Curriculum tree: A knowledge-based architecture for intelligent tutoring systems

Requirements analysis of Multi-Agent Based Intelligent Tutoring SystemsThe agent oriented software engineering research proposes general assumptions for agent oriented software development, while intelligent tutoring system (ITS) research proposes specific ITS architecture and other specific knowledge for ITS development. Both of these views should be taken into consideration while developing multi-agent based ITSs. Thus there is a need for specific approaches for all phases of agent based ITS development which take into consideration main ideas from both agent oriented software engineering and ITS research. In this paper we propose a requirements analysis approach for multi-agent based ITSs. A case study of a simple ITS is included, too. Requirements analysis in the proposed approach consist of two main steps, namely goal modelling and use case modelling. During the goal modelling the main goals of the system are identified and a goal hierarchy for the system is created. During the use case modelling use cases needed to achieve each lower level goal and their descriptions are created. The proposed approach of the requirements analysis is intended to be a part of the full life cycle methodology for multi-agent based ITS development. The developed use case model (especially use case scenarios) is used during the agent interaction design and task definition. Goal hierarchy during the design phase is mainly used for checking, if the results of design achieve all system's goals.

This chapter presents the architecture of an Intelligent Tutoring System for a Neural Networks course that consists of four basic modules. The subject knowledge-base stores an object-oriented representation of the theme knowledge. The student modeling module traces the user interaction with the system and assess goal accomplishment and motivation-towards-achievement. The instructional strategy module in this implementation consists of a knowledge navigation tool based on a concept-space metaphor and an automatic multiple choice question generator. The user interface, based on the concept-space metaphor, provides the means for the student to access hypermedia information that includes theory and examples, the question generator and the neural networks simulators. The description of the architecture is followed by a presentation and analysis of a learning model based on which a new Intelligent Tutoring System architecture using collaborating agents is proposed.

This paper introduces PLAIT, a specific pattern language for architectures of intelligent tutoring systems. A pattern language is a structured collection of interrelated patterns in a specific domain. PLAIT is based on the idea of using patterns in the architectures of learning technology systems, as well as on a number of patterns that have been discovered in the existing architectures of intelligent tutoring systems. Specifically, the paper describes seven patterns that are commonly used in the architectures of intelligent tutoring systems. The seven patterns represent the core of PLAIT.

In this paper an intelligent computer-aided instruction system for teaching normal form determination is described. The general architecture of intelligent tutoring systems is also discussed, as are the results of experience using the tool in an undergraduate database course.

098 Developing an Intelligent tutoring system for prostate cryosurgery

The purpose of this mixed-methods study was to investigate the relationship between teachers’ conceptions of mathematics and their use of intelligent tutoring systems for mathematics instruction. Intelligent tutoring systems are adaptive computer programs which administer mathematics instruction to students based on their cognitive state. A conception is a mixture of beliefs and knowledge. The participants in this study were 93 junior high school mathematics teachers from three school districts in the Midwest. Data were gathered using a two-part online survey. The first part of the survey contained questions about their use of intelligent tutoring systems, graphing calculators, Desmos and dynamic geometry software. The second part of the survey contained Likert questions from the teachers’ version of the Conceptions of Mathematics Inventory. Desmos is a website providing interactive classroom activities and a user-friendly graphing calculator. Dynamic geometry software is a class of interactive geometry programs. The quantitative analysis revealed no statistically significant interactions between teachers’ conception scores and intelligent tutoring system use, or between teachers’ conception scores and how intelligent tutoring systems were used. There were statistically significant interactions between teachers’ conception scores and their use of graphing calculators, Desmos, and dynamic geometry software. The qualitative analysis revealed that teachers used intelligent tutoring systems for differentiation. Teachers used graphing calculators, Desmos, and dynamic geometry software for visual, computational, and exploratory purposes. Teachers exclusively using intelligent tutoring systems to incorporate technology should also incorporate technology which promotes student exploration.

