O slideshow foi denunciado.
Utilizamos seu perfil e dados de atividades no LinkedIn para personalizar e exibir anúncios mais relevantes. Altere suas preferências de anúncios quando desejar.
© author(s) of these slides including research results from the KOM research network and TU Darmstadt; otherwise it is spe...
KOM – Multimedia Communications Lab 2
Motivation and Definitions
 Learning Analytics
 Game Analytics
 Serious Games Ana...
KOM – Multimedia Communications Lab 3
Pop Quiz!
Pressenza.com
KOM – Multimedia Communications Lab 4
Pop Quiz!
echtlustig.com
Wikihow.com
KOM – Multimedia Communications Lab 5
Assessment of Learning
http://assessment.uconn.edu/
KOM – Multimedia Communications Lab 6
Assessment for/in/of Learning?
syllabus.bos.nsw.edu.au
KOM – Multimedia Communications Lab 7
Assessment for/in/of Learning?
Open Learner Model (OLM)
 Presenting to the learner
...
KOM – Multimedia Communications Lab 8
What is Learning Analytics (LA)
edtechreview.in/
“Learning Analytics is the measurem...
KOM – Multimedia Communications Lab 9
 More accessible, affordable and functional
Technology
 Devices able to collect, s...
KOM – Multimedia Communications Lab 10
Learning Analytics (LA)
vs Educational Data Mining (EDM)
KOM – Multimedia Communications Lab 11
Learning Analytics Dimensions
(Greller & Drachsler, 2012)
KOM – Multimedia Communications Lab 12
Learning Analytics - Example
Einmaleins.tu-graz.at
KOM – Multimedia Communications Lab 13
Learning Analytics - Example
Einmaleins.tu-graz.at
KOM – Multimedia Communications Lab 14
Learning Analytics - Example
Einmaleins.tu-graz.at
KOM – Multimedia Communications Lab 15
Learning Analytics - Example
Einmaleins.tu-graz.at
KOM – Multimedia Communications Lab 16
Learning Analytics - Example
Einmaleins.tu-graz.at
KOM – Multimedia Communications Lab 17
Game Analytics
northeastern.edu
“All forms of business intelligence data, and
any k...
KOM – Multimedia Communications Lab 18
Game Analytics - Example
steampowered.com/status/ep2/ep2_stats.php
KOM – Multimedia Communications Lab 19
Game Analytics - Example
steampowered.com/status/ep2/ep2_stats.php
KOM – Multimedia Communications Lab 20
Game Analytics - Example
steampowered.com/status/ep2/ep2_stats.php
KOM – Multimedia Communications Lab 21
Game Analytics - Example
steampowered.com/status/ep2/ep2_stats.php
KOM – Multimedia Communications Lab 22
Game Analytics - Example
steampowered.com/status/ep2/ep2_stats.php
KOM – Multimedia Communications Lab 23
Goal of LA in Serious Games
openequalfree.org/gamification-versus-game-based-learni...
KOM – Multimedia Communications Lab 24
Serious Games Analytics
Learning Analytics
 “The measurement, collection, analysis...
KOM – Multimedia Communications Lab 25
Outline
KOM – Multimedia Communications Lab 26
Modelling for LA
Baalsrud et al. (2014)
KOM – Multimedia Communications Lab 27
Modelling for Learning Analytics in SG
 Competence-Based Knowledge Space Theory (C...
KOM – Multimedia Communications Lab 28
Competence-Based Knowledge Space Theory
(CbKST)
 Knowledge Domain: set of skills r...
KOM – Multimedia Communications Lab 29
Flow
