The ultimate goal of this research was to improve student performance by adjusting an Intelligent Tutoring System's (ITS) coaching strategy based on the student's mood. As a step toward this goal, this study evaluated the relationships between each student's mood variables (pleasure, arousal, dominance and mood intensity), the coaching strategy selected by the ITS and the student's performance. Outcomes included methods to increase the perception of the intelligent tutor to allow it to adapt coaching strategies (methods of instruction) to the student's affective needs to mitigate barriers to performance (e.g. negative affect) during the one-to-one tutoring process. The study evaluated whether the affective state (specifically mood) of the student moderated the student's interaction with the tutor and influenced performance. This research examined the relationships, interactions and influences of student mood in the selection of ITS coaching strategies to determine which strategies were more effective in terms of student performance given the student's mood, state (recent sleep time, previous knowledge and training, and interest level) and actions (e.g. mouse movement rate). Two coaching strategies were used in this study: Student-Requested Feedback (SRF) and Tutor-Initiated Feedback (TIF). The SRF coaching strategy provided feedback in the form of hints, questions, direction and support only when the student requested help. The TIF coaching strategy provided feedback (hints, questions, direction or support) at key junctures in the learning process when the student either made progress or failed to make progress in a timely fashion. The relationships between the coaching strategies, mood, performance and other variables of interest were considered in light of five hypotheses. At alpha = .05 and beta at least as great as .80, significant effects were limited in predicting performance. Highlighted findings include no significant differences in the mean performance due to coaching strategies, and only small effect sizes in predicting performance making the regression models developed not of practical significance. However, several variables including performance, energy level and mouse movement rates were significant, unobtrusive predictors of mood. Regression algorithms were developed using Arbuckle's (2008) Analysis of MOment Structures (AMOS) tool to compare the predicted performance for each strategy and then to choose the optimal strategy. A set of production rules were also developed to train a machine learning classifier using Witten & Frank's (2005) Waikato Environment for Knowledge Analysis (WEKA) toolset. The classifier was tested to determine its ability to recognize critical relationships and adjust coaching strategies to improve performance. This study found that the ability of the intelligent tutor to recognize key affective relationships contributes to improved performance. Study assumptions include a normal distribution of student mood variables, student state variables and student action variables and the equal mean performance of the two coaching strategy groups (student-requested feedback and tutor-initiated feedback ). These assumptions were substantiated in the study. Potential applications of this research are broad since its approach is application independent and could be used within ill-defined or very complex domains where judgment might be influenced by affect (e.g. study of the law, decisions involving risk of injury or death, negotiations or investment decisions). Recommendations for future research include evaluation of the temporal, as well as numerical, relationships of student mood, performance, actions and state variables.

This paper sought to accomplish three goals. First, it provided a systematic, comparative review of several intelligent tutoring systems (ITS). Second, it summarized problems and solutions presented and solved by developers of ITS by consolidating the knowledge of the field into a single review. Third, it provided a unified language from which ITS can be reviewed and understood in the same context. The findings of this review centered on the 5-Component Framework. The first component, the domain model, showed that most ITS are focused on science, technology, and mathematics. Within these fields, ITS generally have mastery learning as the desired level of understanding. The second component, the tutor model, showed that constructivism is the theoretical strategy that informs most ITS. The tutoring tactics employed in the ITS stem from this paradigm. The third component, the student model, describes the several ways ITS infer what a student knows. It described the variety of data that is collected by an ITS and how it is used to build the student model. The fourth component, the interface, revealed that most ITS are now web-based, but vary in their capacity to interact with students. It also showed that user experience is underreported and ought to be included more in the research. Finally, the fifth component, learning gains, demonstrated that ITS are capable of producing learning gains equivalent to a human tutor. However, reporting learning gains does not seem to be a focus of the literature.

No description available.

The purpose of this dissertation was to design, develop and field test an Intelligent Tutoring System (ITS) which I have called Process Oriented Subtraction-Interface for Tutoring or POSIT. POSIT is an Intelligent Tutoring System, developed on a microcomputer, and based on Anderson's (1982, 1987) ACT* model of learning. Unlike the tutoring systems that were developed by Anderson and his colleagues (Anderson, Boyle & Reiser, 1985; Anderson & Reiser, 1985) which focused on the tutoring of students in the context of problem solving, this system focuses on the tutoring of a cognitive skill-subtraction of whole-numbers. Because ACT* theory explicitly describes the interaction of declarative and procedural knowledge (procedural knowledge is dependent on declarative knowledge), this learning theory is ideally suited to the learning of a specific procedure. Further, other "intelligent" systems which have been applied to subtraction (Brown & Burton, 1978; Ohlsson & Langley, 1985; Young & O’Shea, 1981) tend to focus on the answers to subtraction exercises (product oriented). POSIT, on the other hand, is an interactive system that determines errors made by the child as the child attempts to solve subtraction problems. Another difference with previous systems is that POSIT has a teaching component. Other systems assume that instruction has been given at some other point in time prior to the use of the system. One final difference between POSIT and other systems is that it was developed with consideration of the diverse student population that is found in schools. Design decisions were based on the cognitive needs of low-, average- and high-achieving students. Such notions as reading level and complexity of the task were considered with regard to low achieving students. POSIT's ability to allow for a wide variety of algorithms was a consideration with regard to high achieving students and would also benefit students from all levels. The results of the field test of POSIT indicate that the error model used by POSIT was very successful (76% accurate, with potential to improve to between 80 to 90%). In addition, students appear to learn from the system as measured both on the system as well as on a paper and pencil transfer test. / Ed. D.

