Amruth N. Kumar

Reasoning about function and its applications to engineering

We provide an introduction to the field of functional representation and reasoning from an engineering point of view. Our main goals are to: (i) present and clarify the notion of function with the aim of unifying diverse perspectives, (ii) identify the various current approaches to represent function, and (iii) highlight the potential of functional reasoning for engineering applications.

Results from the evaluation of the effectiveness of an online tutor on expression evaluation

Researchers have been developing online tutors for various disciplines, including Computer Science. Educators are increasingly using online tutors to supplement their courses. Are online tutors effective? Can they help students learn? If so, what features contribute to their effectiveness? We will examine these questions in the context of an online tutor that we developed for introductory Computer Science. The tutor is designed to help students learn expression evaluation in C++/Java.We evaluated the tutor over several years, in multiple sections of Computer Science I each year. We used controlled tests with differential treatments, and used pre and post-tests to evaluate the effectiveness of the tutor. Our results show that online tutors indeed help students learn. Students who use the tutor for practice learn better than those who use a printed workbook. Students who receive both graphic visualization and text explanation learn better than those who receive only graphic visualization. Students who use graphic visualization learn better than those who receive no explanation. These results will be of interest to both developers and users of online tutors.

Using robots in an undergraduate artificial intelligence course: an experience report

In this paper, we report our experience using robots in the artificial intelligence course we taught in Fall 2000. Our objective was to use robots to reinforce the traditional concepts of search and expert systems. We wanted the robots to be simple to build, yet powerful enough to illustrate AI concepts. In this paper, we discuss our choice of robot, describe the projects we assigned and list the problems our students encountered carrying out those projects. We surveyed our class regarding the use of robots in this course at the end of the semester. We discuss the results of this survey, which we believe, make a strong case for using robots in the AI course.

A tutor for counter-controlled loop concepts and its evaluation

We have developed a Web-based tutor for teaching and testing counter-controlled loop concepts in C++. The tutor is designed to promote problem-based learning. It repeatedly generates problems, grades users answers and provides feedback about the correct answers. This paper describes the design of the tutor, outlines a test that we used to evaluate its effectiveness, and presents the results of die test. The test confirmed our hypothesis that using the tutor helps improve student learning. The improvement is statistically significant. This tutor can be used for practice or testing in Computer Science I.

Model-Based Reasoning for Domain Modeling in a Web-Based Intelligent Tutoring System to Help Students Learn to Debug C++ Programs

The benefits of using Model-Based Reasoning for domain modeling are several-fold. We analyze these benefits and illustrate them in the context of a Web-based Intelligent Tutoring System. The system is designed to teach students to analyze and debug C++ programs for semantic and run-time errors. We have evaluated one instance of the Model-Based tutor, which deals with debugging pointers in C++, in several sections of Computer Science II course. We will present the results of these evaluations, which confirm the learnability of Model-Based tutors.

Learning Programming by Solving Problems

We have been developing tutors to help students learn programming concepts by solving problems. In this paper, we will discuss the use of problem-solving in Computer Science, the effectiveness of using problem-solving tutors to learn programming concepts, and the pedagogical relationship between solving problems and learning to write programs. We will also present the design and results from the evaluation of one of our tutors.

A scalable solution for adaptive problem sequencing and its evaluation

We propose an associative mechanism for adaptive generation of problems in intelligent tutors. Our evaluations of the tutors that use associative adaptation for problem sequencing show that 1) associative adaptation targets concepts less well understood by students; and 2) associative adaptation helps students learn with fewer practice problems. Apart from being domain-independent, the advantages of associative adaptation compared to other adaptive techniques are that it is easier to build and is scalable.

Learning the interaction between pointers and scope in C

Traditionally, pointers, and their interaction with scope in C++ have been a source of frustration and confusion for students in our Computer Science II course. Since problem-solving is known to improve learning [6], we set out to develop software that would help our students better understand these concepts by repeatedly solving problems based on them.In this paper, we will first describe the design and features of this software. We conducted tests in two sections of our Computer Science II course this fall to evaluate the effectiveness of using this software. The results have been very encouraging: the class average in both the sections increased by 100% from the pretest to the post-test. We will also present the design and results of these tests.

Dynamically generating problems on static scope

Solving problems is an integral part of learning in Computer Science. In order to provide students with a vast supply of problems with which to practice, we propose to use applets that automatically generate problems. In this paper, we first discuss the capabilities required of such applets, and then, present the design and features of an applet we have developed to automatically generate problems on static scope in Pascal.

Increasing Adoption of Smart Learning Content for Computer Science Education

Computer science educators are increasingly using interactive learning content to enrich and enhance the pedagogy of their courses. A plethora of such learning content, specifically designed for computer science education, such as visualization, simulation, and web-based environments for learning programming, are now available for various courses. We call such content smart learning content. However, such learning content is seldom used outside its host site despite the benefits it could offer to learners everywhere. In this paper, we investigate the factors that impede dissemination of such content among the wider computer science education community. To accomplish this we surveyed educators, existing tools and recent research literature to identify the current state of the art and analyzed the characteristics of a large number of smart learning content examples along canonical dimensions. In our analysis we focused on examining the technical issues that must be resolved to support finding, integrating and customizing smart learning content in computer science courses. Finally, we propose a new architecture for hosting, integrating and disseminating smart learning content and discuss how it could be implemented based on existing protocols and standards.