Himanshu Zade
ABSTRACT
When a user learns to use a new device, her understanding of it evolves. A progressive comparison of the evolving user models towards the device target model, for analysing learning, involves determining the behavioral proximity between them. To quantify the gap between a user model and a target model, we introduce an edit distance metric for measuring their behavioral proximity using a bisimulation-based equivalence relation. We define edit distance to be the minimum number of edges and states with incident edges required to be deleted from and/or added to a user model to make it bisimilar to the target model. We propose an algorithm to compute edit distance between two models and employ the heuristic procedure on experimental data for computing edit distance between target and user models. The data is organised into two experiments depending on the device the user interacted with: (a) a simple device resembling a vending machine and (b) a close to real-world vehicle transmission model. The results validate our proposed metric as edit distance converges with progressive user learning, increases for erroneous learning, and remains unchanged indicating no learning.
- A. Dix, J. F., and Beale, R. Analysis of user behaviour as time series. In HCI'92: People and Computers VII, Cambridge University Press (1992), 429--444. Google Scholar
Digital Library
- Anderson, J. R., Corbett, A. T., Koedinger, K. R., and Pelletier, R. Cognitive tutors: Lessons learned. The Journal of the Learning Sciences 4, 2 (1995), 167--207.Google ScholarCross Ref
- Baecker, R., Booth, K., Jovicic, S., McGrenere, J., and Moore, G. Reducing the gap between what users know and what they need to know. In CUU '00, 17--23. Google ScholarDigital Library
- Card, S. K., Moran, T. P., and Newell, A. The keystroke level model for user performance time with interactive systems. Commun. ACM 23, 7 (1980), 396--410. Google ScholarDigital Library
- Carley, K., and Palmquist, M. Extracting, representing, and analyzing mental models. Social Forces 70, 3 (1992), 601--636.Google ScholarCross Ref
- Carroll, J. M., and Olson, J. R., Eds. Mental models in human-computer interaction: research issues about what the user of software knows. National Academy Press, Washington, DC, USA, 1987. Google ScholarDigital Library
- Combés, S., and Pecheur, C. A bisimulation-based approach to the analysis of human-computer interaction. In EICS '09, ACM (2009), 101--110. Google ScholarDigital Library
- Dhawan, R., M. O., and Borman, M. Mental models and dynamic decision making: an experimental approach for testing system methodologies. In 24th International Conference of the System Dynamics Society (2006).Google Scholar
- Doherty, G., Campos, J. C., and Harrison, M. D. Representational reasoning and verification. Formal Aspects of Computing 12 (1998), 260--277.Google ScholarCross Ref
- Dong, Q. A bisimulation approach to verification of molecular implementations of formal chemical reaction networks, 2012.Google Scholar
- Falb, J., Popp, R., Rock, T., Jelinek, H., Arnautovic, E., and Kaindl, H. Fully-automatic generation of user interfaces for multiple devices from a high-level model based on communicative acts. In HICSS (2007), 26--26. Google ScholarDigital Library
- Fischer, G. User modeling in humancomputer interaction. User Modeling and User-Adapted Interaction 11, 1--2 (2001), 65--86. Google ScholarDigital Library
- Gentner, D., and Stevens, A. Mental models. Cognitive Science - Lawrence Erlbaum Associates. Lawrence Erlbaum Associates, Incorporated, 1983.Google Scholar
- Harrison, M. Modelling user structures within system Specifications. In Formal Methods in HCI: III, IEE Colloquium on (1989), 1/1--1/4.Google Scholar
- Harrison, M., and Thimbleby, H. Formal Methods in Human Computer Interactions. Cambridge Middle East Library. Cambridge University Press, 1990. Google ScholarDigital Library
- Hermann, M., and Weber, M. When three worlds collide: a model of the tangible interaction process. In OZCHI '09, ACM (2009), 341--344. Google ScholarDigital Library
- Heymann, M., and Degani, A. Formal analysis and automatic generation of user interfaces: approach, methodology, and an algorithm. Human Factors 49, 2 (Apr. 2007), 311--30.Google ScholarCross Ref
- Hinckley, K., Cutrell, E., Bathiche, S., and Muss, T. Quantitative analysis of scrolling techniques. In CHI '02, ACM (2002), 65--72. Google ScholarDigital Library
- Ippel, M. J., and Beem, A. L. Mental models as finite-state machines: Examples and computational methods. Tech. Rep. 9911, United State Air Force Armstrong Laboratory, October 1998.Google ScholarCross Ref
