Design of a general planmatcher for diagnosing student programs

ACM SIGCSE Bulletin, 1975

A program was developed to use the PLATO IV system of the University of Illinois. to, help students solve typical programing problems. The program tries to approximate a near-ideal situation in which each student receivescorrection of_logiCal errors :and comments. on good programing practice as he goes along in a one-on-one tutorial environment. The tutor program utilizes an AND-OR graph as a representation of all reasonably correct approaches to the particular problem, as veil as many of the wrong approaches introductory students are likely to attempt. The computer-assisted instruction program gives students the personal attention they need for learning the problem solving of computer programs. (NH) aft

2005

It has been noticed that during the initial semesters of Computing Engineering that the amount of human tutors is insufficient. The students/tutors ratio is very high and there is a great difference in the acquired knowledge and backgrounds. The idea is that a system could emulate the human tutor and besides provide the student with a degree of flexibility for the selection of the most adequate tutorial type. This could be a feasible solution to the stated problem. But a tutorial system should not only emulate the human tutor but besides it should be designed from an epistemological conception of what teaching Basic Programming means specially in an Engineering course due to the profile and identity of the future engineer.

American Journal of Mathematical and Management Sciences, 1989

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2002

An educational system is a community where students and teachers are involved in a process of learning and teaching. Intelligent tutoring systems (ITS) are computer systems designed for support and improvement of learning and teaching process in freely chosen domain knowledge. Tutor-Expert System (TEx-Sys) is a hypermedial authoring shell for building intelligent tutoring systems. While testing a student, human tutor observes sequence of student actions, remembers a number and type of student mistakes and number of additional questions or other type of help used to push student forward. Human tutor makes evaluation of student knowledge that is based on gathered information. Those is why ITS that considers only final results, that is, student answer, cannot justly and completely evaluate student knowledge. For that reason we have decided to introduce a model tracing -a diagnostic technique to be used in student modelling in TEx-Sys. The TEx-Sys provides help and remediation for future work that is based on conciderationing of number of misconceptions.

2018

The Cognitive Tutor Authoring Tools (CTAT) support creation of a novel type of tutors called example-tracing tutors. Unlike other types of ITSs (e.g., model-tracing tutors, constraint-based tutors), example-tracing tutors evaluate student behavior by flexibly comparing it against generalized examples of problem-solving behavior. Example-tracing tutors are capable of sophisticated tutoring behaviors; they provide step-by-step guidance on complex problems while recognizing multiple student strategies and (where needed) maintaining multiple interpretations of student behavior. They therefore go well beyond VanLehn's (2006) minimum criterion for ITS status, namely, that the system has an inner loop (i.e., provides within-problem guidance, not just end-of-problem feedback). Using CTAT, example-tracing tutors can be created without programming. An author creates a tutor interface through drag-and-drop techniques, and then demonstrates the problem-solving behaviors to be tutored. These...

In this paper an overview of using of EduSof system (shell framework for realization of intelligent tutoring system) in designing of intelligent tutor for learning of basic programming is given. The aim in realization of LesPas system, intelligent tutor realized by using EduSof shell, is to help beginners in programming in solution defining, learning language construction while using adequate examples, as well as in implementation and testing of his own programs. Intelligent tutoring system LesPas does not only give reports on syntax, semantic and conceptual user errors. It also tries to understand users point of view while designing solution of given problem, to help, to give advice and accept original ideas, i.e. self learning is possible. Intercode representation of knowledge of basic programming enables pupil to direst creation of his solution. One mental model is given, the way of thinking, for better problem solving in basic programming. Sadr`aj-O ovom radu je opisno koriš}enje ljuske za realizaciju inteligentnih tutorskih sistema EduSof, za dizarniranje inteligentnog tutora za u~enje osnova programiranja. Cilj ovokvog pristupa realizacije sistema LesPas je da u~enicima, po~etnicima u programiranju, pomogne pri dizajniranju, implementaciji i testiranju svojih prvih programa, kao i samih jezi~kih konstrukcija Pascal programskog jezika. Sistem LesPas ne samo da pronalazi i daje izveštaj o sintaksnim, semant~ kim i konceptualnim greškama korisnika, ve~ je dizajniran i da razume studentov pristup realizaciji rešenja problema, kao i makar parcijalno, kompletan program, tj. rešenje. Tako|e je ovakvim pristupom, dat i specifi~ni opis jednog mentalnog modela, n~ ina razmišljanja, za podr`avanje procesa rešavanja problema u osnovama programiranja nastalog dugogodi{njim iskustvom autora u podu~avanju studenata programiranju.

