A computer simulator for use in an introductory course on computer systems and assembly language programming

This paper describes a simulator for a computer system comprising a simple central processor, memory and a variety of peripheral devices. The simulator is used in the laboratory component of an introductory course on computer systems and assembly language programming. The simulator exploits the limited “cursor graphics” capabilities of commonly available display terminals; the graphics are used to present a view of the internal operations of the simulated computer.     

Expert tutoring system for teaching computer programming languages

This paper presents an Expert tutoring system (E-TCL) for teaching computer programming languages through WWW. In this version, many teachers can cooperate together to put the curriculum of one/more computer programming language(s). Their contributions may include: (a) add or modify the commands' structure that will be taught; (b) generate different tutoring dialogs for the same command; and (c) generate different tutoring styles. On the contrary, the students can access the system through WWW, select any language they want to learn as well as the style of presentation they prefer and they can exchange their experiences. A personal assistant agent for teachers (PAA-T), a personal assistant agent for students (PAA-S) with an adaptive interface, and tutoring agent (TA) has been built. The TA resides on the server side and communicates via HTTP and IIOP with both the PAA-T and PAA-S on the clients side. This structure allows customization of the PAA-T and PAA-S to the needs of the teachers and students, without putting extra burden on the server. In addition, this allows having many teacher agents attending to the needs of a single or multiple student agent(s).     

Automatic summary assessment for intelligent tutoring systems

Summary writing is an important part of many English Language Examinations. As grading students’ summary writings is a very time-consuming task, computer-assisted assessment will help teachers carry out the grading more effectively. Several techniques such as latent semantic analysis (LSA), n-gram co-occurrence and BLEU have been proposed to support automatic evaluation of summaries. However, their performance is not satisfactory for assessing summary writings. To improve the performance, this paper proposes an ensemble approach that integrates LSA and n-gram co-occurrence. As a result, the proposed ensemble approach is able to achieve high accuracy and improve the performance quite substantially compared with current techniques. A summary assessment system based on the proposed approach has also been developed.     

TEx-Sys model for building intelligent tutoring systems

Special classes of asynchronous e-learning systems are the intelligent tutoring systems which represent an advanced learning and teaching environment adaptable to individual student’s characteristics. Authoring shells have an environment that enables development of the intelligent tutoring systems. In this paper we present, in entirety, for the first time, our approach to research, development and implementation related to intelligent tutoring systems and ITS authoring shells. Our research relies on the traditional intelligent tutoring system, the consideration that teaching is control of learning and principles of good human tutoring in order to develop the Tutor–Expert System model for building intelligent tutoring systems in freely chosen domain knowledge. In this way we can wrap up an ongoing process that has lasted for the previous fifteen years. Prototype tests with the implemented systems have been carried out with students from a primary education to an academic level. Results of those tests are advantageous, according to surveys, and the implemented and deployed software satisfies functionalities and actors’ demands.     

Assessment frequency in introductory computer programming disciplines

Introductory computer programming disciplines commonly show a significant failure rate.Although several reasons have been advanced for this state of affairs, we argue that for a beginner student it is hard to understand the difference between know-about disciplines and know-how-to-do-it disciplines, such as computer programming. This leads to failure because when students understand they are not able to solve a programming problem it is usually too late to catch all the time meanwhile lost.In order to make students critically analyse their progress, instructors have to provide them with realistic indicators of their performance.To achieve this awareness and to trigger corrective actions in a timely manner there is a need to increase assessment frequency. This paper discusses how this can be done, analyses benefits of the proposed approach and presents data on the effects of changes in assessment frequency for a university first year course in fundamentals of computer programming.     