- John, B. E., and Kieras, D. E. The goms family of analysis techniques: Tools for design and evaluation. Tech. rep., Carnegie Mellon University, 1994. Google ScholarDigital Library
- Johnson-Laird, P. N. Mental models: Towards a cognitive science of language, inference, and consciousness, vol. 6. 1983. Google ScholarDigital Library
- Kieras, D. E. Towards a practical goms model methodology for user interface design. Handbook of human-computer interaction (1988), 135--158.Google Scholar
- Kieras, D. E., and Bovair, S. The role of a mental model in learning to operate a device. Cognitive Science 8, 3 (1984), 255--273.Google ScholarCross Ref
- Long, J., and Dowell, J. Formal methods: the broad and the narrow view. In Formal Methods and HCI: II, IEE Colloquium on (1988), 5/1--5/8.Google Scholar
- Milner, R. A Calculus of Communicating Systems. Springer-Verlag New York, Inc., 1982. Google ScholarDigital Library
- Norman, D. Some observations on mental models. Mental models 7 (1983).Google Scholar
- Payne, S. J., and Green, T. R. G. Task-action grammars: a model of the mental representation of task languages. Human-Computer Interaction 2, 2 (June 1986), 93--133. Google ScholarDigital Library
- Popp, R., Falb, J., Raneburger, D., and Kaindl, H. A transformation engine for model-driven ui generation. In EICS '12, ACM (2012), 281--286. Google ScholarDigital Library
- Qian, X., Yang, Y., and Gong, Y. The art of metaphor: a method for interface design based on mental models. In VRCAI '11, ACM (2011), 171--178. Google ScholarDigital Library
- Read, J. C., MacFarlane, S., and Casey, C. What's going on?: discovering what children understand about handwriting recognition interfaces. In IDC '03, 135--140. Google ScholarDigital Library
- Reeves, S. Principled formal methods in hci research. In Formal Methods in HCI: III, IEE Colloquium on (1989), 2/1--2/3.Google Scholar
- Romera, M. E. Using finite automata to represent mental models. Master's thesis, San Jose State University, 2000.Google Scholar
- Rushby, J. Using model checking to help discover mode confusions and other automation surprises. In Reliability Engineering and System Safety, vol. 75 (2002), 167--177.Google ScholarCross Ref
- Santosh, and Himanshu. Restrcting edit operations to generate bisimilar fsms. Tech. Rep. xxxx, IIIT-Hyderabad, January 2014.Google Scholar
- Schneider, K., and Cordy, J. Abstract user interfaces: A model and notation to support plasticity in interactive systems. In Interactive Systems: Design, Specification, and Verification, vol. 2220. Springer, 2001, 28--48. Google ScholarDigital Library
- Thimbleby, B., Blandford, A., Cairns, P., Curzon, P., and Jones, M. User interface design as systems design. In People and Computers XVI - Memorable yet Invisible, Springer (2002), 281--301.Google ScholarCross Ref
- Thimbleby, H. Press On Principles of Interaction Programming. MIT Press, 2007. Google ScholarDigital Library
- Thimbleby, H. Contributing to safety and due diligence in safety-critical interactive systems development by generating and analyzing finite state models. In EICS '09, ACM (2009), 221--230. Google ScholarDigital Library
- Tullio, J., Dey, A. K., Chalecki, J., and Fogarty, J. How it works: a field study of non-technical users interacting with an intelligent system. In CHI '07, 31--40. Google ScholarDigital Library
- Zade, H., and Choppella, V. Functionality or user interface: Which is easier to learn when changed? In IHCI '12 (2012), 1--6.Google ScholarCross Ref
Index Terms
- Edit distance modulo bisimulation: a quantitative measure to study evolution of user models
Recommendations
Redefining the Graph Edit Distance
AbstractGraph edit distance has been used since 1983 to compare objects in machine learning when these objects are represented by attributed graphs instead of vectors. In these cases, the graph edit distance is usually applied to deduce a distance between ...
An Edit Distance Between Graph Correspondences
Graph-Based Representations in Pattern RecognitionAbstractThe Hamming Distance has been largely used to calculate the dissimilarity of a pair of correspondences (also known as labellings or matchings) between two structures (i.e. sets of points, strings or graphs). Although it has the advantage of being ...
Edit distance for timed automata
HSCC '14: Proceedings of the 17th international conference on Hybrid systems: computation and controlThe edit distance between two (untimed) traces is the minimum cost of a sequence of edit operations (insertion, deletion, or substitution) needed to transform one trace to the other. Edit distances have been extensively studied in the untimed setting, ...
Comments