Online learning with Intelligent Tutoring System (ITS) is becoming very popular where the system models the student's learning behavior and presents to the student the learning material (content, questions‐answers, assignments) accordingly. In today's distributed computing environment, the tutoring system can take advantage of networking to utilize the model for a student for students from other similar groups. In the present paper we present a methodology where using Case Based Reasoning (CBR), ITS provides student modeling for online learning in a distributed environment with the help of agents.Student modeling is a key component in any Intelligent Tutoring System (ITS). In today's distributed computing environment, the tutoring system can take advantage of networking to utilize the model for a student for students from other similar group. In the present paper we present a methodology where using Case Based Reasoning (CBR), the ITS provides student modeling for online learning in a distributed environment with the help of agents.The Chapter describes the approach, the architecture, and the agent characteristics for the student modeling in the ITS. This concept can be deployed to develop ITS where the tutor can author and the students can learn locally whereas the ITS can model the students' learning globally in a distributed environment. The advantage of such an approach is that both the learning material (domain knowledge) and students' model can be globally distributed thus enhancing the efficiency of ITS with reducing the bandwidth requirement and complexity of the system.

This study examined Chinese undergraduate medical students’ acceptance and adoption of intelligent tutoring systems (ITSs) using the general extended technology acceptance model for e-learning via a Likert-scale questionnaire. Specifically, it examined the relations between the five antecedents and the four core components in the model (i.e., perceived usefulness (PU), perceived ease of use (PEOU), behavioural intention and actual use of ITSs). The results of PLS-SEM showed that perceived enjoyment was the most influential antecedent as it significantly impacted both PU and PEOU. Both self-efficacy and prior experience only significantly contributed to PEOU but not PU. Both PU and PEOU significantly and positively predicted behavioural intention, which in turn had a significant and positive path to actual use. The results provide some practical implications to teachers as to how to encourage Chinese undergraduate medical students’ adoption of ITSs: by integrating some gamification elements into the learning activities in ITSs to foster students’ enjoyable feelings or familiarising students with using ITSs so that they can quickly adapt to learning through them. This could be achieved by providing guidance in using ITSs via videos, websites or booklets, or at the beginning of the course, inviting senior students to share their perceived advantages and usefulness of using ITSs. Implications for practice or policy: Because of the importance of perceived enjoyment, teachers may integrate some gamification into the learning activities in ITSs to nurture students’ enjoyable feelings. To enable students to quickly accommodate learning via ITSs, institutions may provide students with information on common features of ITSs or how to navigate a specific ITS. Teachers may explicitly explain how learning objectives can be better achieved through using an ITSs so that students will appreciate its usefulness.

The concept of Intelligent Tutoring System (ITS) is used to describe any computer-based educational systems that provide immediate and personalized instruction and feedback to learners, usually without the intervention from a human teacher. This study examined the availability, accessibility, and utilization of ITS applications for teaching among selected secondary schools in Ilorin, Kwara state. The research was a descriptive type using the survey method. A random sampling technique was used to select 150 teachers from 5 secondary schools in Ilorin, Kwara state. Intelligent tutoring systems are not available to secondary school teachers in the Ilorin metropolis; the reaction of the respondents towards the use of ITS application software for instruction was Positive. From the findings of this study, it was concluded that Teachers do not have access to ITS application for instruction as a result of the availability of ITS instruction application. The implication is that if teachers have access to the ITS application for Instruction it will further foster education security. The study recommends that Government and School administrators should ensure adequate power supply and Internet facilities for easy access and utilization of ITS applications for Instruction in the classroom, also teachers who are not in ICT compliance should be encouraged to study further to meet up with the current technological trends.