"Intelligent tutoring systems (ITSs) are computer based tutoring systems that provide individualized tutoring to the students. Building an ITS is recognized to be expensive task in terms of cost and resources. Authoring tools provide a framework and an environment for building the ITSs that help to reduce the resources like skills, time and cost required to build an intelligent tutoring system. In this thesis we have implemented the Cognitive Tutor Authoring Tools (CTAT) and performed experiments to empirically determine the common programming errors that authors tend to make while building an ITS and study what is hard in authoring an ITS. The CTAT were used in a graduate class at Worcester Polytechnic Institute and also at the 4th Summer school organized at the Carnegie Mellon University. Based on the analysis of the experiments we suggest future work to reduce the debugging time and thereby reduce the time required to author an ITS. We also implemented the model tracing algorithm in JESS, evaluated its performance and compared to that of the model tracing algorithm in TDK. This research is funded by the Office of Naval Research (Grant # N00014-0301-0221)."

"Intelligent Tutoring Systems (ITSs), while effective in enhancing students’ problem solving skills, are difficult and time-consuming to build. In order to reduce the length and the complexity of ITS construction, authoring tools are used. These tools provide a solid foundation for creating pedagogical exercises for students, and offer graphical user interfaces that eliminate the need for programming expertise. One of the major problems with today’s authoring tools is that they are still quite intricate and time-consuming to utilize, even for users who are familiar with them. Their steep learning curves often intimidate users who are only interested in creating simple tutoring systems. I have designed and implemented an authoring tool, called Mason, which strips away the visual interface design features of today’s top ITSs, and focuses on the creation of sophisticated pedagogical exercises using a hierarchical domain model. The exercise creation process includes the definition of numerous components, such as: a problem statement, the desired answer to the exercise, the strategies for tutoring students on the mistakes they make while trying to formulate the correct answer, and diagnostic rules for launching the appropriate strategies for specific student errors. The ultimate goal of Mason is to be able to significantly reduce the time needed to author text-based ITSs that are able to diagnose student answers and generate pedagogical dialogue accordingly. This goal was verified by using Mason to replicate the architecture of Ms. Lindquist, a sophisticated ITS for algebra that originally took over a year a construct. The replica was finished in less than a week, and was able to emulate Ms. Lindquist’s dialogue generation accurately with minor limitations."

The present research incorporated the content of basic statistics into the Artificial Intelligence Physics Tutor (ARPHY), which was used as the expert system shell, and investigated the effects of the Artificial Intelligent Statistics Tutor (ARSTAT) as a supplement to learning statistics at the college level. Two classes of an introductory educational statistics course in the Department of Educational Foundations, University of North Texas, were used in the study. The daytime class was used as the experimental group and the evening class was used as the control group. The experimental group's lecture/discussion was supplemented with ARSTAT, and the control group received only lecture/discussion. A one-way analysis of covariance was used to compare students' test scores. No significant difference was found; however, the adjusted mean score of the experimental group was slightly higher than that of the control group. A two-way analysis of covariance showed no significant main effect or interaction between gender and study technique. A second two-way analysis of covariance showed no significant interaction between the students' attitude toward statistics and the study technique used. However, the students with a statistics-positive attitude scored significantly higher on the test than students who had a negative attitude toward statistics. This study concluded that the ARSTAT can be used effectively as a tutor for students taking an introductory course in educational statistics. The following recommendations for further study were made: incorporate more advanced topics of statistics into the ARPHY teaching model; incorporate the ARPHY learning theory and statistical content using another version of LISP language or another programming language such as PROLOG; and compare the ARSTAT tutor to some other kind of supplement to lecture/discussion.

The purpose of this research was to apply machine learning techniques to automate rule generation in the construction of Intelligent Tutoring Systems. By using a pair of somewhat intelligent iterative-deepening, depth-first searches, we were able to generate production rules from a set of marked examples and domain background knowledge. Such production rules required independent searches for both the“if" and“then" portion of the rule. This automated rule generation allows generalized rules with a small number of sub-operations to be generated in a reasonable amount of time, and provides non-programmer domain experts with a tool for developing Intelligent Tutoring Systems.