Automatic program debugging for intelligent tutoring systems

Program debugging is an important part of the domain expertise required for intelligent tutoring systems that teach programming languages. This article explores the process by which student programs can be automatically debugged in order to increase the instructional capabilities of these systems. The research presented provides a methodology and implementation for the diagnosis and correction of nontrivial recursive programs. In this approach, recursive programs are debugged by repairing induction proofs in the Boyer-Moore logic. The induction proofs constructed and debugged assert the computational équivalence of student programs to correct exemplar solutions. Exemplar solutions not only specify correct implementations but also provide correct code to replace buggy student code. Bugs in student code are repaired with heuristics that attempt to minimize the scope of repair. The automated debugging of student code is greatly complicated by the tremendous variability that arises in student solutions to nontrivial tasks. This variability can be coped with, and debugging performance improved, by explicit reasoning about computational semantics during the debugging process. This article supports these claims by discussing the design, implementation, and evaluation of Talus, an automatic debugger for LISP programs, and by examining related work in automated program debugging. Talus relies on its abilities to reason about computational semantics to perform algorithm recognition, infer code teleology, and to automatically detect and correct nonsyntactic errors in student programs written in a restricted, but nontrivial, subset of LISP. Solutions can vary significantly in algorithm, functional decomposition, role of variables, data flow, control flow, values returned by functions, LISP primitives used, and identifiers used. Solutions can consist of multiple functions, each containing multiple bugs. Empiricial evaluation demonstrates that Talus achieves high performance in debugging widely varying student solutions to challenging tasks.     

Cooperative learning strategies for intelligent tutoring systems

Intelligent tutoring systems (ITS) are evolving towards a more cooperative relationship between the system and the student. More and more, learning is considered as a constructive process rather than a simple transfer of knowledge. This trend has brought to light new cooperative tutoring strategies. One of these tutoring strategies, the learning companion, designed to overcome some of the limitations of the classical tutoring model, involves a student and two simulated participants: a tutor and another student. More recently, a new strategy, learning by disturbing, has been proposed. In this strategy, the simulated student is a troublemaker whose role is to deliberately disturb the human student. This article describes the learning by disturbing strategy by contrasting it with the learning companion strategy. In addition, links are drawn between this new strategy and the psychology of learning, in particular the cognitive dissonance theory. An indicator has been developed that measures discord between the ideas, helping to pinpoint the concepts that are most likely to be misunderstood by the learner. Doing so allows one to plan more efficiently the interventions of the troublemaker.     

Student modelling in intelligent tutoring systems

Student modelling is a special type of user modelling which is relevant to the adaptability of intelligent tutoring systems. This paper reviews the basic techniques which have been used in student modelling and discusses issues and approaches of current interest. The role of a student model in a tutoring system and methods for representing information about students are discussed. The paper concludes with an overview of some unresolved issues and problems in student modelling.Electric Brain Company     

Using planning techniques in intelligent tutoring systems

This paper proposes an architecture for building better Computer-Assisted Instruction (CAI) programs by applying and extending Artificial Intelligence (AI) techniques which were developed for planning and controlling the actions of robots. A detailed example shows how programs built according to this architecture are able to plan global teaching strategies using local information. Since the student's behavior can never be accurately predicted, the pre-planned teaching strategies may be foiled by sudden surprises and obstacles. In such cases, the planning component of the program is dynamically reinvoked to revise the unsuccessful strategy, often by recognizing student misconceptions and planning a means to correct them. This plan-based teaching strategy scheme makes use of global course knowledge in a flexible way that avoids the rigidity of earlier CAI systems. It also allows larger courses to be built than has been possible in most AI-based “intelligent tutoring systems” (ITSs), which seldom address the problem of global teaching strategies.     

Some techniques for building mathematical intelligent tutoring systems

Abstract

Some techniques that are useful in building intelligent tutoring systems for mathematics are described. These techniques have been implemented in Integration-Kid, an intelligent tutoring system for integral calculus. Its production system is a language which helps the problem-solver to interact with students. For the mathematical problem-solver, we have adopted term-rewriting rules which represent basic mathematical knowledge. The situation map is a representation which lays out all possible situations in the process of solving a complex problem. We shall also describe an algorithm which transforms a linear mathematical expression to a readable, two-dimensional expression.     

