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In the history of Intelligent Tutoring Systems, SOPHIE (Brown, Burton, and Bell, 1974), now considered a classic, contained many important ideas and features. One of these was its natural language user interface. Today, the trend has moved away from natural language interfaces towards graphical ones although the argument in favour of natural language user interfaces, both from Human Computer Interaction and natural language researchers, still persist. Is this argument correct? This thesis explores this question by investigating how SOPHIE might be re-implemented with a graphical direct manipulation interface instead of a natural language one, with the goal of improving its standard of usability. It begins by analysing the features that seem to have been central to SOPHIE's usability. These, it argues, were not so much an ability to accept well formed complete English sentences, as an ability to accept and interpret correctly a wide range of abbreviated inputs. Two models of interaction, Circuit 1, a pilot, and Circuit II, a fairly full implementation of SOPHIE were implemented and tested. Both employ free-order syntax that allows users to specify the components of a full command in any order. The combination of deixis and free-order syntax supported allows completely general ellipsis which achieves, in extended interaction sequences, the same economy and naturalness that SOPHIE achieved through its use of anaphora and ellipsis. Whilst the free-order syntax. technique is little used at present in user interfaces, the results of observational studies conducted have shown that it saves users time and convenience. Thus, considering key linguistic features of a natural language user interface has shown how novel features can enhance the usability of direct manipulation interfaces. This thesis argues that user interfaces can be improved by employing structures found in natural language or at least conversation which can be constructed within direct manipulation interface styles. This approach was further expanded to support topic shifts between different circuit contexts. Circuit II, like SOPHIE, supports three different topics: normal circuit behaviour, a circuit with an unknown fault, and circuits with user-hypothesised faults. Drawing on Reichman's (1981) work, Circuit II uses natural language cue phrases of the type "by the way", re-implemented in the direct manipulation style, to facilitate shifts between topics in a smoother and more natural way than SOPHIE which , used clumsy explicit commands.

With the help of the internet, teaching is not constrained in the traditional classroom pedagogy; the instructors can put the course material on the website and allow the students go on to the course webpage as an alternative way to learn the domain knowledge. The problem here is how to design a web-based system that is intelligent and adaptive enough to teach the students domain knowledge in Programmable Logic Controller (PLC). In my research, I proposed a system architecture which combines the pre-test, cased-based reasoning (i.e., heuristic functions), tutorials and tests of the domain concepts, and post-test (i.e., including pre-exam and post-exam) to customize students’ needs according to their knowledge levels and help them learn the PLC concepts effectively. I have developed an intelligent tutoring system which is mainly based on the feedback and learning preference of the users’ questionnaires. It includes many pictures, colorful diagrams, and interesting animations (i.e., switch control of the user’s rung configuration) to attract the users’ attention. From the model simulation results, a knowledge proficiency effect occurs on problem-solving time. If the students are more knowledgeable about PLC concepts, they will take less time to complete problems than those who are not as proficient. Additionally, from the system experiments, the results indicate that the learning algorithm in this system is robust enough to pinpoint the most accurate error pattern (i.e., almost 90 percent accuracy of mapping to the most similar error pattern), and the adaptive system will have a higher accuracy of discerning the error patterns which are close to the answers of the PLC problems when the databases have more built-in error patterns. The participant evaluation indicates that after using this system, the users will learn how to solve the problems and have a much better performance than before. After evaluating the tutoring system, we also ask the participants to submit the survey (feedback), which will be taken into serious consideration in our future work.

With the help of the internet, teaching is not constrained in the traditional classroom pedagogy; the instructors can put the course material on the website and allow the students go on to the course webpage as an alternative way to learn the domain knowledge. The problem here is how to design a web-based system that is intelligent and adaptive enough to teach the students domain knowledge in Programmable Logic Controller (PLC). In my research, I proposed a system architecture which combines the pre-test, cased-based reasoning (i.e., heuristic functions), tutorials and tests of the domain concepts, and post-test (i.e., including pre-exam and post-exam) to customize students’ needs according to their knowledge levels and help them learn the PLC concepts effectively. I have developed an intelligent tutoring system which is mainly based on the feedback and learning preference of the users’ questionnaires. It includes many pictures, colorful diagrams, and interesting animations (i.e., switch control of the user’s rung configuration) to attract the users’ attention. From the model simulation results, a knowledge proficiency effect occurs on problem-solving time. If the students are more knowledgeable about PLC concepts, they will take less time to complete problems than those who are not as proficient. Additionally, from the system experiments, the results indicate that the learning algorithm in this system is robust enough to pinpoint the most accurate error pattern (i.e., almost 90 percent accuracy of mapping to the most similar error pattern), and the adaptive system will have a higher accuracy of discerning the error patterns which are close to the answers of the PLC problems when the databases have more built-in error patterns. The participant evaluation indicates that after using this system, the users will learn how to solve the problems and have a much better performance than before. After evaluating the tutoring system, we also ask the participants to submit the survey (feedback), which will be taken into serious consideration in our future work.

