Plan-based delivery composition in intelligent tutoring systems for introductory computer programming

In a shell system for the generation of intelligent tutoring systems, the instructional model that one applies should be variable independent of the content of instruction. In this article, a taxonomy of content elements is presented in order to define a relatively content-independent instructional planner for introductory programming ITS's; the taxonomy is based on the concepts of programming goals and programming plans. Deliveries may be composed by the instantiation of delivery templates with the content elements. Examples from two different instructional models illustrate the flexibility of this approach. All content in the examples is taken from a course in COMAL-80 turtle graphics

TOWARDS THE DEVELOPMENT OF AN AUTOMATIC FEEDBACK SYSTEM FOR PROGRAMMING ASSIGNMENT

Programming problems and assignment are considered essential elements of software engineering and computer scienceeducation. We propose a framework with which student programming assignments can receive automatic feedback on thesemantics of their program codes. The proposed system increases the interest to study and understand the concept of theprogramming subjects. The objective is to assist teachers to promote programming as a subject and increase, increasestudent’s performance while improving the quality of content delivered in computer programing courses.Keywords: Programming, students, framework, feedback, understanding and assignments

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

Research on knowledge representation, machine learning, and knowledge acquisition

Research in knowledge representation, machine learning, and knowledge acquisition performed at Knowledge Systems Lab. is summarized. The major goal of the research was to develop flexible, effective methods for representing the qualitative knowledge necessary for solving large problems that require symbolic reasoning as well as numerical computation. The research focused on integrating different representation methods to describe different kinds of knowledge more effectively than any one method can alone. In particular, emphasis was placed on representing and using spatial information about three dimensional objects and constraints on the arrangement of these objects in space. Another major theme is the development of robust machine learning programs that can be integrated with a variety of intelligent systems. To achieve this goal, learning methods were designed, implemented and experimented within several different problem solving environments

Expert system technology

The expert system is a computer program which attempts to reproduce the problem-solving behavior of an expert, who is able to view problems from a broad perspective and arrive at conclusions rapidly, using intuition, shortcuts, and analogies to previous situations. Expert systems are a departure from the usual artificial intelligence approach to problem solving. Researchers have traditionally tried to develop general modes of human intelligence that could be applied to many different situations. Expert systems, on the other hand, tend to rely on large quantities of domain specific knowledge, much of it heuristic. The reasoning component of the system is relatively simple and straightforward. For this reason, expert systems are often called knowledge based systems. The report expands on the foregoing. Section 1 discusses the architecture of a typical expert system. Section 2 deals with the characteristics that make a problem a suitable candidate for expert system solution. Section 3 surveys current technology, describing some of the software aids available for expert system development. Section 4 discusses the limitations of the latter. The concluding section makes predictions of future trends

A Proposal For An Intelligent Debugging Assistant

There are many ways to find bugs in programs. For example, observed input and output values can be compared to predicted values. An execution trace can be examined to locate errors in control flow. The utility of these and other strategies depends on the quality of the specifications available. The Debugging Assistant chooses the most appropriate debugging strategy based on the specification information available and the context of the bug. Particular attention has been given to applying techniques from the domain of hardware troubleshooting to the domain of software debugging. This has revealed two important differences between the two domains: (1) Unlike circuits, programs rarely come with complete specifications of their behavior, and (2) Unlike circuits, the cost of probing inputs and outputs of programs is low.MIT Artificial Intelligence Laborator

An Intelligent Debugging Tutor For Novice Computer Science Students

Debugging is a necessary aspect of computer science that can be difficult for novices and experienced programmers alike. This skill is mainly self-taught and is generally gained through trial and error, perhaps with some assistance from a professor or other expert figure. Novices encountering their first software defects may have few avenues open to them depending on the environment in which they are learning to program. The evident problem here is that the potential for a student to become stuck, frustrated, and/or losing confidence in their ability to pursue computer science is great. For a student to be successful when working professionally or progressing through academia they need to be able to function independently; trusting their own knowledge on par or above that of others so that their productivity does not rely on the knowledge of someone else. In order to solve this problem an Intelligent Tutoring System for teaching debugging skills to the novice utilizing Case Based Reasoning, Static Program Slicing, and the student\u27s preferred learning style was proposed. Case acquisition and automatic Exercise Generation were also explored. The system built for this research program was evaluated using novice students at the College and High School levels. Results of this evaluation produced statistically significant results at the p\u3c.05 and p\u3c.01 levels, with generated exercises exhibiting significance at the p\u3c.01 level. These results prove that the methodology chosen is a valid approach for the problem described, that the system does in fact teach students how to debug programs, and that the system is capable of successfully generating exercises on the fly

REDIR: Automated Static Detection of Obfuscated Anti-Debugging Techniques

Reverse Code Engineering (RCE) to detect anti-debugging techniques in software is a very difficult task. Code obfuscation is an anti-debugging technique makes detection even more challenging. The Rule Engine Detection by Intermediate Representation (REDIR) system for automated static detection of obfuscated anti-debugging techniques is a prototype designed to help the RCE analyst improve performance through this tedious task. Three tenets form the REDIR foundation. First, Intermediate Representation (IR) improves the analyzability of binary programs by reducing a large instruction set down to a handful of semantically equivalent statements. Next, an Expert System (ES) rule-engine searches the IR and initiates a sensemaking process for anti-debugging technique detection. Finally, an IR analysis process confirms the presence of an anti-debug technique. The REDIR system is implemented as a debugger plug-in. Within the debugger, REDIR interacts with a program in the disassembly view. Debugger users can instantly highlight anti-debugging techniques and determine if the presence of a debugger will cause a program to take a conditional jump or fall through to the next instruction