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Suggested Readings

  1. Takumi Toyama, Daniel Sonntag, Jason Orlosky, and Kiyoshi Kiyokawa. 2015. Attention Engagement and Cognitive State Analysis for Augmented Reality Text Display Functions. InProceedings of the 20th International Conference on Intelligent User Interfaces (IUI '15). ACM, New York, NY, USA, 322-332. (link). <Suggested by Sébastien>
  2. Oswald Barral, Manuel J.A. Eugster, Tuukka Ruotsalo, Michiel M. Spapé, Ilkka Kosunen, Niklas Ravaja, Samuel Kaski, and Giulio Jacucci. 2015. Exploring Peripheral Physiology as a Predictor of Perceived Relevance in Information Retrieval. In Proceedings of the 20th International Conference on Intelligent User Interfaces (IUI '15). ACM, New York, NY, USA, 389-399. (link). <Suggested by Sébastien>
  3. [Short Paper] Beate Grawemeyer, Wayne Holmes, Sergio Gutiérrez-Santos, Alice Hansen, Katharina Loibl, and Manolis Mavrikis. 2015. Light-Bulb Moment?: Towards Adaptive Presentation of Feedback based on Students' Affective State. In Proceedings of the 20th International Conference on Intelligent User Interfaces (IUI '15). ACM, New York, NY, USA, 400-404. (link). <Suggested by Sébastien>
  4. [Short Paper] Mathieu Rodrigue, Jungah Son, Barry Giesbrecht, Matthew Turk, and Tobias Höllerer. 2015. Spatio-Temporal Detection of Divided Attention in Reading Applications Using EEG and Eye Tracking. In Proceedings of the 20th International Conference on Intelligent User Interfaces (IUI '15). ACM, New York, NY, USA, 121-125. (link). <Suggested by Sébastien>

  Adaptive Support
  1. Frias-Martinez, Chen, and Liu. Evaluation of a personalized digital library based on cognitive styles: Adaptivity vs. adaptability. International Journal of Information Management, 2008. 9 pages. <suggested by Matt: examines impact of cognitive styles on adaptive/adaptable interfaces; analyzed a 60-participant user study>
  2. Kong and Agrawala. Graphical Overlays: Using Layered Elements to Aid Chart Reading. Visualization and Computer Graphics. (pdf link, 8 pages). <suggested by Matt: I'm not sure if this paper fits this category as it does not involve user modeling, but it presents a system for automatically adding supporting overlays to bitmap graphs which is highly relevant to the ATUAV project>

  Learning @ Scale
  1. Yang, D., Wen, M., Howley, I., Kraut, R., & Rose, C. (2015, March). Exploring the effect of confusion in discussion forums of massive open online courses. InProceedings of the Second (2015) ACM Conference on Learning@ Scale (pp. 121-130). ACM. (link) <suggested by Lauren>

  User Goal Classification <suggested by Matt>
  1. Inferring User Image-Search Goals Under the Implicit Guidance of Users. Circuits and Systems for Video Technology, 2014. (IEEE link, 13 pages)
  2. A New Algorithm for Inferring User Search Goals with Feedback Sessions. Knowledge and Data Engineering, 2013 (IEEE link, 12 pages)  [Note that this paper is similar to the one suggested above (same authors)]
  3. What Are You Looking For? An Eye-tracking Study of Information Usage in Web Search. CHI 2007. (pdf, 10 pages) [already read]

  CHI 2014

  1. Kieras and Hornof, Towards Accurate and Practical Predictive Models of Active-Vision-Based Visual Search, CHI 2014 Session: Modelling Users and Interaction <suggested by Matt>
  2. Zhang and Hornof, Understanding Multitasking Through Parallelized Strategy Exploration and Individualized Cognitive Modeling, CHI 2014 Session: Modeling users and Interaction <suggested by Matt>
  3. Yang, Li, and Zhou, Understand Users' Comprehension and Preferences for Composing Information Visualizations, CHI 2014 Session: Studying Visualization (TOCHI presentation, 30 pages), http://dl.acm.org/citation.cfm?id=2582013.2541288 <suggested by Matt>
  4. Add suggestions here!

  IUI 2014
  1. Martinez-Gomez, P. & Aizawa, A. Recognition of Understanding Level and Language Skill Using Measurements of Reading Behaviour, IUI'14, pp.95-104. [Eye-tracking for User Modeling] <suggested by Matt>
  2. Perer, A. & Wang, F. Frequence: Interactive Mining and Visualization of Temporal Frequent Event Sequences, IUI'14, pp. 153-162. [Sequence Mining] <suggested by Dereck and Matt>
  3. Wan, S. & Paris, C. Improving Government Services with Social Media Feedback, IUI'14, pp. 27-36. [Visualization of Social Media Conversations; NLP] <suggested by Matt>
  4. Rosman, B.; Ramamoorthy, S.; Mahmud, M. M. H.; & Kohli, P. On User Behaviour Adaptation Under Interface Change, IUI'14, pp. 273-278 (short paper). [Adaptive User Interfaces] <suggested by Matt>
  5. Li, L. & Gajos, K. Adaptive Click-and-Cross: Adapting to Both Abilities and Task Improves Performance of Users With Impaired Dexterity, IUI'14, pp. 299-304 (short paper). [Adaptive User Interfaces] <suggested by Matt>
  6. Wang, F.; Li, Y.; Sakamoto, D.; & Igarashi, T. Hierarchical Route Maps for Efficient Navigation, IUI'14, pp. 169-178. [Best Paper winner]
  Modeling Learning and knowledge estimation
  1. Yanbo Xu and Jack Mostow, Comparison of methods to trace multiple subskills: Is LR-DBN best? EDM 2012 Best paper. [pdf] (8 pages) <Suggested by Samad>
  2. Pedro, M. A. S., Baker, R. S. J. de, Gobert, J. D., Montalvo, O., & Nakama, A. (2013). Leveraging machine-learned detectors of systematic inquiry behavior to estimate and predict transfer of inquiry skill. User Modeling and User-Adapted Interaction, 23(1), 1–39. 