Kiili et al. (2014)
Flow Theory , Csikszentmihalyi 1988,
Zone of Proximal Deve...
KOM – Multimedia Communications Lab 30
Authoring
 Annotation of Situations
 Narrative, Gaming and Learning Context
 App...
KOM – Multimedia Communications Lab 31
Evidence-Centered Design (ECD)
KOM – Multimedia Communications Lab 32
Outline
KOM – Multimedia Communications Lab 33
Choosing Data for Learning Analytics in SG
Depends on learning goals, setting, task...
KOM – Multimedia Communications Lab 34
Serious Games Analytics
Serrano-Leguana et al. 2014
Measures to be derived:
 Gamin...
KOM – Multimedia Communications Lab 35
Outline
KOM – Multimedia Communications Lab 36
Capturing Data for Learning Analytics in SG
Depends on data modalities and interact...
KOM – Multimedia Communications Lab 37
Privacy Concerns
Collectiveevolution.com
Nutzungsbedingungen für E-Learnin an der H...
KOM – Multimedia Communications Lab 38
Capturing Data – Example Code
Sample variable traces (Gleaner format)
KOM – Multimedia Communications Lab 39
Capturing Data – Example Code
Logging using Google Analytics
KOM – Multimedia Communications Lab 40
Outline
KOM – Multimedia Communications Lab 41
Aggregating Data for Learning Analytics in SG
Depends on data sources and sample si...
KOM – Multimedia Communications Lab 42
Outline
KOM – Multimedia Communications Lab 43
Analyzing Data for Learning Analytics in SG
Depends on learning context and applica...
KOM – Multimedia Communications Lab 44
Game Verbs
Gamedesign.glasslabgames.com
KOM – Multimedia Communications Lab 45
Outline
KOM – Multimedia Communications Lab 46
Deploying Results for Learning Analytics in SG
Depends on learning context and
appl...
KOM – Multimedia Communications Lab 47
Visualization - Dashboards
Teachtown.com
KOM – Multimedia Communications Lab 48
Visualization - Dashboards
StoryPlay Dashboard
KOM – Multimedia Communications Lab 49
Visualization - Dashboards
GLEANER Dashboard
KOM – Multimedia Communications Lab 50
Visualization - Dashboards
GLEANER Dashboard
KOM – Multimedia Communications Lab 51
Visualization - Dashboards
GLEANER Dashboard
KOM – Multimedia Communications Lab 52
Visualization - Dashboards
EngAGe Dashboard
KOM – Multimedia Communications Lab 53
Popular Analytics Tools
Piwik
Google Analytics
OpenSim Analytics for Virtual Worlds
KOM – Multimedia Communications Lab 54
Projects (some ongoing)
Lemo-projekt.de
KOM – Multimedia Communications Lab 55
Summary
Laila Shoukry, Stefan Göbel, Ralf Steinmetz:
Learning Analytics and Serious...
KOM – Multimedia Communications Lab 56
Questions & Contact
KOM – Multimedia Communications Lab 57
References
• N. R. Aljohani and H. C. Davis. Learning analytics in mobile and ubiqu...
KOM – Multimedia Communications Lab 58
References
• S. Goebel, V. Wendel, C. Ritter, and R. Steinmetz. Personalized, adapt...
KOM – Multimedia Communications Lab 59
References
• K. Nurmela, E. Lehtinen, and T. Palonen. Evaluating CSCL log les by so...
KOM – Multimedia Communications Lab 60
References
• Baalsrud Hauge, J., Berta, R., Fiucci, G., Fernandez Manjon, B., Padró...
Próximos SlideShares
Carregando em…5
×