Graphical representation of help knowledge for intelligent tutoring systems

Today an expected facility of interactive computer systems and application programs is an extensive, well-designed, and well-written online help system. A help system frequently provides online tutorial text concerning the operation of the system or application. In the case of Intelligent Tutoring Systems (ITS), an embedded help system provides help with topics in the training domain. In this environment, the help system not only provides user initiated help, but also help text introduced by the embedded computer expert for the purpose of directing or correcting student actions.

Explanatory material contained in help systems is called help documentation, and is usually accessed through a “HELP” command. Identification of relevant help texts is difficult for a user, and is a special case of the general information retrieval problem.

This paper discusses methods for the retrieval of help knowledge items using graphical, on-screen, tree structures. The configuration of these “study trees” conforms to the hierarchical nature of the tutorial information being referenced. Selection of natural language terms affords initial entry to the trees. A user of the help system browses the trees directly on-screen and consults the subject help texts at will. The study tree presentation facilitates effective selection of pertinent help texts and relates the collection of help material into an integrated whole.     

Transformation-based diagnosis of student programs for programming tutoring systems

A robust technology that automates the diagnosis of students' programs is essential for programming tutoring systems. Such technology should be able to determine whether programs coded by a student are correct. If a student's program is incorrect, the system should be able to pinpoint errors in the program as well as explain and correct the errors. Due to the difficulty of this problem, no existing system performs this task entirely satisfactorily, and this problem still hampers the development of programming tutoring systems. This paper describes a transformation-based approach to automate the diagnosis of students' programs for programming tutoring systems. Improved control-flow analysis and data-flow analysis are used in program analysis. Automatic diagnosis of student programs is achieved by comparing the student program with a specimen program at the semantic level after both are standardized. The approach was implemented and tested on 525 real student programs for nine different programming tasks. Test results show that the method satisfies the requirements stated above. Compared to other existing approaches to automatic diagnosis of student programs, the approach developed here is more rigorous and safer in identifying student programming errors. It is also simpler to make use of in practice. Only specimen programs are needed for the diagnosis of student programs. The techniques of program standardization and program comparison developed here may also be useful for research in the fields of program understanding and software maintenance.     

Developing hybrid intelligent tutoring and hypertext systems

Abstract

The objective of this paper is to present an alternative paradigm to the traditional Knowledge Based Expert Systems Paradigm for developing a full-scale Intelligent Tutoring System that has dominated for years Intelligent Tutoring Systems development. This alternative paradigm which integrates Minsky's Frames with hypertext has been successfully deployed so far in the development of PEDRO, an Intelligent Tutoring System for foreign language learning, SONATA, an Intelligent Tutoring System for music theory learning and INTUITION, an Intelligent Tutoring System for Gaming-Simulation.     

Integrating affective learning into intelligent tutoring systems

Affectivity has influence in learning face-to-face environments and improves some aspects in students, such as motivation. For that reason, it is important to integrate affectivity elements into virtual environments. We propose a conceptual model that suggests which elements of tutor, student and dialogue should be integrated and implemented into learning systems. We design an ontology guided by methontology, and apply a mathematical evaluation (OntoQA) to determine the richness of the proposed model. The mathematical evaluation states that the proposed model has relationship richness and horizontal nature. We developed a software application implementing the conceptual model in order to prove its effectivity to generate students’ motivation. The findings suggest that the implemented affective learning ontology impacts positively the motivation in students with low academic performance, in female students and in engineering students.     

An architecture for a self-improving instructional planner for intelligent tutoring systems

Machine instructional planners use changing and uncertain data to incrementally configure plans and control the execution and dynamic refinement of these plans. Current instructional planners cannot adequately plan, replan, and monitor the delivery of instruction. This is due in part to the fact that current instructional planners are incapable of planning in a global context, developing competing plans in parallel, monitoring their planning behavior, and dynamically adapting their control behavior. In response to these and other deficiencies of instructional planners a generic system architecture based on the blackboard model was implemented. This self-improving instructional planner (SUP) dynamically creates instructional plans, requests execution of these plans, replans, and improves its planning behavior based on a student's responses to tutoring. Global planning was facilitated by explicitly representing decisions about past, current, and future plans on a global data structure called the plan blackboard. Planning in multiple worlds is facilitated by labeling plan decisions by the context in which they were generated. Plan monitoring was implemented as a set of monitoring knowledge sources. The flexible control capability for instructional planner was adapted from the blackboard architecture BB1. The explicit control structure of SUP enabled complex and flexible planning behavior while maintaining a simple planning architecture.     

