Automatic Construction of a Bug Library for Object-Oriented Novice Java Programmer Errors

Suarez, Merlin Teodosia; Sison, Raymund

doi:10.1007/978-3-540-69132-7_23

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…learning, a specific type of AI, is another possibility for generating feedback. JavaBugs sought to identify a student's intention by using a machine learning approach to generate a library of common bugs for beginning Java students (Suarez and Sison, 2008). This ITS utilized a set of sample solutions to the problems and compared the student's solution to these samples, using it to determine how the student intends to solve the problem.…”

Section: Generating Feedback Through Machine Learning Techniques Macmentioning

confidence: 99%

A Neural Network Model for a Tutoring Companion Supporting Students in a Programming with Java Course

Day

Gonzalez-Sanchez

2019

Proceedings of the 50th ACM Technical Symposium on Computer Science Education

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Feedback represents a vital component of the learning process and is especially important for Computer Science students. With class sizes that are often large, it can be challenging to provide individualized feedback to students. Consistent, constructive, supportive feedback through a tutoring companion can scaffold the learning process for students.This work contributes to the construction of a tutoring companion designed to provide this feedback to students. It aims to bridge the gap between the messages the compiler delivers, and the support required for a novice student to understand the problem and fix their code. Particularly, it provides support for students learning about recursion in a beginning university Java programming course. Besides also providing affective support, a tutoring companion could be more effective when it is embedded into the environment that the student is already using, instead of an additional tool for the student to learn. The proposed Tutoring Companion is embedded into the Eclipse Integrated Development Environment (IDE).This thesis focuses on the reasoning model for the Tutoring Companion and is developed using the techniques of a neural network. While a student uses the IDE, the Tutoring Companion collects 16 data points, including the presence of certain key words, cyclomatic complexity, and error messages from the compiler, every time it detects an event, such as a run attempt, debug attempt, or a request for help, in the IDE. This data is used as inputs to the neural network. The neural network produces a correlating single output code for the feedback to be provided to the student, which is displayed in the IDE.

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Section: Generating Feedback Through Machine Learning Techniques Macmentioning

confidence: 99%

A Neural Network Model for a Tutoring Companion Supporting Students in a Programming with Java Course

Day

Gonzalez-Sanchez

2019

Proceedings of the 50th ACM Technical Symposium on Computer Science Education

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“…For JavaBugs [29], unfortunately, sample feedback messages were not available in literature. However, feedback may be reasoned from the following excerpt: "The task of automatic bug library construction entails detecting the most similar correct program (intention expressed as reference programs), extracting the superficial differences (discrepancies) between the student's and the correct program and forming misconception definitions (error hierarchies) described by discrepancies based on similarity and causality heuristics."…”

Section: Semantic Feedbackmentioning

confidence: 99%

“…However, feedback may be reasoned from the following excerpt: "The task of automatic bug library construction entails detecting the most similar correct program (intention expressed as reference programs), extracting the superficial differences (discrepancies) between the student's and the correct program and forming misconception definitions (error hierarchies) described by discrepancies based on similarity and causality heuristics." ( [29], p. 185). The authors applied a multi-strategy machine learning approach to automatically construct a library of Java errors, which novice programmers often make.…”

Section: Semantic Feedbackmentioning

confidence: 99%

A Classification of Adaptive Feedback in Educational Systems for Programming

2016

Systems

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Over the last three decades, many educational systems for programming have been developed to support learning/teaching programming. In this paper, feedback types that are supported by existing educational systems for programming are classified. In order to be able to provide feedback, educational systems for programming deployed various approaches to analyzing students' programs. This paper identifies analysis approaches for programs and introduces a classification for adaptive feedback supported by educational systems for programming. The classification of feedback is the contribution of this paper.

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“…The functions that such systems can perform vary. Some of them are used for learner assessment like JavaBugs [22] and JITS [24], [23], or basic tutoring like Jeliot 3 and Logic-ITA [1], while some of them are adaptive web-based tutorials [16], [21]. One step further in implementation of adaptation was made by systems like JOSH-online [2], iWeaver [28] and CIMEL ITS [27], [8].…”

Section: Related Workmentioning

confidence: 99%

“…JavaBugs examines a complete Java program and identifies the most similar correct program to the learner's solution among a collection of correct solutions. After that, it builds trees of misconceptions using similarity measures and background knowledge [22]. They focused on the construction of a bug library for novice Java programmer errors, which is a collection of commonly occurring errors and misconceptions.…”

Section: Related Workmentioning

confidence: 99%

Integration of recommendations and adaptive hypermedia into java tutoring system

Klašnja-Milićević¹,

Vesin²,

Ivanović³

et al. 2011

COMSIS J

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A way to improve the effectiveness in e-learning is to offer the personalized approach to the learner. Adaptive e-learning system needs to use different strategies and technologies to predict and recommend the most likely preferred options for further learning material. This can be achieved by recommending and adapting the appearance of hyperlinks or simply by recommending actions and recourses. This paper presents an idea for integration of such recommender system into existing web-based Java tutoring system in order to provide various adaptive programming courses.

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Automatic Construction of a Bug Library for Object-Oriented Novice Java Programmer Errors

Cited by 10 publications

References 7 publications

A Neural Network Model for a Tutoring Companion Supporting Students in a Programming with Java Course

A Neural Network Model for a Tutoring Companion Supporting Students in a Programming with Java Course

A Classification of Adaptive Feedback in Educational Systems for Programming

Integration of recommendations and adaptive hypermedia into java tutoring system

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