2

Compartilhar

Baixar para ler offline

Serious Games Analytics - Lecture at TU Darmstadt

Baixar para ler offline

A lecture I gave in Summer Semester 2015 at TU Darmstadt on 09.06.2015

Audiolivros relacionados

Gratuito durante 30 dias do Scribd

Ver tudo

Serious Games Analytics - Lecture at TU Darmstadt

  1. 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 Multimedia Communications Lab09.06.2015 | Serious Games – Serious Games Analytics| 9 Serious Games Analytics Laila Shoukry, M.Sc.
  2. 2. 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/
  3. 3. KOM – Multimedia Communications Lab 3 Pop Quiz! Pressenza.com
  4. 4. KOM – Multimedia Communications Lab 4 Pop Quiz! echtlustig.com Wikihow.com
  5. 5. KOM – Multimedia Communications Lab 5 Assessment of Learning http://assessment.uconn.edu/
  6. 6. KOM – Multimedia Communications Lab 6 Assessment for/in/of Learning? syllabus.bos.nsw.edu.au
  7. 7. KOM – Multimedia Communications Lab 7 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
  8. 8. KOM – Multimedia Communications Lab 8 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
  9. 9. KOM – Multimedia Communications Lab 9  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
  10. 10. KOM – Multimedia Communications Lab 10 Learning Analytics (LA) vs Educational Data Mining (EDM)
  11. 11. KOM – Multimedia Communications Lab 11 Learning Analytics Dimensions (Greller & Drachsler, 2012)
  12. 12. KOM – Multimedia Communications Lab 12 Learning Analytics - Example Einmaleins.tu-graz.at
  13. 13. KOM – Multimedia Communications Lab 13 Learning Analytics - Example Einmaleins.tu-graz.at
  14. 14. KOM – Multimedia Communications Lab 14 Learning Analytics - Example Einmaleins.tu-graz.at
  15. 15. KOM – Multimedia Communications Lab 15 Learning Analytics - Example Einmaleins.tu-graz.at
  16. 16. KOM – Multimedia Communications Lab 16 Learning Analytics - Example Einmaleins.tu-graz.at
  17. 17. KOM – Multimedia Communications Lab 17 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
  18. 18. KOM – Multimedia Communications Lab 18 Game Analytics - Example steampowered.com/status/ep2/ep2_stats.php
  19. 19. KOM – Multimedia Communications Lab 19 Game Analytics - Example steampowered.com/status/ep2/ep2_stats.php
  20. 20. KOM – Multimedia Communications Lab 20 Game Analytics - Example steampowered.com/status/ep2/ep2_stats.php
  21. 21. KOM – Multimedia Communications Lab 21 Game Analytics - Example steampowered.com/status/ep2/ep2_stats.php
  22. 22. KOM – Multimedia Communications Lab 22 Game Analytics - Example steampowered.com/status/ep2/ep2_stats.php
  23. 23. KOM – Multimedia Communications Lab 23 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
  24. 24. KOM – Multimedia Communications Lab 24 Serious Games Analytics 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 Serious Games Analytics  “Methods for learners' gameplay data collection, analysis and visualization to be used in Serious Games research.” Christian Loh 2014
  25. 25. KOM – Multimedia Communications Lab 25 Outline
  26. 26. KOM – Multimedia Communications Lab 26 Modelling for LA Baalsrud et al. (2014)
  27. 27. KOM – Multimedia Communications Lab 27 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
  28. 28. KOM – Multimedia Communications Lab 28 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
  29. 29. KOM – Multimedia Communications Lab 29 Flow Kiili et al. (2014) Flow Theory , Csikszentmihalyi 1988, Zone of Proximal Development, Vygotsky
  30. 30. KOM – Multimedia Communications Lab 30 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)
  31. 31. KOM – Multimedia Communications Lab 31 Evidence-Centered Design (ECD)
  32. 32. KOM – Multimedia Communications Lab 32 Outline
  33. 33. KOM – Multimedia Communications Lab 33 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
