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Learning Analytics and Serious Games: Trends and Considerations

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

Publicada em: Tecnologia
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Learning Analytics and Serious Games: Trends and Considerations

  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 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.
  2. 2. KOM – Multimedia Communications Lab 2 Outline
  3. 3. KOM – Multimedia Communications Lab 3 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
  4. 4. KOM – Multimedia Communications Lab 4 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
  5. 5. KOM – Multimedia Communications Lab 5 Outline
  6. 6. KOM – Multimedia Communications Lab 6 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
  7. 7. KOM – Multimedia Communications Lab 7 Outline
  8. 8. KOM – Multimedia Communications Lab 8 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
  9. 9. KOM – Multimedia Communications Lab 9 Outline
  10. 10. KOM – Multimedia Communications Lab 10 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
  11. 11. KOM – Multimedia Communications Lab 11 Outline
  12. 12. KOM – Multimedia Communications Lab 12 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
  13. 13. KOM – Multimedia Communications Lab 13 Outline
  14. 14. KOM – Multimedia Communications Lab 14 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
  15. 15. KOM – Multimedia Communications Lab 15 Outline
  16. 16. KOM – Multimedia Communications Lab 16 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
  17. 17. KOM – Multimedia Communications Lab 17 Popular Analytics Tools Piwik Google Analytics OpenSim Analytics for Virtual Worlds
  18. 18. KOM – Multimedia Communications Lab 18 Questions & Contact
  19. 19. KOM – Multimedia Communications Lab 19 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.
  20. 20. KOM – Multimedia Communications Lab 20 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.
  21. 21. KOM – Multimedia Communications Lab 21 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.
  22. 22. KOM – Multimedia Communications Lab 22 Websites http://www.zoodles.com http://www.google.com/analytics http://piwik.org/ http://secondlife.com/ http://opensimulator.org/

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