Development of intelligent tutoring systems using knowledge structures

The aim of this work is to develop a tool for teachers, without necessarily software development experience, to elaborate tutoring applications on a given domain. The teacher makes use of stored knowledge to choose the required contents so that the system automatically generates exercises. These exercises are completed and evaluated in real time, according to the student's reality, being able to mediate with the pupil to achieve the maximum session utilization. Control of the learning process is distributed among the student, the teacher, and the system. The student may choose the activities he/she likes to carry out, within a sub-group specified by the teacher, whereas the system specifies the exercise complexity, on the basis of the pupils performance. This work is grounded on knowledge re-utilization that makes use of pre-defined knowledge structures. These structures can be edited by a teacher to generate activities. These are carried out by a simulator, controlled by an expert system, that interacts with the student adjusting to the pupil's needs. Elements from both the instructionist and constructivist model were used. It was implemented for the practice of certain skills related to math in pre-school children.     

Applications of Bayesian decision theory to intelligent tutoring systems

The purpose of this paper is to consider some applications of Bayesian decision theory to intelligent tutoring systems. In particular, it will be indicated how the problem of adapting the appropriate amount of instruction to the changing nature of student's capabilities during the learning process can be situated within the general framework of Bayesian decision theory. Two basic elements of this approach will be used to improve instructional decision making in intelligent tutoring systems. First, it is argued that in many decision-making situations the linear loss model is a realistic representation of the losses actually incurred. Second, it is shown that the psychometric model relating observed test scores to the true level of functioning can be represented by Kelley's regression line from classical test theory. Optimal decision rules will be derived using these two features.     

Modeling self-efficacy in intelligent tutoring systems: An inductive approach

Self-efficacy is an individual’s belief about her ability to perform well in a given situation. Because self-efficacious students are effective learners, endowing intelligent tutoring systems with the ability to diagnose self-efficacy could lead to improved pedagogy. Self-efficacy is influenced by (and influences) affective state. Thus, physiological data might be used to predict a student’s level of self-efficacy. This article investigates an inductive approach to automatically constructing models of self-efficacy that can be used at runtime to inform pedagogical decisions. It reports on two complementary empirical studies. In the first study, two families of self-efficacy models were induced: a static self-efficacy model, learned solely from pre-test (non-intrusively collected) data, and a dynamic self-efficacy model, learned from both pre-test data as well as runtime physiological data collected with a biofeedback apparatus. In the second empirical study, a similar experimental design was applied to an interactive narrative-centered learning environment. Self-efficacy models were induced from combinations of static and dynamic information, including pre-test data, physiological data, and observations of student behavior in the learning environment. The highest performing induced naïve Bayes models correctly classified 85.2% of instances in the first empirical study and 82.1% of instances in the second empirical study. The highest performing decision tree models correctly classified 86.9% of instances in the first study and 87.3% of instances in the second study.     

Adaptive systems: from intelligent tutoring to autonomous agents

Computer systems which can automatically alter aspects of their functionality or interface to suit the needs of individuals or groups of users have appeared over the years in a variety of guises. Most recently, attention has focused on intelligent interface agents, which are seen as specialised, knowledge-based systems acting on behalf of the user in some aspect of the interaction. Similar requirements for automatic adaptation have been noted in intelligent tutoring systems, natural-language systems and intelligent interfaces. The paper brings together the research which has emanated from a number of backgrounds, and provides a unifying perspective on adaptive systems in general. An architecture for adaptive systems and a methodology for their development are presented. The paper also describes software support for producing adaptive systems, and offers some experimental evidence to justify both the desirability and feasibility of exploiting an adaptive system approach to human-computer interaction