Learning Analytics and Serious Games: Trends and ... technology concepts ... • Description model for Serious Games ... Intelligence in Education, 17(2):121-144, 2007.

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<ul><li><p> author(s) of these slides including research results from the KOM research network and TU Darmstadt; otherwise it is specified at the respective slide </p><p>7-Nov-14 </p><p>Prof. Dr.-Ing. Ralf Steinmetz </p><p>KOM - Multimedia Communications Lab </p><p>ACM Workshop on Serious Games, Orlando, 2014 </p><p>Learning Analytics </p><p>and Serious Games: </p><p>Trends and Considerations </p><p>ACM Multimedia Serious Games Workshop Nov 7, 2014 </p><p>Laila Shoukry, M.Sc. Dr. Stefan Gbel </p><p>Laila.shoukry@kom.tu-darmstadt.de stefan.goebel@kom.tu-darmstadt.de </p><p>mailto:Laila.shoukry@kom.tu-darmstadt.demailto:Laila.shoukry@kom.tu-darmstadt.demailto:Laila.shoukry@kom.tu-darmstadt.demailto:stefan.goebel@kom.tu-darmstadt.demailto:stefan.goebel@kom.tu-darmstadt.demailto:stefan.goebel@kom.tu-darmstadt.de</p></li><li><p>KOM Multimedia Communications Lab 2 </p><p>Serious Games Team </p><p> Stefan Krepp (1.9.14) </p><p> Sabrina Radke </p><p> Robert Konrad </p><p> Christian Reuter, Florian Mehm, Michael Gutjahr, Sandro Hardy, Tim Dutz, Laila Shoukry, Stefan Gbel </p><p> Martin Knll Interdisciplinary research area UNICO, architecture Serious Games </p><p> Urban Health Games since 2011 15 groups EXIST uniworlds </p></li><li><p>KOM Multimedia Communications Lab 3 </p><p>Approach Game technology &amp; concepts </p><p>+ further RTD concepts application areas </p><p>Characteristics </p><p> Real data &amp; real users </p><p> Complex, interdisciplinary </p><p> Fun &amp; Characterizing Goal </p><p> Personalization &amp; adaptation </p><p> Authoring, control &amp; evaluation </p><p>Serious Games Games more than fun </p><p>SG, KWT 2014 </p></li><li><p>KOM Multimedia Communications Lab 4 </p><p>Serious Games Research Field </p><p>Overall Aim: Maximise effects &amp; fun </p><p>characterizing goal (health..) </p><p>user / game experience </p><p>Serious Game </p><p> Game state, game world Game Design, gameplay </p><p> Single / Multiplayer Offline / Online / Mobile </p><p>State Monitoring </p><p> (mobile) sensing Context awareness </p><p> Player state &amp; behaviour Psychophysiologic data </p><p>Knowledge Base </p><p> Description &amp; model for Serious Games Game patterns &amp; interaction templates </p><p> User profile, player / learner model (dynamic) Game Data, e.g. vital data </p><p> (domain) knowledge, situation/adaption base </p><p>Adaptation </p><p> Adaptive control Adaptive gameplay Difficulty adaptation Procedural Content </p><p>Adaptive </p><p>Serious Games </p></li><li><p>KOM Multimedia Communications Lab 5 </p><p>Outline Learning Analytics &amp; Serious Games </p></li><li><p>KOM Multimedia Communications Lab 6 </p><p>Definition What is Learning Analytics ? </p><p>http://edtechreview.in/event/87-webinar/835-can-learning-analytics-enable-personalized-learning </p><p>Learning analytics is the measurement, collection, </p><p>analysis and reporting of data about learners and </p><p>their contexts, for purposes of understanding and </p><p>optimizing learning and the environments in which </p><p>it occurs. George Siemens 2011 </p></li><li><p>KOM Multimedia Communications Lab 7 </p><p>Motivation </p><p>http://www.openequalfree.org/gamification-versus-game-based-learning-in-the-classroom/10082 </p><p>Why Learning Analytics &amp; Serious Games? </p><p> Evaluation of Serious Games </p><p> Justifying expense in learning contexts </p><p> Objective and cost-effective approach </p><p> Evaluation with Serious Games </p><p> Provide a big amount of gameplay data </p><p> Interactive and engaging nature </p><p> Stealth Assessment </p><p> Enable insight about learner attributes </p><p>and learning progress </p></li><li><p>KOM Multimedia Communications Lab 8 </p><p>Conceptual Approach Learning Analytics &amp; SG </p></li><li><p>KOM Multimedia Communications Lab 9 </p><p>Modelling for Learning Analytics in SG </p><p>https://www.linkedin.com/pulse/article/20140320222540-1265384-show-what-you-know-the-future-of-</p><p>competency-based-learning </p><p> Content </p><p> Competence-based Knowledge Space Theory </p><p>(CbKST) </p><p> Requires learning domains to be modelled as a </p><p>prerequisite competency structure </p><p> Users </p><p> Open Learner Model (OLM) </p><p> Presenting to the learner an understandable </p><p>visualization of his current knowledge state </p><p> Proven to improve learning outcomes </p><p> Player Model by Bartle </p><p> Achiever, Explorer, Killer, Socializer </p><p> Content &amp; Users </p><p> Narrative Game-Based Learning Objects (NGLOB) </p><p> Additionally considers player type and narrative </p><p>aspects </p><p> Triple vector: Narrative, Gaming and Learning Context </p></li><li><p>KOM Multimedia Communications Lab 10 </p><p>Conceptual Approach Learning Analytics &amp; SG </p></li><li><p>KOM Multimedia Communications Lab 11 </p><p>Choosing and Capturing Data I </p><p>Recording data depends </p><p>on </p><p> Learning goals, tasks </p><p>and setting </p><p> Game genre, mechanic </p><p>and platform </p><p> Single-Player vs. </p><p>Multiplayer </p><p> additional social </p><p>component in </p><p>collaborative