  34. 34. KOM – Multimedia Communications Lab 34 Serious Games Analytics 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
  35. 35. KOM – Multimedia Communications Lab 35 Outline
  36. 36. KOM – Multimedia Communications Lab 36 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
  37. 37. KOM – Multimedia Communications Lab 37 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.
  38. 38. KOM – Multimedia Communications Lab 38 Capturing Data – Example Code Sample variable traces (Gleaner format)
  39. 39. KOM – Multimedia Communications Lab 39 Capturing Data – Example Code Logging using Google Analytics
  40. 40. KOM – Multimedia Communications Lab 40 Outline
  41. 41. KOM – Multimedia Communications Lab 41 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
  42. 42. KOM – Multimedia Communications Lab 42 Outline
  43. 43. KOM – Multimedia Communications Lab 43 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
  44. 44. KOM – Multimedia Communications Lab 44 Game Verbs Gamedesign.glasslabgames.com
  45. 45. KOM – Multimedia Communications Lab 45 Outline
  46. 46. KOM – Multimedia Communications Lab 46 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
  47. 47. KOM – Multimedia Communications Lab 47 Visualization - Dashboards Teachtown.com
  48. 48. KOM – Multimedia Communications Lab 48 Visualization - Dashboards StoryPlay Dashboard
  49. 49. KOM – Multimedia Communications Lab 49 Visualization - Dashboards GLEANER Dashboard
  50. 50. KOM – Multimedia Communications Lab 50 Visualization - Dashboards GLEANER Dashboard
  51. 51. KOM – Multimedia Communications Lab 51 Visualization - Dashboards GLEANER Dashboard
  52. 52. KOM – Multimedia Communications Lab 52 Visualization - Dashboards EngAGe Dashboard
  53. 53. KOM – Multimedia Communications Lab 53 Popular Analytics Tools Piwik Google Analytics OpenSim Analytics for Virtual Worlds
  54. 54. KOM – Multimedia Communications Lab 54 Projects (some ongoing) Lemo-projekt.de
  55. 55. KOM – Multimedia Communications Lab 55 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.
  56. 56. KOM – Multimedia Communications Lab 56 Questions & Contact
  57. 57. KOM – Multimedia Communications Lab 57 References • N. R. Aljohani and H. C. Davis. Learning analytics in mobile and ubiquitous learning environments. In 11th • World Conference on Mobile and Contextual Learning: mLearn 2012, Helsinki, Finland, 2012. • R. S. J. D. Baker and K. Yacef. The State of Educational Data Mining in 2009 : A Review and Future Visions. 1(1):3-16, 2009. • P. Blikstein. Multimodal learning analytics. Proceedings of the Third International Conference on • Learning Analytics and Knowledge - LAK '13, page 102, 2013. • S. Bull, Y. C. Y. Cui, a.T. McEvoy, E. Reid, and W. Y. W. Yang. Roles for mobile learner models. The 2nd IEEE International Workshop on Wireless and Mobile Technologies in Education, 2004. Proceedings., pages 124-128, 2004. • S. Bull and J. Kay. Open learner models. In Advances in Intelligent Tutoring Systems, pages 301-322. Springer, 2010. • G. K. Chung and D. S. Kerr. A Primer on Data Logging to Support Extraction of Meaningful • Information from Educational Games: An Example from Save Patch. CRESST Report 814. National • Center for Research on Evaluation, Standards, and Student Testing (CRESST), page 27, 2012. • A. Cooper. Learning Analytics Interoperability – The Big Picture In Brief. Learning Analytics Community Exchange, 2014. • E. Duval. Attention Please!: Learning Analytics for Visualization and Recommendation. LAK '11, pages 9-17, New York, NY, USA, 2011. ACM. • A. Dyckho and D. Zielke. Design and Implementation of a Learning Analytics Toolkit for Teachers. Journal of . . . , 15:58-76, 2012. • G. Dyke, K. Lund, and J.