  Modeling Interaction in Learning Environments
  1. Imène Jraidi, Maher Chaouachi, and Claude Frasson. 2013. A dynamic multimodal approach for assessing learners' interaction experience. In Proceedings of the 15th ACM on International conference on multimodal interaction (ICMI '13). <Suggested by Cristina>
  Machine Learning Techniques

  Eye Tracking for offline analysis:
  1. Halszka Jarodzka, Katharina Scheiter, Peter Gerjets, Tamara van Gog, In the eyes of the beholder: How experts and novices interpret dynamic stimuli, Learning and Instruction, Volume 20, Issue 2, April 2010, Pages 146-154, ISSN 0959-4752.
  2. Claudio M. Privitera , Lawrence W. Stark. Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.22 n.9, p.970-982, September 2000  [doi>10.1109/34.877520]
  3. Nacke, L.E., Stellmach, S., Sasse, D., Niesenhaus, J., Dachselt, R. 2010. LAIF: A Logging and Interaction Framework for Gaze-Based Interfaces in Virtual Entertainment Environments. In Entertainment Computing. DOI: 10.1016/j.entcom.2010.09.004
  4. David Gotz and Krist Wongsuphasawat. Interactive Intervention AnalysisTo Appear in American Medical Informatics Association Annual Symposium (AMIA), Chicago, IL (2012).  <Suggested by Dereck>
  5. Kules, B., and Capra, R. (2012). Influence of training and stage of search on gaze behavior in a library catalog faceted search interface. Journal of the American Society for Information Science and Technology, 63(1): 114-138. http://dx.doi.org/10.1002/asi.21647 <Suggested by Cristina>
  6. Jang, Mallipeddi, and Lee. Identification of human implicit visual search intention based on eye movements and pupillary analysis. UMUAI 2013. (Springer link).

  Online use of Eye-tracking


  Adaptive Visualization:

  Eye Tracking in InfoVis:

  1. Beyond Mouse and Keyboard: Expanding Design Considerations for Information Visualization Interactions, Bongshin Lee, Petra Isenberg, Nathalie Henry Riche, Sheelagh Carpendale, Infovis 2012. <Suggested by Ben>
  Providing Support/feedback to User:
  1. Walker, E., Rummel, N., & Koedinger, K. R. (2011). Designing automated adaptive support to improve student helping behaviors in a peer tutoring activity. International Journal of Computer-Supported Collaborative Learning, 6 (2), 279-306.<Suggested by Samad>
  2. Ilya Goldin, Kenneth Koedinger and Vincent Aleven, Learner Differences in Hint Processing, EDM 2012 [pdf] (8 pages). <Suggested by Samad>
  3. Erin Walker, Nikol Rummel, Sean Walker, Kenneth R. Koedinger:Noticing Relevant Feedback Improves Learning in an Intelligent Tutoring System for Peer Tutoring. 222-232 (10 pages) ITS 2012 <Suggested by Samad>
  4. Philippe Fournier-Viger, Roger Nkambou, André Mayers, Engelbert Mephu Nguifo, Usef Faghihi: Multi-paradigm Generation of Tutoring Feedback in Robotic Arm Manipulation Training. 233-242 (10 pages) ITS 2012 <Suggested by Samad>

  Modeling long-term, stable Cognitive and Meta cognitive Traits:

  1. Blair Lehman, Caitlin Mills, Sidney K. D'Mello, Arthur C. Graesser:Automatic Evaluation of Learner Self-Explanations and Erroneous Responses for Dialogue-Based ITSs. 541-550 (10 pages). Best student paper ITS 2012. <Suggested by Samad>
 Modeling short-term, transient user states and behaviors (Emotions, Cognitive Load, confusions, ...)
  1. Bryan Reimer, Bruce Mehler, Joseph F. Coughlin, Kathryn M. Godfrey, and Chuanzhong Tan. 2009. An on-road assessment of the impact of cognitive workload on physiological arousal in young adult drivers. In Proceedings of the 1st International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI '09). ACM, New York, NY, USA, 115-118. DOI=10.1145/1620509.1620531 http://doi.acm.org/10.1145/1620509.1620531
  2. R Rajendran, S Iyer, S Murthy, C Wilson, J Sheard, A Theory-Driven Approach to Predict Frustration in an ITS, IEEE Transactions on Learning …, 2013. (12 pages)  <Suggested by Cristina>
 Experiment design and data analysis

 Adaptive Games
  1. Shlomo Berkovsky, Jill Freyne, and Mac Coombe. 2012. Physical Activity Motivating Games: Be Active and Get Your Own Reward. ACM Trans. Comput.-Hum. Interact. 19, 4, Article 32 (December 2012), 41 pages. DOI=10.1145/2395131.2395139 http://doi.acm.org/10.1145/2395131.2395139
  2. David Thule and Vadim Bulitko Procedural Game Adaptation: Framing Experience Management as Changing an MDP (6 pages) <Suggested by Cristina>
 Crowd Sourcing
  1. V Dimitrova, CM Steiner, D Despotakis, P Brna, Crowdsourcing for Evaluating a Simulated Learning Environment for Interpersonal Communication and Cultural Awareness. CULTEL workshop at EC-TEL 2013 http://www.macs.hw.ac.uk/CulTEL/submissions/cultel2013_submission_5.pdf <Suggested by Cristina>
 Using Dialogue in User-adaptive Systems