An Approach to Identifying Meaningful Action Patterns in Student-Tutor Interactions

1993 ◽  
Vol 37 (18) ◽  
pp. 1281-1285
Author(s):  
Anna L. Rowe ◽  
Nancy J. Cooke
Keyword(s):  
Intelligent Tutoring Systems ◽  
Scoring Method ◽  
Tutoring Systems ◽  
Knowledge Based ◽  
Scaling Procedure ◽  
Student Tutor ◽  
On Line ◽  
Pathfinder Network ◽  
Network Patterns ◽  
Initial Research

Part of the success of computerized intelligent tutoring systems will be associated with their ability to assess and diagnose students' knowledge in order to direct pedagogical interventions. What is needed is a methodology for identifying general relationships between on-line action patterns and patterns of knowledge derived off-line. Such a methodology would allow an assessment and diagnosis of knowledge, based only on student actions. The focus of this initial research is the development of a means of identifying meaningful action patterns in student-tutor interactions. Actions executed by subjects on a set of verbal troubleshooting tests (Nichols et al., 1989) were summarized using the Pathfinder network scaling procedure (Schvaneveldt, 1990). The results obtained from this work indicate that meaningful patterns of actions can be identified using the Pathfinder procedure. The network patterns are meaningful in the sense that they can differentiate high and low performers as defined by a previous scoring method. In addition, the networks reveal differences between high and low performers suggestive of targets for intervention.

scholarly journals ONLINE FUNCTIONAL LITERACY, INTELLIGENT TUTORING SYSTEMS AND SCIENCE EDUCATION

2015 ◽  
Vol 14 (2) ◽  
pp. 162-171
Author(s):  
Kosta Dolenc ◽  
Boris Aberšek ◽  
Metka Kordigel Aberšek
Keyword(s):  
Elementary School ◽  
Reading Comprehension ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Educational Process ◽  
Tutoring Systems ◽  
8Th Grade ◽  
On Line ◽  
Print Reading

We live in a time of transition from print reading (off-line) to screen reading (on-line), where the role of the book and other literature is being taken over by different types of electronic devices (computers, tablets, smart phones). In the lives of young people, there is less and less printed media, because it is being pushed out by electronic media. Most written media that is still used is thus bound to the classroom. However, in recent years schools have also become more like e-schools. It is almost impossible to find a school that does not use e-material in its educational process. Research indicates that there are differences in reading comprehension when reading off-line and on-line. In a study in which 78 students from the 8th grade of elementary school participated at the course Technology and science (n=77; 53.2% female), it was shown that in order to overcome this difference, individualised and adaptive Intelligent Tutoring Systems (ITS) can be used. The evaluation of the results also indicates that, for such a form of ITS, there is still plenty of space for optimisation, which is a permanent method of improvement and upgrade in such systems. Key words: reading comprehension, Technology and science, ITS, elementary school.

scholarly journals How Flexible Is Your Data? A Comparative Analysis of Scoring Methodologies across Learning Platforms in the Context of Group Differentiation

2017 ◽  
Vol 4 (2) ◽  
Author(s):  
Korinn S. Ostrow ◽  
Yan Wang ◽  
Neil T. Heffernan
Keyword(s):  
Intelligent Tutoring Systems ◽  
Credit Scoring ◽  
Data Availability ◽  
Type Ii Error ◽  
Partial Credit ◽  
Scoring Method ◽  
Tutoring Systems ◽  
Mathematics Skills ◽  
Group Differentiation ◽  
Learning Platforms

Data is flexible in that it is molded by not only the features and variables available to a researcher for analysis and interpretation, but also by how those features and variables are recorded and processed prior to evaluation. “Big Data” from online learning platforms and intelligent tutoring systems is no different. The work presented herein questions the quality and flexibility of data from two popular learning platforms, comparing binary measures of problem-level accuracy, the scoring method typically used to inform learner analytics, with partial credit scoring, a more robust, real-world methodology. This work extends previous research by examining how the manipulation of scoring methodology has the potential to alter outcomes when testing hypotheses, or specifically, when looking for significant differences between groups of students. Datasets from ASSISTments and Cognitive Tutor are used to assess the implications of data availability and manipulation within twelve mathematics skills. A resampling approach is used to determine the size of equivalent samples of high- and low-performing students required to reliably differentiate performance when considering each scoring methodology. Results suggest that in eleven out of twelve observed skills, partial credit offers more efficient group differentiation, increasing analytic power and reducing Type II error. Alternative applications of this approach and implications for the Learning Analytics community are discussed.

Towards knowledge-based tutors: a survey and appraisal of Intelligent Tutoring Systems

1991 ◽  
Vol 6 (2) ◽  
pp. 59-95 ◽  
Author(s):  
Tomas Sokolnicki
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Tutoring Systems ◽  
Computer Based Training ◽  
Training Systems ◽  
Knowledge Based ◽  
Engineering Discipline ◽  
Key Points ◽  
Key Issues ◽  
Computer Based

AbstractIntelligent tutoring systems can be seen as a next step for computer-based training systems, but also as an important by-product of knowledge-based expert systems. This paper surveys the development and progress in the area, with a special emphasis on the potential for an emerging engineering discipline as opposed to a mere crafting of systems. Major components in intelligent tutoring systems as realized so far are discussed, and key issues for successful future development identified. Knowledge representation, student modelling, planning, natural language issues, explanations and learning are discussed in more depth as being the cornerstones of both tutoring systems and artificial intelligence. Examples from specific implementations are used to illustrate key points. In the concluding discussion we present our attempt at dealing with some of the problems facing the area. In the project Knowledge-Linker, we aim at extending the functionality of a knowledge-based system with tutoring capabilities, and suggest one way of explicitly dealing with teaching strategies.

scholarly journals Modeling Human-Like Robustness in an Intelligent Tutoring System

1970 ◽  
Vol 3 (1) ◽  
pp. 2-8
Author(s):  
Hameedullah Kazi ◽  
Peter Haddawy ◽  
Siriwan Suebnukarn
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Medical Problem ◽  
Knowledge Based Systems ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Specific Solution ◽  
Knowledge Based ◽  
Narrow Scope

Intelligent tutoring systems are no different from other knowledge based systems in that they are often plagued by brittleness. Intelligent tutoring systems for problem solving are typically loaded with problem scenarios for which specific solutions are constructed. Solutions presented by students, are compared against these specific solutions, which often leads to a narrow scope of reasoning, where students are confined to reason towards a specific solution. Student solutions that are different from the specific solution entertained by the system are rejected as being incorrect, even though they may be acceptable or close to acceptable. This leads to brittleness in tutoring systems in evaluating student solutions and returning appropriate feedback. In this paper we discuss a few human-like attributes in the context of robustness that are desirable in knowledge based systems. We then present a model of reasoning through which a tutoring system for medical problem-based learning, can begin to exhibit human-like robust behavior in evaluating solutions in a broader context using UMLS, and respond with hints that are mindful of the partial correctness of the student solution.

Intelligent tutoring systems for aviation automation

2000 ◽  
Author(s):  
Christine Mitchel ◽  
Alan Chappell ◽  
W. Gray ◽  
Alex Quinn ◽  
David Thurman
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Tutoring Systems
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