-J. Girardot. Tatiana: An Environment to Support the CSCL Analysis Process. In Proceedings of the 9th International Conference on Computer Supported Collaborative Learning – Volume 1, CSCL'09, pages 58{67. International Society of the Learning Sciences, 2009. • B. M. Eun Young Ha Jonathan Rowe and J. Lester. Recognizing Player Goals in Open-Ended Digital Games with Markov Logic Networks. Plan, Activity and Intent Recognition: Theory and Practice, 2014. • J.-C. Falmagne, D. Albert, C. Doble, D. Eppstein, and X. Hu. Knowledge Spaces: Applications in Education. Springer Science & Business, 2013. • I. Garcia, A. Duran, and M. Castro. Comparing the eectiveness of evaluating practical capabilities through hands-on on-line exercises versus conventional methods. In Frontiers in Education Conference, 2008. • FIE 2008. 38th Annual, pages F4H{18{F4H{22, Oct • 2008. • S. Goebel, M. Gutjahr, and S. Hardy. Evaluation of Serious Games. Serious Games and Virtual Worlds in Education, Professional Development, and Healthcare, page 105, 2013. • Kiili, K., Lainema, T., de Freitas, S., & Arnab, S. (2014). Flow framework for analyzing the quality of educational games. Entertainment Computing, 5(4), 367-377.
  58. 58. KOM – Multimedia Communications Lab 58 References • S. Goebel, V. Wendel, C. Ritter, and R. Steinmetz. Personalized, adaptive digital educational games using narrative game-based learning objects. pages 438-445. Springer, 2010. • S. L. . B. H. Jan Plass. Assessment mechanics: design of learning activities that produce meaningful data.New York University, CREATE Lab, Apr. 2013. • K. E. B. E. W. Joseph Grafsgaard Joseph Wiggins and J. Lester. Predicting Learning and Aect from Multimodal Data Streams in Task-Oriented Tutorial Dialogue. In Seventh International Conference on Educational Data Mining, 2014. • D. S. Kerr and G. K. W. K. Chung. Using Cluster Analysis to Extend Usability Testing to Instructional Content. CRESST Report 816. National Center for Research on Evaluation, Standards, and Student Testing (CRESST), May 2012. • M. D. Kickmeier-Rust and D. Albert. An Alien's Guide to Multi-Adaptive Educational Computer Games. Informing Science, 2012. • M. D. Kickmeier-Rust and D. Albert. Learning analytics to support the use of virtual worlds in the classroom. In Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data, pages 358{365. Springer, 2013. • K. R. Koedinger, R. Baker, K. Cunningham, A. Skogsholm, B. Leber, and J. Stamper. A data repository for the EDM community: The PSLC DataShop. Handbook of educational data mining, pages 43-55, 2010. • J. Konert, K. Richter, F. Mehm, S. G•obel, R. Bruder, and R. Steinmetz. Pedale - a peer education diagnostic and learning environment. Journal of Educational Technology & Society, 2012. • K. Korossy. Modeling knowledge as competence and performance. Knowledge spaces: Theories, empirical research, and applications, pages 103{132, 1999. • J. Lester, B. Mott, J. Rowe, and J. Sabourin. {learner modeling to predict real-time aect in serious games. Design Recommendations for Intelligent Tutoring Systems, page 201, 2013. • S. M. Letourneau. Intensive data at CREATE -Research approaches & future directions. In Simulations and Games Learning Analytics and Data Mining Workshop, 2013. • R. Levy. Dynamic Bayesian Network Modeling of Game Based Diagnostic Assessments. CRESST Report, 2014. • C. Martin, O. Galibert, M. Michel, F. Mougin, and V. Stanford. NIST Smart Flow System User's guide. • M. Minovic and M. Milovanovic. Real-time learning analytics in educational games. pages 245{251. ACM, • 2013. • R. J. Mislevy, R. G. Almond, and J. F. Lukas. A brief introduction to evidence-centered design. cse report 632. US Department of Education, 2004. • A. Mitrovic and B. Martin. Evaluating the eect of open student models on self-assessment. International Journal of Articial Intelligence in Education, 17(2):121-144, 2007. • A. Morrison, P. Tennent, and M. Chalmers. Coordinated visualisation of video and system log data. In Coordinated and Multiple Views in Exploratory Visualization, 2006. Proceedings. International Conference on, pages 91-102. IEEE,2006.
