Dynamic instructional planning for an intelligent physiology tutoring system
24
Citation
7
Reference
10
Related Paper
Citation Trend
Abstract:
An intelligent tutoring system (ITS) that will assist first-year medical students in learning the causal relationships between the parameters of the circulatory system and in solving problems of disturbances to the system is discussed. The central component of the ITS, the instructional planner, is responsible for determining what to do next at each point during a tutoring session. The approach taken is to build the planner by combining the capabilities of lesson planning and discourse planning in order to provide globally coherent and adaptive instruction. The planner consists of two parts: a lesson planner and a discourse planner. The lesson planner generates lesson plans, which the discourse planner carries out. A sample dialogue, extracted from the transcript of an actual human tutor-student interaction, is used to provide a framework for the development of the overall system, especially from the planner's point of view.< Keywords:
Planner
TUTOR
Intelligent tutoring system
TUTOR
Peer tutor
Intelligent tutoring system
Reflection
Cite
Citations (21)
Rules have been showed to be appropriate representations to model tutoring and can be easily applied to intelligent tutoring systems. We applied a machine learning technique, Classification based on Associations, to automatically learn tutorial rules from annotated tutoring dialogues of a human expert tutor. The rules we learn concern the tutor's attitude, the domain concepts to focus on, and the tutor moves. These rules have very good accuracy. They will be incorporated in the feedback generator of an Intelligent Tutoring System.
TUTOR
Intelligent tutoring system
Cite
Citations (5)
A Web-based adaptive tutoring system which dynamically adapts to each student's needs and gives a student immediate feedback is being developed for our CS-I and CS-II closed laboratories. The system currently contains the question tutor, the program tutor, and the course management components. The tutoring components help students learn programming concepts through hands-on, self-paced exercises. The course management component helps teachers prepare and maintain the lab materials. Experiments have been conducted to evaluate the effectiveness of this new tutoring system and promising preliminary results were obtained.
TUTOR
Intelligent tutoring system
Component (thermodynamics)
Cite
Citations (12)
We present the design and an evaluation of Thermo-Tutor, an Intelligent Tutoring System (ITS) that teaches thermodynamic cycles in closed systems. Thermo-Tutor provides opportunities for students to practice their skills by solving problems. When a student submits a solution, Thermo-Tutor analyzes it and provides appropriate feedback. We discuss the support for problem solving, and the student model the ITS maintains. An initial evaluation of Thermo-Tutor was performed at the University of Canterbury. The findings show that the ITS supports student learning effectively.
TUTOR
Intelligent tutoring system
Cite
Citations (13)
Intelligent computers are perfect for educational use since computers never get tired, they are available all the time, and they are accessible by users all over the world. One key component that makes an Intelligent Tutoring System more intelligent and more adaptive to individual student's needs is a student model.
This dissertation attacks problems of student modeling in a natural language based Intelligent Tutoring System—Circsim-Tutor. A student model that is composed of different sub-models has been built by considering the actual demands of different decision-making components of the system. We also discuss how to determine the knowledge required in each model by considering constraints imposed by the system. By comparing the quality of tutorial dialogues based on two versions of the student model in CIRCSIM-Tutor, we show the advantages of using the new student model.
We have also developed a new model of hinting for Circsim-Tutor. It can systematically deliver pedagogically sound and conversationally coherent hints in the tutoring dialogue by considering the domain knowledge, the type of error made by the student, the focus of the tutor's question, and the conversational history. In addition we have studied how to use machine learning techniques to discover tutoring rules from human tutoring transcripts.
TUTOR
Intelligent tutoring system
Component (thermodynamics)
Cite
Citations (5)
This paper presents FITS - an Intelligent Tutoring System (ITS) for the domain of addition of fractions. It was developed with the aim of improving on many of the shortcomings of existing tutors in the mathematical domain. The paper largely describes its functioning. In order to give the reader a better 'feel' of the tutor's capabilities than obtained from its description, an actual student-tutor protocol extract is given. More significantly the tutor has also been evaluated in several ways with seemingly very encouraging results so far; however, due to length restrictions they are not reported in this paper. The paper concludes by briefly highlighting some of FITS's improved features over other existing tutors in the domain as well as some of its shortcomings.
TUTOR
Intelligent tutoring system
Fraction (chemistry)
Cite
Citations (3)
Over the last few decades, researchers put efforts to improve intelligent tutoring systems' abilities with the aim to get them as close as possible to the ultimate goal of one-to-one tutoring. CoLaB Tutor and AC-ware Tutor are intelligent tutoring systems based on conceptual knowledge learning and are notable due to the fact they are relatively easy to generalize to multiple knowledge domains. CoLaB Tutor's forte lies in teacher-learner communication in controlled natural language, while AC-ware Tutor focuses on the automatic and dynamic generation of adaptive courseware. In order to compare various intelligent tutoring system supported education environments, in this chapter, the authors summarize several empirical evaluations of CoLaB Tutor and AC-ware Tutor. The results of intelligent tutoring systems' effectiveness in these environments offer the possibility to observe the specific intelligent tutoring system across various education levels, as well as to compare the intelligent tutoring systems' supported education environments.
TUTOR
Intelligent tutoring system
Cite
Citations (0)
TUTOR
Intelligent tutoring system
Cite
Citations (0)
The intelligent tutoring system (ITS) is an educational software system that provides personalized and adaptive tutoring to students based on their needs, profiles and preferences. The tutor model and student model are two dependent components of any ITS system. The goal of any ITS system is to help the students to achieve maximum learning gain and improve their engagements to the systems by capturing the student's interests through the system's adaptive behavior. In other words an ITS system is always developed with the aim of providing an immediate and efficient solution to student's learning problems. In recent years a lot of work has been devoted to improving student and tutor models in order enhance the teaching and learning activities within the ITS systems. The aim of this paper is to investigate the most recent state of art in the development of these two vital components of the intelligent tutoring systems.
TUTOR
Intelligent tutoring system
Tutorial system
Adaptive system
Adaptive Learning
Cite
Citations (24)
Hinting is an important tutoring tactic in one-on-one tutoring, used when the tutor needs to respond to an unexpected answer from the student. To issue a follow-up hint that is pedagogically helpful and conversationally smooth, the tutor needs to suit the hinting strategy to the student's need while making the strategy fit the high level tutoring plan and the tutoring context. This paper describes a study of the hinting strategies in a corpus of human tutoring transcripts and the implementation of these strategies in a dialogue-based intelligent tutoring system, CIRcslM-Tutor v. 2. We isolated a set of hinting strategies from human tutoring transcripts. We describe our analysis of these strategies and a model for choosing among them based on domain knowledge, the type of error made by the student, the focus of the tutor's question, and the conversational history. We have tested our model with two classes totaling 74 medical students. Use of this extended model of hinting increases the percentage of questions that students are able to answer for themselves rather than needing to be told.
TUTOR
Intelligent tutoring system
Peer tutor
Cite
Citations (52)