and knowledge advancement. Current LearnersCommunity of Future Students Community of Advocates Market Analytics Curriculum Analytics Teaching Analytics Graduate AnalyticsLearner Analytics Education Analytics Domains learning experience; executed at a global scale. Track and Assist the Learner’s Journey The university needs to build staff leadership & capacity in data-driven decision-making across all of its delivery domains to promote game based (challenge based) learning.
  40. 40. Build a Distributed Learning Analytics Capacity (Ifenthaler, 2015)
  41. 41. THANK YOU david.c.gibson@curtin.edu.au Learning Futures @ Curtin University Inspiring and supporting innovation, excellence and impact in learning and teaching

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