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.

A Model for Content Sequencing in Intelligent Tutoring Systems Based on the Ecological Approach and Its Validation Through Simulated Students

Abstract: In this paper, we present an algorithm for reasoning about the sequencing of content for students in an intelligent tutoring system. Our motivating influence is McCalla’s ecological approach which advocates attaching models of learners to the learning objects they interact with, and then mining these models for patterns that are useful for various purposes. In particular, we record with each learning object those students who experienced the object, together with their initial and final states of knowledge, and then use these interactions to reason about the most effective lesson to show future students based on their similarity to previous students. We validate our approach in a context of simulated students, providing details of the model of learning used in the simulation and the results obtained in order to demonstrate the value of our model. As a result we offer a novel approach for peer-to-peer intelligent tutoring from repositories of learning objects.

Development of a Simulation-Based Intelligent Tutoring System for Assisting PID Control Learning.

Abstract: Provide research computing consultative services to the Psychology Department in all phases of the software development life cycle: feasibility studies, requirements gathering and analysis, software architecture and user interface design, implementation (coding), testing/calibration, deployment and maintenance. Collaborate with

HERA: Learner Tracking in a Virtual Environment

Abstract: The main goals of using simulations and Virtual Environments for Training/Learning (VET/L) are to avoid risks and unwanted consequences, to reduce training costs, and to promote trial and error as an effective strategy for learning. Such environments should enable monitoring of the plans followed by learners, the errors they commit and the resulting risks, and show learners the impact of their decisions during the training process. In this paper, we present a research work that aims to develop a learner tracking system in a virtual environment equipped with an Intelligent Tutoring System (ITS), that we have called HERA (Helpful agent for safEty leaRning in virtuAl environment). This system is composed of different models representing the data sources, and other modules corresponding to the main processes charged with exchanging, analyzing, transforming, registering, and interpreting data. Thanks to the collaboration between these models and modules, HERA is able to determine learners' current tasks, the errors and risks produced, and to help them during and after learning. Our system allows learners to perform their tasks in front of a screen, and to see 1) the impact of their decisions by modifying the Virtual Environment, and 2) a textual feedback of their activities (tasks and errors) in a list form.

Applying a student modeling with non-monotonic diagnosis to Intelligent Virtual Environment for Training/Instruction

Abstract: We present a student modeling approach that has been designed to be part of an Intelligent Virtual Environment for Training and/or Instruction (IVET). In order to provide the proper tutoring to a student, an IVET needs to keep and update dynamically a student model taking into account the student's behaviour in the Virtual Environment. For that purpose, the proposed student model employs a student ontology, a pedagogic diagnosis module and a Conflict Solver module. The goal of the pedagogic diagnosis module is to infer which learning objectives have been acquired or not by the student. Nevertheless, the diagnosis process can be complicated by the fact that while learning the student will not only acquire new knowledge, but he/she may also forget some previously acquired knowledge, or he/she may have some oversights that could mislead the tutor about the true state of the student's knowledge. All of these situations will lead to contradictions in the student model that must be solved so that the diagnosis can continue. Thus, our approach consists in applying diagnosis rules until a contradiction arises. At that moment, a conflict solver module is responsible of classifying and solving the contradiction. Next, the student ontology is updated according to the resolution adopted by the Conflict Solver and the diagnosis can continue. This paper mainly focuses on the design of the proper mechanisms of the student model to deal with the non monotonic nature of the pedagogic diagnosis.