learning </p><p> Fun vs. Learning </p><p>(effects) </p><p>Designing games with </p><p>analytics in mind </p><p>www.storytec.de StoryTec Authoring Environment with StoryPlay Learning Analytics Tool </p><p>http://www.storytec.de/</p></li><li><p>KOM Multimedia Communications Lab 12 </p><p>Choosing and Capturing Data II </p><p>Data modalities and interactions </p><p> Multimodal Learning Analytics </p><p> Includes biometric data and other multimodal </p><p>data for assessing motivation, fun and </p><p>collaboration aspects in learning settings </p><p> Mobile and Ubiquitous Learning Analytics </p><p> Data of mobile game-based learning appliances </p><p> Interaction with mobile devices </p><p> Considering contextual information </p></li><li><p>KOM Multimedia Communications Lab 13 </p><p>Conceptual Approach Learning Analytics &amp; SG </p></li><li><p>KOM Multimedia Communications Lab 14 </p><p>Aggregating and Analyzing Data </p><p>Aggregation Model </p><p> using semantic rules to map game actions or states to meaningful (machine-readable) </p><p>expressions under which similar events are grouped </p><p>Analyzing data depends on learning context and application </p><p> By instructor (via browser/analyzer) </p><p> Automatic Analysis (for intelligent tutoring systems and adaptive Serious Games) </p><p> Measures to be derived: </p><p> Gaming: general in-game performance, in-game learning, in-game strategies, </p><p>player type </p><p> Learning: general traits and abilities of the learner, general knowledge, situation-</p><p>specific state, learning behaviors, learning outcomes </p><p> Rules and algorithms (applied during learning sessions) governing the interpretation of </p><p>in-game sources of evidence to infer competencies and to update competency models </p><p> Data Mining and Machine Learning approaches can be used for identifying solution </p><p>strategies, error patterns and player goals </p></li><li><p>KOM Multimedia Communications Lab 18 </p><p>Conceptual Approach Learning Analytics &amp; SG </p></li><li><p>KOM Multimedia Communications Lab 19 </p><p>Deploying Results for Learning Analytics in SG </p><p>Visualization </p><p> visualizations of narrative structure, </p><p>player model and skill tree </p><p> graphs, Hasse Diagrams, Heat Maps </p><p> for games, a special need for real-time </p><p>operation, extensibility and </p><p>interoperability </p><p>Adaptation </p><p> macro-adaptivity: system responds by </p><p>choosing the appropriate next learning </p><p>object or narrative event </p><p> micro-adaptivity: adjusting aspects </p><p>within a learning task like task diffculty </p><p>or feedback type </p></li><li><p>KOM Multimedia Communications Lab 21 </p><p>Questions &amp; Contact </p></li><li><p>KOM Multimedia Communications Lab 22 </p><p>References </p><p> 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 </p><p>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, </p><p>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 </p><p>International Conference on Computer Supported Collaborative Learning Volume 1, CSCL'09, pages 58{67. International Society of the Learning Sciences, 2009. </p><p> 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. </p><p> J.-C. Falmagne, D. Albert, C. Doble, D. Eppstein, and X. Hu. Knowledge Spaces: Applications in Education. Springer Science &amp; Business, 2013. </p><p> 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. </p><p> 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 </p><p>Development, and Healthcare, page 105, 2013. </p></li><li><p>KOM Multimedia Communications Lab 23 </p><p>References </p><p> 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. </p><p> S. L. . B. H. Jan Plass. Assessment mechanics: design of learning activities that produce meaningful data.New York University, CREATE Lab, Apr. 2013. </p><p> 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. </p><p> 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. </p><p> 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 </p><p>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 </p><p>PSLC DataShop. Handbook of educational data mining, pages 43-55, 2010. J. Konert, K. Richter, F. Mehm, S. Gobel, R. Bruder, and R. Steinmetz. Pedale - a peer education diagnostic and learning </p><p>environment. Journal of Educational Technology &amp; Society, 2012. K. Korossy. Modeling knowledge as competence and performance. Knowledge spaces: Theories, empirical research, and </p><p>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 </p><p>for Intelligent Tutoring Systems, page 201, 2013. S. M. Letourneau. Intensive data at CREATE -Research approaches &amp; future directions. In Simulations and Games Learning </p><p>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 </p><p>Education, 2004. A. Mitrovic and B. Martin. Evaluating the eect of open student models on self-assessment. International Journal of Articial </p><p>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 </p><p>Views in Exploratory Visualization, 2006. Proceedings. International Conference on, pages 91-102. IEEE,2006. </p></li><li><p>KOM Multimedia Communications Lab 24 </p><p>References </p><p> K. Nurmela, E. Lehtinen, and T. Palonen. Evaluating CSCL log les by social network analysis. 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