Abstract
Computer users have different levels of system skills. Moreover, each user has different levels of skill across different applications and even in different portions of the same application. Additionally, users’ skill levels change dynamically as users gain more experience in a user interface. In order to adapt user interfaces to the different needs of user groups with different levels of skills, automatic methods of skill detection are required. In this paper, we present our experiments and methods, which are used to build automatic skill classifiers for desktop applications. Machine learning algorithms were used to build statistical predictive models of skill. Attribute values were extracted from high frequency user interface events, such as mouse motions and menu interactions, and were used as inputs to our models. We have built both task-independent and task-dependent classifiers with promising results.
Similar content being viewed by others
References
Anderson J.R.: Acquisition of cognitive skill. Psychol. Rev. 89(4), 369–406 (1982)
Anderson J.R.: The Architecture of Cognition. Harvard University Press, Cambridge (1983)
Barfield W.: Expert-novice differences for software: implications for problem-solving and knowledge acquisition. Behav. Inf. Technol. 5(1), 15–29 (1986)
Beale, R., Finlay, J., Austin, J., Harrison, M.: User modelling by classification: a neural-based approach. New Developments in Neural Computing, In: Taylor, J.G., Mannion, C.L.T. (eds.) (Adam Hilger), pp. 103–110 (1989)
Benyon D.: Adaptive systems: a solution to usability problems. User Model. User-adapt. Interact. 3(1), 65–87 (1993)
Brewer M.: Research design and issues of validity. In: Reis, H., Judd, C. (eds) Handbook of research methods in social and personality psychology, Cambridge University Press, Cambridge (2000)
Browne, D., Totterdell, P., Norman, M. (eds): Adaptive User Interfaces. Academic Press Ltd, London (1990)
Brusilovsky P.: Methods and techniques of adaptive hypermedia. User Model. User-adapt. Interact. 6(2–3), 87–129 (1996)
Card S.K., Moran T.P., Newell A.: The keystroke-level model for user performance time with interactive systems. Commun. ACM. 23(7), 396–410 (1980)
Caruana R.: Multitask learning. Mach. Learn. 28, 41–75 (1997)
Chase W.G., Simon H.A.: Perception in chess. Cogn. Psychol. 4, 55–81 (1973)
Cheung Chiu B., Webb G.I.: Using decision trees for agent modeling: improving prediction performance. User Model. User-adapt. Interact. 8(1–2), 131–152 (1998)
Chin, D.N.: KNOME: modeling what the user knows in UC. In: Kobsa A., Wahlster W. (eds.) User Models in Dialog Systems, pp. 74–107. Springer, Berlin (Symbolic computation series edited by Loveland D.W.) (1989)
Crossman E.R.F.W.: A theory of the acquisition of speed-skill. Ergonomics 2, 153–156 (1959)
Darzi A., Mackay S.: Skills assessment of surgeons. Surgery 131(2), 121–124 (2002)
Datta V., Mackay S., Mandalia M., Darzi A.: The use of electromagnetic motion tracking analysis to objectively measure open surgical skill in laboratory-based model. J. Am. Coll. Surg. 193, 479–485 (2001)
Datta V., Mandalia M., Mackay S., Chang A., Cheshire N., Darzi A.: Relationship between skill and outcome in the laboratory-based model. Surgery 131(3), 318–323 (2001)
Desmarais, M.C., Liu, J.: Exploring the applications of user-expertise assessment for intelligent interfaces. In: Proceedings of the INTERACT ‘93 and CHI ‘93 Conference on Human Factors in Computing Systems, (pp. 308–313). Amsterdam, The Netherlands (1993)
Encarnação, L.M.: Multi-level user support through adaptive hypermedia: a highly application-independent help component. In: Proceedings of the 1997 International Conference on Intelligent User Interfaces, pp. 187–194. Orlando, Florida, United States (1997)
Eurostat: http://epp.eurostat.ec.europa.eu (2005)
Faulkner L., Wick D.: Cross-user analysis: benefits of skill level comparison in usability testing. Interact. Comput. 17(6), 773–786 (2005)
Fischer G.: User modeling in human–computer interaction. User Model. User-adapt. Interact. 11(1-2), 65–86 (2001)
