Intelligent tutoring system

About: Intelligent tutoring system is a research topic. Over the lifetime, 3472 publications have been published within this topic receiving 58217 citations. The topic is also known as: ITS.

Beyond Intelligent Tutoring Systems: Situations, Interactions, Processes and Affordances

Abstract: The idea of an intelligentsystem to support learning has been epitomisedby that of an intelligent tutoring system(ITS). However, ITSs are, in fact, just aparticular kind of intelligent system tosupport learning whose components reflect thevalues of the particular view that ITSsemphasise in regard to the nature of knowledge,learning and teaching, which have led to anarchitecture that focuses on representing theknowledge to be learned (domain model),inferring the learner's knowledge (learnermodel), and planning instructional steps to thelearner (teaching model). On the other hand,other views of learning may lead to differentneeds in terms of knowledge representation,reasoning, and decision making capabilities inthe intelligent systems that support them.Constructivist views, for example, emphasisedifferent values and may require an entirelydifferent architecture of intelligent system tosupport its philosophy of learning. This paperpresents an architecture of an intelligentsystem to support learning that is able toaddress the issues that arise fromconstructivist theories of learning in a waythat, rather than opposing to the standard ITSarchitecture, characterises a broader view inall its components which can be appropriatelyattuned to address the issues of particularphilosophies.

A flowchart-based intelligent tutoring system for improving problem-solving skills of novice programmers

Abstract: Intelligent tutoring and personalization are considered as the two most important factors in the research of learning systems and environments An effective tool that can be used to improve problem-solving ability is an Intelligent Tutoring System which is capable of mimicking a human tutor's actions in implementing a one-to-one personalized and adaptive teaching In this paper, a novel Flowchart-based Intelligent Tutoring System FITS is proposed benefiting from Bayesian networks for the process of decision making so as to aid students in problem-solving activities and learning computer programming FITS not only takes full advantage of Bayesian networks, but also benefits from a multi-agent system using an automatic text-to-flowchart conversion approach for engaging novice programmers in flowchart development with the aim of improving their problem-solving skills In the end, in order to investigate the efficacy of FITS in problem-solving ability acquisition, a quasi-experimental design was adopted by this research According to the results, students in the FITS group experienced better improvement in their problem-solving abilities than those in the control group Moreover, with regard to the improvement of a user's problem-solving ability, FITS has shown to be considerably effective for students with different levels of prior knowledge, especially for those with a lower level of prior knowledge

Improving Student Performance Using Self-Assessment Tests

Abstract: Testing is the most generic and perhaps most widely used mechanism for student assessment. Most tests are based on the classical test theory, which says that a student's score is the sum of the scores obtained in all questions plus some kind of error. The most relevant is that the student test result depends heavily on the individual's learning preferences or abilities and also on the actual test's format. According to this theory, tests aren't necessarily useful in intelligent educational systems, which require accurately obtaining the student's knowledge state to guide the learning process. Yet the Web has created a new generation of intelligent systems-adaptive hypermedia systems which offer new types of instructional interaction. Educational AHSs adapt the learning process on the basis of the student's learning preferences, knowledge, and availability. One such Web-based tool is Siette (the system of intelligent evaluation using rests), which infers student knowledge using adaptive testing.

A Knowledge Discovery Framework for Learning Task Models from User Interactions in Intelligent Tutoring Systems

Abstract: Domain experts should provide relevant domain knowledge to an Intelligent Tutoring System (ITS) so that it can guide a learner during problem-solving learning activities. However, for many ill-defined domains, the domain knowledge is hard to define explicitly. In previous works, we showed how sequential pattern mining can be used to extract a partial problem space from logged user interactions, and how it can support tutoring services during problem-solving exercises. This article describes an extension of this approach to extract a problem space that is richer and more adapted for supporting tutoring services. We combined sequential pattern mining with (1) dimensional pattern mining (2) time intervals, (3) the automatic clustering of valued actions and (4) closed sequences mining. Some tutoring services have been implemented and an experiment has been conducted in a tutoring system.