  59. 59. KOM – Multimedia Communications Lab 59 References • K. Nurmela, E. Lehtinen, and T. Palonen. Evaluating CSCL log les by social network analysis. In Proceedings of the 1999 conference on Computer support for collaborative learning, page 54. International Society of the Learning Sciences, 1999. • H. Ogata, M. Li, B. Hou, N. Uosaki, M. M. El-Bishouty, and Y. Yano. SCROLL: Supporting to share and reuse ubiquitous learning log in the context of language learning. Research & Practice in Technology Enhanced Learning, 6(2), 2011. • S. Oviatt, A. Cohen, and N. Weibel. Multimodal learning analytics: Description of math data corpus for icmi grand challenge workshop. In Proceedings of the 15th ACM on International conference on multimodal interaction, pages 563{568. ACM, 2013. • J. L. Plass, B. D. Homer, C. K. Kinzer, Y. K. Chang, J. Frye, W. Kaczetow, K. Isbister, and K. Perlin. Metrics in Simulations and Games for Learning. In Game Analytics, pages 697{729. Springer, 2013. • M. Prensky. Computer games and learning: Digital game-based learning. Handbook of computer game studies, 18:97-122, 2005. • C. Reuter, F. Mehm, S. G•obel, and R. Steinmetz. Evaluation of Adaptive Serious Games using Playtraces and Aggregated Play Data. Proceedings of the 7th European Conference on Game Based Learning (ECGBL) 2013, (October):504{511, 2013. • A. Serrano, E. J. Marchiori, A. del Blanco, J. Torrente, and B. Fernandez-Manjon. A framework to improve evaluation in educational games. In Global Engineering Education Conference (EDUCON), 2012 IEEE, pages 1-8. IEEE, 2012. • A. Serrano-Laguna, J. Torrente, P. Moreno-Ger, and B. Fernandez-Manjon. Application of learning analytics in educational videogames. Entertainment Computing, 2014. • V. J. Shute, M. Ventura, M. Bauer, and D. Zapata-Rivera. Melding the power of serious games and embedded assessment to monitor and foster learning. Serious games: Mechanisms and eects, • pages 295-321, 2009. • G. Siemens and R. S. J. Baker. Learning Analytics and Educational Data Mining : Towards Communication and Collaboration. 2010. • K. K. Tatsuoka. Architecture of knowledge structures and cognitive diagnosis: A statistical pattern recognition and classication approach. Cognitively diagnostic assessment, pages 327-359, 1995. • T. P. Vendlinski, G. C. Delacruz, R. E. Buschang, G. Chung, and E. L. Baker. Developing high-quality assessments that align with instructional video games (CRESST Report 774). . National Center for Research on Evaluation, Standards, and Student Testing, 2010. • A. V. Wallop. Althea Vail Wallop CS 229 Final Project Report Learning to Predict Disengagement in Educational iPad Application. pages 1-5, 2010. • J.-D. Zapata-Rivera and J. E. Greer. Interacting with inspectable bayesian student models. International Journal of Articial Intelligence in Education, 14(2):127-163, 2004.
  60. 60. KOM – Multimedia Communications Lab 60 References • Baalsrud Hauge, J., Berta, R., Fiucci, G., Fernandez Manjon, B., Padrón-Nápoles, C., Westra, W., & Nadolski, R. (2014, July). Implications of learning analytics for serious game design. In Advanced Learning Technologies (ICALT), 2014 IEEE 14th International Conference on (pp. 230-232). IEEE.
  • AlexeyKruchenok1

    Apr. 21, 2018
  • fva17

    Aug. 17, 2015

A lecture I gave in Summer Semester 2015 at TU Darmstadt on 09.06.2015

Vistos

Vistos totais

1.150

No Slideshare

0

De incorporações

0

Número de incorporações

7

Ações

Baixados

9

Compartilhados

0

Comentários

0

Curtir

2

×