Fitts P.M.: The information capacity of the human motor system in controlling the amplitude of movement. J. Exp. Psychol. 47(6), 381–391 (1954) (Reprinted in J. Exp. Psychol.: General, 121(3):262–269, 1992)
Fitts P.M.: Perceptual-motor skill learning. In: Melton, A.W. (eds) Categories of Human Learning, Academic Press, New York and London (1964)
Fitts P.M., Posner M.I.: Human Performance. Brooks/Cole Publishing Company, Belmont (1968)
Frias-Martinez E., Chen S.Y., Macredie R.D., Liu X.: The role of human factors in stereotyping behavior and perception of digital library users: a robust clustering approach. User Model. User-adapt. Interact. 17(3), 305–337 (2007)
Ghazarian, A.: A case study of defect introduction mechanisms. In: Proceedings of the 21st International Conference on Advanced Information Systems (CAiSE’09), pp. 156–170. Amsterdam, The Netherlands (2009)
Hacker W.: Action theory and occupational psychology. Review of German empirical research since 1987. Ger. J. Psychol. 18(2), 91–120 (1994)
Hackos J.T., Redish J.C.: User and task analysis for interface design. Wiley, New York (1998)
Hassel L., Hagen E.: Adaptation of an automotive dialogue system to users’ expertise and evaluation of the system. Computers and the Humanities 40(1), 67–85 (2006)
Hilbert, D.M., Robinns, J.E., Redmiles, D.F.: Supporting ongoing user involvement in development via expectation-driven event monitoring. In: Tech. Rep. UCI-ICS-97-19, Department of Information and Computer Science, University of California, Irvine (1997)
Hilbert D.M., Redmiles D.F.: Extracting usability information from user interface events. ACM Comput. Surv. 32(4), 384–421 (2000)
Horvitz, E., Breese, J., Heckerman, D., Hovel, D., Romelse, K.: The Lumière project: Bayesian user modeling for inferring the goals and needs of software users. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 256–265. Madison, WI (1998)
Hurst, A., Hudson, S.E., Mankoff, J.: Dynamic detection of novice versus skilled use without a task model. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 271–280. San Jose, California, USA (2007)
Jameson, A.: Generalizing the double-stereotype approach: a psychological perspective. In: Proceedings of the Third International Workshop on User Modeling, pp. 69–83. Wadern, Germany (1992)
John B.E., Kieras D.E.: Using GOMS for user interface design and evaluation: which technique?. ACM Trans. Comput. Hum. Interact. 3(4), 287–319 (1996)
Jokinen, K., Kanto, K.: User expertise modelling and adaptivity in a speech-based e-mail system. In: Proceedings of the 42nd Annual Meeting on Association For Computational Linguistics, vol. 87. Barcelona, Spain (2004)
Kobsa, A.: User modeling: recent work, prospects and hazards. In: SchneiderHufschmidt, M., Kühme, T., Malinowski, U. (eds.) Adaptive User Interfaces: Principles and Practice, pp. 111–128. Amsterdam North-Holland (1993)
Kobsa A.: Generic user modeling systems. User Model. User-adapt. Interact. 11(1–2), 49–63 (2001)
Kurosu, M., Urokohara, H., Sato, D., Nishimura, T., Yamada, F.: A new quantitative measure for usability testing: NEM (novice expert ratio method). Poster session presented at the annual conference of the usability professionals’ association on humanizing design, Orlando, Florida (2002)
Langley P., Simon H.A.: Applications of machine learning and rule induction. Commun. ACM 38(11), 54–64 (1995)
Leung S.C., Fulcher J.: Classification of user expertise level by neural networks. Intl. J. Neural Syst. 8(2), 155–171 (1997)
Lin H.C., Shafran I., Yuh D., Hager G.D.: Towards automatic skill evaluation: detection and segmentation of robot-assisted surgical motions. Comput. Aided Surg. 11(5), 220–230 (2006)
Mitchel T.M.: Machine Learning. McGrawHill, New York (1997)
Neves D.M., Anderson J.R.: Knowledge compilation: mechanisms for the automatization of cognitive skills. In: Anderson, J.R. (eds) Cognitive Skills and Their Acquisition, pp. 57–84. Wiley, Hillsdale (1981)