Intelligent tutoring systems (ITS) are characterized by their direct and adaptive feedback as well as their capability of assessing the knowledge of students and administering exercises based on those assessments. The state of research regarding the effectiveness of ITS in mathematics is inconclusive. Hence, in this study, we examined the impact of utilizing an ITS on the learning gains in mathematics for students in grades 7 and 8. This longitudinal investigation was conducted with students from 55 classes (940 students) in northern Germany. Mathematics performance as well as relevant covariates were measured at the beginning and the end of the school year, and interactions with the ITS were recorded throughout the school year, providing a comprehensive dataset for analysis. Teachers were free in choosing the extent, subjects and methods of ITS usage. In addition, students could also use the ITS on their own. A multilevel analysis revealed that the frequency of ITS usage had no significant effect, neither at the class level nor at the individual level. Our results show that using ITS does not automatically lead to better learning gains. Therefore, future studies need to identify the conditions and practices that contribute to effective ITS use.

This chapter provides information on how to design intelligent tutoring systems for distributed learning to cater to individual learner needs and styles. It argues that intelligent tutoring systems must use the expertise that tutors use in a one-to-one teaching situation to build intelligent tutoring systems for distributed learning. Also, the appropriate psychological and educational theories must be used to build the domain module, student model, and pedagogical module. The components of intelligent tutoring systems are described, and the author makes the case that to build effective intelligent tutoring systems, a multidisciplinary team should be involved. Finally, the author identifies trends that are influencing the development of intelligent tutoring systems and suggests areas for future research and development.

This chapter provides information on how to design intelligent tutoring systems for distributed learning to cater to individual learner needs and styles. It argues that intelligent tutoring systems must use the expertise that tutors use in a one-to-one teaching situation to build intelligent tutoring systems for distributed learning. Also, the appropriate psychological and educational theories must be used to build the domain module, student model, and pedagogical module. The components of intelligent tutoring systems are described, and the author makes the case that to build effective intelligent tutoring systems, a multidisciplinary team should be involved. Finally, the author identifies trends that are influencing the development of intelligent tutoring systems and suggests areas for future research and development.

This chapter provides information on how to design intelligent tutoring systems for distributed learning to cater to individual learner needs and styles. It argues that intelligent tutoring systems must use the expertise that tutors use in a one-to-one teaching situation to build intelligent tutoring systems for distributed learning. Also, the appropriate psychological and educational theories must be used to build the domain module, student model, and pedagogical module. The components of intelligent tutoring systems are described, and the author makes the case that to build effective intelligent tutoring systems, a multidisciplinary team should be involved. Finally, the author identifies trends that are influencing the development of intelligent tutoring systems and suggests areas for future research and development.

Due to the rapid growth of the use of computers and increasing use of the Internet in education a large number of Web-based educational applications have been developed and implemented. However, very few of them are pedagogically intelligent and interactive for learning purposes. The Web-based intelligent learning has become more effective in the past decade due to increasing use of the Internet in education. A literature search indicates that there is a lack of relevant comprehensive research concerning the efficiency of computer-assisted instructions used in engineering education. The main focus of the research described in this chapter is on the comprehensive review of design and development of the Web-based authoring tool for an Intelligent Tutoring System in engineering education. The chapter outlines and discusses important issues of the development of Intelligent Tutoring System (ITS) in engineering education with an example of the development of a Web-Based Computer-Assisted Tutorials and Laboratory Procedures (WCALP).KeywordsEducational SoftwareExpert ModelInstructional SoftwareEducational Technology PublicationNatural Language DialogueThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

We present an approach to managing the side effect of information updates in the student model (SM). The approach is based on fuzzy logic and fuzzy reasoning on the knowledge structure in the SM. The SM includes three parts: a cognitive model, a behavioural model and an inference engine. The cognitive model is an overlay on curriculum knowledge structures, the behaviour model contains affective and conative values of the student and the inference engine aims to manage updates occurring in the SM during the learning process. As updates imply the evolution of the knowledge structure of the student the propagation module evaluates the possible impacts of the updated information on other related information. This is done by activating a fuzzy reasoning on knowledge networks in the curriculum and deducing new information in the SM. The control module handles conflict situations by considering factors such as the information source and the confidence degree.