Development of a personal-computer-based intelligent tutoring system

A large number of Intelligent Tutoring Systems (ITSs) have been built since they were first proposed in the early 1970's. Research conducted on the use of the best of these systems has demonstrated their effectiveness in tutoring in selected domains. A prototype ITS for tutoring students in the use of CLIPS language: CLIPSIT (CLIPS Intelligent Tutor) was developed. For an ITS to be widely accepted, not only must it be effective, flexible, and very responsive, it must also be capable of functioning on readily available computers. While most ITSs have been developed on powerful workstations, CLIPSIT is designed for use on the IBM PC/XT/AT personal computer family (and their clones). There are many issues to consider when developing an ITS on a personal computer such as the teaching strategy, user interface, knowledge representation, and program design methodology. Based on experiences in developing CLIPSIT, results on how to address some of these issues are reported and approaches are suggested for maintaining a powerful learning environment while delivering robust performance within the speed and memory constraints of the personal computer

Tools for Tutoring Theoretical Computer Science Topics

This thesis introduces COMPLEXITY TUTOR, a tutoring system to assist in learning abstract proof-based topics, which has been specifically targeted towards the population of computer science students studying theoretical computer science. Existing literature has shown tremendous educational benefits produced by active learning techniques, student-centered pedagogy, gamification and intelligent tutoring systems. However, previously, there had been almost no research on adapting these ideas to the domain of theoretical computer science. As a population, computer science students receive immediate feedback from compilers and debuggers, but receive no similar level of guidance for theoretical coursework. One hypothesis of this thesis is that immediate feedback while working on theoretical problems would be particularly well-received by students, and this hypothesis has been supported by the feedback of students who used the system. This thesis makes several contributions to the field. It provides assistance for teaching proof construction in theoretical computer science. A second contribution is a framework that can be readily adapted to many other domains with abstract mathematical content. Exercises can be constructed in natural language and instructors with limited programming knowledge can quickly develop new subject material for COMPLEXITY TUTOR. A third contribution is a platform for writing algorithms in Python code that has been integrated into this framework, for constructive proofs in computer science. A fourth contribution is development of an interactive environment that uses a novel graphical puzzle-like platform and gamification ideas to teach proof concepts. The learning curve for students is reduced, in comparison to other systems that use a formal language or complex interface. A multi-semester evaluation of 101 computer science students using COMPLEXITY TUTOR was conducted. An additional 98 students participated in the study as part of control groups. COMPLEXITY TUTOR was used to help students learn the topics of NP-completeness in algorithms classes and prepositional logic proofs in discrete math classes. Since this is the first significant study of using a computerized tutoring system in theoretical computer science, results from the study not only provide evidence to support the suitability of using tutoring systems in theoretical computer science, but also provide insights for future research directions

AUTOMATIC GENERATION OF INTELLIGENT TUTORING CAPABILITIES VIA EDUCATIONAL DATA MINING

Intelligent Tutoring Systems (ITSs) that adapt to an individual student’s needs have shown significant improvement in achievement over non-adaptive instruction (Murray 1999). This improvement occurs due to the individualized instruction and feedback that an ITS provides. In order to achieve the benefits that ITSs provide, we must find a way to simplify their creation. Therefore, we have created methods that can use data to automatically generate hints to adapt computer-aided instruction to help individual students. Our MDP method uses data from past student attempts on given problem to generate a graph of likely paths students take to solve a problem. These graphs can be used by educators to clearly understand how students are solving the problem or to provide hints for new students working the problem by pointing them down a successful path to solve the problem. We introduce the Hint Factory which is an implementation of the MDP method in an actual tutor used to solve logic proofs. We show that the Hint Factory can successfully help students solve more problems and show that students with access to hints are more likely to attempt harder problems than those without hints. In addition, we have enhanced the MDP method by creating a “utility” function that allows MDPs to be created when the problem solution may not be labeled. We show that this utility function performs as well as the traditional MDP method for our logic problems. We also created a Bayesian Knowledge Base to combine the information from multiple MDPs into a single corpus that will allow the Hint Factory to provide hints on new problems where no student data exists. Finally, we applied the MDP method to create models for other domains, including Stoichiometry and Algebra. This work shows that it is possible to use data to create ITS capabilities, primarily hint generation, automatically in ways that can help students solve more and more difficult problems, and builds a foundation for effective visualization and exploration of student work for both teachers and researchers

Can Cogency Vanish?

This paper considers whether universally—for all (known) rational beings—an argument scheme or pattern can go from being cogent (well-reasoned) to fallacious. This question has previously received little attention, despite the centrality of the concepts of cogency, scheme, and fallaciousness. I argue that cogency has vanished in this way for the following scheme, a common type of impersonal means-end reasoning: X is needed as a basic necessity or protection of human lives, therefore, X ought to be secured if possible. As it stands (with no further elaboration), this scheme is committed to the assumption that the greater the number of human lives, the better. Although this assumption may have been indisputable previously, it is clearly disputable now. It is a fallacy or non sequitur to make a clearly disputable assumption without providing any justification. Although this topic raises critical issues for practically every discipline, my primary focus is on logical (as opposed to empirical or ethical) aspects of the case, and on implications for practical and theoretical logic. I conclude that the profile of vanishing cogency of the scheme may be unique and is determined by a peculiar combination of contingent universality and changing conditions

Kriesel and Wittgenstein

Georg Kreisel (15 September 1923 - 1 March 2015) was a formidable mathematical logician during a formative period when the subject was becoming a sophisticated field at the crossing of mathematics and logic. Both with his technical sophistication for his time and his dialectical engagement with mandates, aspirations and goals, he inspired wide-ranging investigation in the metamathematics of constructivity, proof theory and generalized recursion theory. Kreisel's mathematics and interactions with colleagues and students have been memorably described in Kreiseliana ([Odifreddi, 1996]). At a different level of interpersonal conceptual interaction, Kreisel during his life time had extended engagement with two celebrated logicians, the mathematical Kurt Gödel and the philosophical Ludwig Wittgenstein. About Gödel, with modern mathematical logic palpably emanating from his work, Kreisel has reflected and written over a wide mathematical landscape. About Wittgenstein on the other hand, with an early personal connection established Kreisel would return as if with an anxiety of influence to their ways of thinking about logic and mathematics, ever in a sort of dialectic interplay. In what follows we draw this out through his published essays—and one letter—both to elicit aspects of influence in his own terms and to set out a picture of Kreisel's evolving thinking about logic and mathematics in comparative relief.Accepted manuscrip