Newell A., Rosenbloom P.: Mechanisms of skill acquisition and the law of practice. In: Anderson, J.R. (eds) Cognitive Skills and Their Acquisition, pp. 2–55. Erlbaum Associates, Hillsdale (1981)
Nielsen J.: Usability Engineering. Academic Press, Boston (1993)
Nisbett R.E., Wilson T.D.: Telling more than we can know: verbal reports on mental processes. Psychol. Rev. 84, 231–259 (1977)
Norman D.: Design of Everyday Things. Doubleday, New York (1988)
Olson J.R., Olson G.M.: The growth of cognitive modeling in human-computer interaction since GOMS. In: Baecker, R.M., Grudin, J., Buxton, W.A., Greenberg, S. (eds) Human-Computer Interaction: Toward the Year 2000, pp. 603–625. Morgan Kaufmann Publishers, San Francisco (1995)
Phillips J.G., Triggs T.J.: Characteristics of cursor trajectories controlled by the computer mouse. Ergonomics 44, 527–536 (2001)
Popovic, V.: Novice and Expert User Models, PhD dissertation, University of Sydney (1998)
Provost, F.J., Fawcett, T., Kohavi, R.: The Case against accuracy estimation for comparing induction algorithms. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 445–453. Madison, Wisconsin, USA (1998)
Quinlan J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, Los Altos (1993)
Rasmussen J.: Skills, rules and knowledge; signals, signs and symbols, and other distinctions in human performance models. IEEE Trans. Syst. Man Cybern. 13(3), 257–266 (1983)
Rich E.: Users are individuals: individualizing user models. Int. J. Hum. Comput. Stud. 51(2), 323–338 (1999)
Richards C., Rosen J., Hannaford B., Pellegrini C., Sinanan M.: Skills evaluation in minimally invasive surgery using force/torque signatures. Surg. Endosc. 14(9), 791–798 (2000)
Roberts M.: Integrating the Mind. Routledge, London (2007)
Rosen J., Hannaford B., Richards C.G., Sinanan M.N.: Markov modeling of minimally invasive surgery based on tool/tissue interaction and force/torque signatures for evaluating surgical skills. IEEE Trans. Biomed. Eng. 48(5), 579–591 (2001)
Sanderson P.M., Fisher C.: Exploratory sequential data analysis: foundations. Hum. Comput. Interact. Special Issue ESDA 9(3–4), 251–317 (1994)
Santos P.J., Badre A.N.: Automatic chunk detection in human-computer interaction. In: Costabile, M.F., Catarci, T., Levialdi, S., Santucci, G. (eds) Proceedings of the Workshop on Advanced Visual Interfaces, pp. 69–77. Bari, Italy (1994)
Simon H.A., Gilmartin K.: A simulation of memory for chess positions. Cogn. Psychol. 5, 29–46 (1973)
Sukaviriya, P.N., Foley, J.D.: Supporting adaptive interfaces in a knowledge-based user interface environment. In: Proceedings of the 1st International Conference on Intelligent User Interfaces, pp. 107–113. Orlando, Florida, United States (1993)
Sun R., Giles C.L.: Sequence learning: from recognition and prediction to sequential decision making”. IEEE Intell. Syst. 16(4), 67–70 (2001)
Trumbly J.E., Arnett K.P., Martin M.P.: Performance effect of matching computer interface characteristics and user skill level. Int. J. Man-Mach. Stud. 38(4), 713–724 (1993)
Uehling, D.L., Wolf, K.: User action graphing effort (UsAGE). In: Conference on Human Factors in Computing Systems, pp. 290–291. Denver, Colorado, United States (1995)
Vaubel K.P., Gettys C.F.: Inferring user expertise for adaptive interfaces. Hum. Comput. Interact. 5(1), 95–117 (1990)
Webb G.I., Pazzani M.J., Billsus D.: Machine learning for user modeling. User Model. User-adapt. Interact. 11(1–2), 19–29 (2001)
Witten I.H., Frank E.: Data Minning: Practical Machine Learning Tools and Techniques, 2nd Edn. Morgan Kaufmann, San Francisco (2005)
Zukerman I., Albrecht D.W.: Predictive statistical models for user modeling. User Model. User-adapt. Interact. 11(1–2), 5–18 (2001)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ghazarian, A., Noorhosseini, S.M. Automatic detection of users’ skill levels using high-frequency user interface events. User Model User-Adap Inter 20, 109–146 (2010). https://doi.org/10.1007/s11257-010-9073-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11257-010-9073-5