This research focused on a formal (theory based) approach to designing Intelligent Tutoring System (ITS) authoring tool involving two specific conventional pedagogical theories—Conversation Theory (CT) and Cognitive Apprenticeship (CA). The research conceptualised an Augmented Conversation and Cognitive Apprenticeship Metamodel (ACCAM) based on apriori theoretical knowledge and assumptions of its underlying theories. ACCAM was implemented in an Intelligent Learning Activity Builder System (ILABS)—an ITS authoring tool. ACCAM’s implementation aims to facilitate formally designed tutoring systems, hence, ILABS―the practical implementation of ACCAM― constructs metamodels for Intelligent Learning Activity Tools (ILATs) in a numerical problem-solving context (focusing on the construction of procedural knowledge in applied numerical disciplines). Also, an Intelligent Learning Activity Management System (ILAMS), although not the focus of this research, was developed as a launchpad for ILATs constructed and to administer learning activities. Hence, ACCAM and ILABS constitute the conceptual and practical contributions that respectively flow from this research. ACCAM’s implementation was tested through the evaluation of ILABS and ILATs within an applied numerical domain―the accounting domain. The evaluation focused on the key constructs of ACCAM―cognitive visibility and conversation, implemented through a tutoring strategy employing Process Monitoring (PM). PM augments conversation within a cognitive apprenticeship framework; it aims to improve the visibility of the cognitive process of a learner and infers intelligence in tutoring systems. PM was implemented via an interface that attempts to bring learner’s thought process to the surface. This approach contrasted with previous studies that adopted standard Artificial Intelligence (AI) based inference techniques. The interface-based PM extends the existing CT and CA work. The strategy (i.e. interface-based PM) makes available a new tutoring approach that aimed fine-grain (or step-wise) feedbacks, unlike the goal-oriented feedbacks of model-tracing. The impact of PM—as a preventive strategy (or intervention) and to aid diagnosis of learners’ cognitive process—was investigated in relation to other constructs from the literature (such as detection of misconception, feedback generation and perceived learning effectiveness). Thus, the conceptualisation and implementation of PM via an interface also contributes to knowledge and practice. The evaluation of the ACCAM-based design approach and investigation of the above mentioned constructs were undertaken through users’ reaction/perception to ILABS and ILAT. This involved, principally, quantitative approach. However, a qualitative approach was also utilised to gain deeper insight. Findings from the evaluation supports the formal (theory based) design approach—the design of ILABS through interaction with ACCAM. Empirical data revealed the presence of conversation and cognitive visibility constructs in ILATs, which were determined through its behaviour during the learning process. This research identified some other theoretical elements (e.g. motivation, reflection, remediation, evaluation, etc.) that possibly play out in a learning process. This clarifies key conceptual variables that should be considered when constructing tutoring systems for applied numerical disciplines (e.g. accounting, engineering). Also, the research revealed that PM enhances the detection of a learner’s misconception and feedback generation. Nevertheless, qualitative data revealed that frequent feedbacks due to the implementation of PM could be obstructive to thought process at advance stage of learning. Thus, PM implementations should also include delayed diagnosis, especially for advance learners who prefer to have it on request. Despite that, current implementation allows users to turn PM off, thereby using alternative learning route. Overall, the research revealed that the implementation of interface-based PM (i.e. conversation and cognitive visibility) improved the visibility of learner’s cognitive process, and this in turn enhanced learning—as perceived.

Adaptive tutorial dialogues have been successfully employed by ITSs to facilitate deep learning of conceptual domain knowledge. But none of the approaches used for generating dialogues have been used across instructional domains and tasks. The objective of this project was twofold: (i) to propose a general model that provides adaptive dialogue support in both well- and ill-defined instructional tasks (ii) to explore whether adaptive tutorial dialogues are better than non-adaptive dialogues in acquiring domain knowledge. Our model provides adaptive dialogue support by identifying the concepts that the student has most difficulty with, and then selecting the tutorial dialogues corresponding to those concepts. The dialogues are customised based on the student’s knowledge and explanation skills, in terms of the length and the exact content of the dialogue. The model consists of three parts: an error hierarchy, tutorial dialogues and rules for adapting them. We incorporated our model into EER-Tutor, a constraint-based tutor that teaches database design. The effectiveness of adaptive dialogues compared to non-adaptive dialogues in learning this ill-defined task was evaluated in an authentic classroom environment. The results revealed that the acquisition of the domain knowledge (represented as constraints) of the experimental group who received adaptive dialogues was significantly higher than their peers in the control group with non-adaptive dialogues. We also incorporated our model into NORMIT, a constraint-based tutor that teaches data normalization. We repeated the experiment using NORMIT in a real-world class room environment with a much smaller group of students (18 in NORMIT study vs 65 in EER-Tutor study) but did not find significant differences. We also investigated whether our model could support dialogues in logical database design and fraction addition using paper-based methods. Our evaluation studies and investigations on paper indicated that our model can provide adaptive support for both ill-and well-defined tasks associated with a well-defined domain theory. The results also indicated that adaptive dialogues are more effective than non-adaptive dialogues in teaching the ill-defined task of database design.