Lecture Notes in Computer Science, 2011

The constraint-based modeling (CBM) approach for developing intelligent tutoring systems has shown useful in several domains. However, when applying this approach to an exploratory environment where students are allowed to explore a large solution space for problems to be solved, this approach encounters its limitation: It is not well suited to determine the solution variant the student intended. As a consequence, system's corrective feedback might be not in accordance with the student's intention. To address this problem, this paper proposes to adopt a soft computing approach for solving constraint satisfaction problems. The goal of this paper is two-fold. First, we will show that classical CBM is not well-suited for building a tutoring system for tasks which have a large solution space. Second, we introduce a weighted constraintbased model for intelligent tutoring systems. An evaluation study shows that a coaching system for logic programming based on the weighted constraint-based model is able to determine the student's intention correctly in 90.3% of 221 student solutions, while a corresponding tutoring system using classical CBM can only hypothesize the student's intention correctly in 35.5% of the same corpus.

2020 IEEE Frontiers in Education Conference (FIE)

The core of this work-in-progress is that the best way to learn how to code is to practice by solving problems. However, if students have trouble with this, they can get frustrated and give up. Automated Tutoring Systems (ATS) aim to provide hints to help them solve the problems they encounter. Many of the existing systems offer general hints, e.g., "check the conditional statement" or help the student interpret the compiler or test-case errors. While this can be useful, we think that an ATS should provide interactive and specialized feedback for each program. We snowballed through publications on promising ATS and found that there are several such systems (in 27 publications), but we could also identify many challenges and that our requirements were not met by any existing system. For example, few of them work on general-purpose programming languages, e.g., Java, or scale to realistic problems consisting of multiple methods and classes. From the search, we find ATS based on Automated Program Repair (APR) shows the most promise. However, while program repair has the potential to generate specialized hints to help guide the student to a working state, studies that looked into these have identified further challenges. For example, many APR ATS tools only show the repaired program to the students, who then have to compare and modify their program accordingly. Another issue is that APR generally only modifies a few lines, so if the student solution is far from correct, the repair might fail. This can be solved by partial repair, i.e., the program is repaired so at least one additional test-case passes. While this increases the repair rate, it might make hints more difficult or point the students in a non-obvious or even "wrong" direction. The APR can take several minutes, which also makes it unsuitable for interactive ATS. We take a design science approach to define an ATS based on APR that attempts to address the identified challenges. We give a review of the state-of-the-art for the required components, e.g., APR, how to generate hints from differences between two programs. From this, we suggest a threestep roadmap; 1. identify suitable APR-tools, 2. construct an oversized test-suite, and 3. adopt APR to the tutoring context.

Proceedings of the …, 2007

In this paper the framework for intelligent tutor programs creation is described. It separates the logical part of tutor program and technical parts like user interface, database connectivity, etc. The intelligent program is built in specially developed graphical tool by means of performing primitive actions to create task model as a graph with states and conditional transitions. So it may be composed by a teacher with base programming skills.

2015

Abstract. In this paper, we will examine how a model-based tutor can automatically generate demand feedback. We will propose a two-stage feedback generation algorithm that maintains the principle of modu-larity characteristic of model-based representation, while automatically generating detailed demand feedback. We will evaluate model-based pro-gramming tutors to demonstrate that the feedback generated using this algorithm is coherent and effective at improving learning. 1