Abstract
Intelligent Tutoring Systems (ITSs) are intended to help in tutoring the students in specific domains typically by improving their problem solving skills. An important aspect of such ITSs is their ability to solve the generated problems in the same way that the student would in addition to interpreting the student actions to provide relevant feedback and help. Cognitive models that mimic the way knowledge is represented in human minds are excellent means toward achieving this goal. This paper discusses cognitive modelling in the MAth Story problem Tutor (MAST). MAST is a Web-based ITS that can generate probability story problems of different contexts, types and difficulty levels. The paper also discusses the model tracing approach of MAST to interpret the student actions in symbolizing the word problems and estimating the required probabilities to provide relevant feedback and help. A major contribution of the paper is in considering the symbolization of the probability word problems to convert them to the symbolic form and tracing the students errors in this process. As an example, the paper considers the context of rolling a die and tossing a coin. Evaluation results have shown the ability of MAST to considerably improve the probability story problem solving skills of the students.
Highlights
Intelligent tutoring systems (ITSs) aim to assist the students to acquire and enhance their knowledge and problem solving skills in a specific domain [1]
This paper presents MAth Story problem Tutor (MAST), a Web-based ITS of probability story problems
This paper presented MAST, a Web-based ITS of probability story problems
Summary
Intelligent tutoring systems (ITSs) aim to assist the students to acquire and enhance their knowledge and problem solving skills in a specific domain [1]. A common approach to achieving those requirements is via developing an expert system module that is able to generate the problem answers and provide the base for interpreting student actions [5]. An excellent means of achieving this goal is by adopting a cognitive approach in implementing the expert system module to mimic the manner by which procedural knowledge is represented in the human mind This would allow ITSs to respond to problem-solving situations in a manner similar to that of the student [6]. MAST employs a rule-based cognitive model to independently solve the generated word problems and integrates cognitive modeling with model tracing to interpret the student actions in terms of the cognitive model knowledge to provide relevant help.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have