Development of a model of user attributes and its implementation within an adaptive tutoring system

Milne, Sue; Shiu, Edward; Cook, Jean

doi:10.1007/bf00213186

Cited by 12 publications

(3 citation statements)

References 9 publications

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“…All of the empirical evaluation papers in the ¢rst nine years of UMUAI can be classi¢ed into four broad topic areas: ten papers in adaptive-hypermedia/information-¢ltering (Jennings and Higuchi, 1993;Kaplan et al, 1993;Boyle and Encarnacion, 1994;Vassileva, 1996;Mathe¨and Chen, 1996;Raskutti et al, 1997;Newell, 1997;Hirashima et al, 1997;Alspector et al, 1997;Balabanovic¨, 1998), nine in student modeling (Nwana, 1991;London, 1992;Carbonaro et al, 1995;Corbett and Anderson, 1995;Kashihara et al, 1995;Shute, 1995;Webb and Kuzmycz, 1996;Mitrovic et al, 1996;Milne et al, 1996;Sison et al, 1998), nine in plan recognition/mixed-initiative interaction (Calistri-Yeh, 1991;Albrecht et al, 1998;Gmytrasiewicz et al, 1998;Chiu and Webb, 1998;Chu-Carroll and Brown, 1998;Guinn, 1998;Ishizaki et al, 1999;Green and Carberry, 1999;Virvou and du Bulay, 1999), and three in user interfaces/help systems (Tattersall, 1992;Krause et al, 1993;Debevc et al, 1996).…”

Section: Um Evaluations In the Pastmentioning

confidence: 99%

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Chin

2001

User Modeling and User-Adapted Interaction

171

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Abstract. Empirical evaluations are needed to determine which users are helped or hindered by user-adapted interaction in user modeling systems. A review of past UMUAI articles reveals insuf¢cient empirical evaluations, but an encouraging upward trend. Rules of thumb for experimental design, useful tests for covariates, and common threats to experimental validity are presented. Reporting standards including effect size and power are proposed.Key words: empirical evaluation, experimental design, covariant variables, e¡ect size, treatment magnitude, power, sensitivity. What Is Empirical Evaluation?Empirical evaluation refers to the appraisal of a theory by observation in experiments. The key to good empirical evaluation is the proper design and execution of the experiments so that the particular factors to be tested can be easily separated from other confounding factors. For example, one may want to test whether a software system with a user model works better than the same system without a user model, test the effect of different levels of user modeling or different user model parameter settings, or test different user interfaces. These factors, which are under the control of the experimenter, are termed independent variables because their values can be varied independently of other variables by the experimenter. Dependent variables are variables whose values depend on the values of other variables. They include response variables or recorded measures such as the frequency/extent of certain behaviours (e.g., system usage), qualities of a behavior in a particular situation, number of errors, error rate, time to complete a task, proportion/quality of tasks achieved, interaction patterns, learning time/rate, and/or subjective evaluations (e.g., user satisfaction). Some dependent variables can only be measured indirectly such as cognitive load measured through blood pressure or pupil dilation.In an ideal experiment, only the independent variables are varied and everything else is ¢xed so that any changes in the dependent variables can be directly attributed to the variations in the independent variables. Unfortunately, such ideal control is

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Section: Um Evaluations In the Pastmentioning

confidence: 99%

Untitled

Chin

2001

User Modeling and User-Adapted Interaction

171

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“…Additionally, unlike a human tutor, ITS faces greater challenges due to the restricted observation of student's behavior, usually through the computer keyboard and mouse. Despite realized limitations such as having to make tutoring decisions under uncertainty of student knowledge mastery, most recent ITSs, such as Milne, Shiu, and Cook (1996), ANDES (Gertner & VanLehn, 2000), SQL-Tutor (Mitrovi, Martin, & Mayo, 2002), and ADELE (Ganeshan, Johnson, Shaw, & Wood, 2000), use heuristic strategies that take the posterior probabilities from the student model and use them as the input to some decision rules.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Tutoring System for Newtonian Mechanics: Can it Adapt to Different Learners?

Pek

Poh

2004

Journal of Educational Computing Research

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Newtonian mechanics is a core module in technology courses, but is difficult for many students to learn. Computerized tutoring can assist the teachers to provide individualized instruction. This article presents the application of decision theory to develop a tutoring system, iTutor, to select optimal tutoring actions under uncertainty of students' mastery states. The novelties of this research are: 1) the automation of student diagnosis that is made possible when tutoring alternatives and the utilities for different outcomes are incorporated to the Bayesian network; and 2) the ability of the tutoring system to select test items with difficulties that are appropriate for the students. The results from formative evaluation on iTutor indicate that it is adaptive, working in a well-structured knowledge space, and able to use the information gathered from the student's responses to dynamically modify the presentation in clearly defined ways.

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“…All material is provided by the internet and the student's interaction with the material can be monitored and used to develop student or class models (e.g. Milne, Shiu and Cook 1996) that can in turn provide a filter on the subsequent material presented. Off-line browsing is the mode used where there is absolutely no interaction with the content producer's servers.…”

Section: Student's Computermentioning

confidence: 99%

Integrated tools for on-line education

Jesshope

Proceedings International Workshop on Advanced Learning Technologies. IWALT 2000. Advanced Learning Technology: Design and Deve

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Massey University has, for some years now, been developing and using tools for the delivery of multimedia presentations-on web sites. The key issues have been the size of the web site a d the bandwidth requirements f o r downloading the multimedia, which are both very small. This work is been used for the delivery of extramural, multi-campus and even internal courses using web browsers. These practical applications of our tools in real-live courses have given us insight into the requirement for on-line delivery. This paper described a project (Tile -Technology integrated learning environments), which integrates a wide range of tools for managing, designing, authoring, delivering and monitoring on-line education. The aim of the project is to develop an open, extensible and easy-to-use system, in which a number of diflerent tools can be integrated, sharing data on the structure and content of a given unit of educational material. \

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Development of a model of user attributes and its implementation within an adaptive tutoring system

Cited by 12 publications

References 9 publications

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A Bayesian Tutoring System for Newtonian Mechanics: Can it Adapt to Different Learners?

Integrated tools for on-line education

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