Teamwork is crucial to many disciplines, from activities such as organized sports to economic and military organizations. Team training is difficult and as yet there are few automated tools to assist in the training task. As with the training of individuals, effective training depends upon practice and proper training protocols. In this research, we defined a team training framework for constructing team training systems in domains involving command and control teams. This team training framework provides an underlying model of teamwork and programming interfaces to provide services that ease the construction of team training systems. Also, the framework enables experimentation with training protocols and coaching to be conducted more readily, as team training systems incorporating new protocols or coaching capabilities can be more easily built. For this framework (called CAST-ITT) we developed an underlying intelligent agent architecture known as CAST (Collaborative Agents Simulating Teamwork). CAST provides the underlying model of teamwork and agents to simulate virtual team members. CAST-ITT (Intelligent Team Trainer) uses CAST to also monitor trainees, and support performance assessment and coaching for the purposes of evaluating the performance of a trainee as a member of a team. CAST includes a language for describing teamwork called MALLET (Multi-Agent Logic Language for Encoding Teamwork). MALLET allows us to codify the behaviors of team members (both as virtual agents and as trainees) for use by CAST. In demonstrating CAST-ITT through an implemented team training system called TWP-DDD we have shown that a team training system can be built that uses the framework (CAST-ITT) and has good performance and can be used for achieving real world training objectives.

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.

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Throughout history education has been restricted to a relatively small percentage of the world's population. The cause can be attributed to a number of factors; how- ever, it has been chiefly due to excessive cost. As we enter the information age it becomes conceivable to make education freely available to anyone, anywhere, any- time. The Intelligent Tutoring System is an automated teaching system designed to improve through experience, eventually learning to tailor its teaching to perfectly match each individual student's needs and preferences. In this dissertation we describe a template which we use for building problem-oriented skill teaching intelligent tutoring systems based on a Dynamic Bayes network framework. We present two case studies in which the template is adapted to very different teaching domains, documenting in each case the process of building, training, and testing the resulting ITS. In both case studies, the performance of the ITS is validated through human subject experiments. The results of these studies show that our template is a viable technique for designing ITSs that teach in skill based domains. We also show that, while conducting artificial intelligence research on the design of an ITS and collecting data for use in that regard, we can concurrently run educational research experiments. We find that the two are quite inextricably tied and that showing good general results regarding the performance of the ITS is not sufficient; a strong understanding of the experience of the students is also required. We report some interesting results covering the effect of choice in learning and a gender bias that shows up in our tutoring system.

This study explores the ability of an Intelligent Tutoring System (ITS) to increase parental engagement in student learning. A parental notification feature was developed for the web-based ASSISTments ITS that allows parents to log into their own accounts and access detailed data about their students' performance. Parents from a local middle school were then invited to create accounts and answer a survey assessing how engaged they felt they were in their students' education. A randomized controlled experiment was run during which weekly automated messages were sent home to parents regarding their students' assignments and how they were performing. After having them take a post-survey, it was found that access to this data caused parents to become more involved in their students' education. Additionally, this led to increased student performance in the form of higher homework completion rates. Qualitative feedback from parents was very positive.

Using electronic portfolios (e-portfolios) to assist learning is an important component of future educational models. A portfolio is a purposeful collection of student work that exhibits the student's efforts, progress and achievements in one or more areas. An e-portfolio contains a variety of information about a person's learning outcomes, such as artifacts, assertions from others, self-reflective information and presentation for different purposes. E-portfolios become sources of evidence for claims about prior conceptual knowledge or skills. This thesis investigates using the information contained in e-portfolios to initialize the learner model for an intelligent tutoring system. We examine the information model from the e-portfolio standardized specification and present a method that may assist users in initializing learner models using e-portfolios as evidence for claims about prior conceptual knowledge or skills. We developed the EP-LM system for testing how accurately a learner model can be built and how beneficial this approach can be for reflective and personalized learning. Experimental results are presented aiming at testing whether accurate learner models can be created through this approach and whether learners can gain benefits in reflective and personalized learning. Monitoring this process can also help ITS developers and experts identify how an initial learner model can automatically arise from an e-portfolio. Additionally, a well-structured learner model, generated by an intelligent tutoring system also can be attached to an e-portfolio for further use by the owner and others.