An Automated Learning System for Java Programming

Daly, Charles A.; Horgan, Jane M.

doi:10.1109/te.2003.816064

Cited by 34 publications

(13 citation statements)

References 3 publications

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“…RELATED SYSTEMS There are several existing tools that implement elements of the idealized environment described previously. Tools such as RoboProf [4], ASAP [5] and Coursemarker [6] can provide some feedback based on students' solutions to programming exercises. However, such tools are focused on automated assessment of code.…”

Section: Introductionmentioning

confidence: 99%

Learn Programming++: The Design, Implementation and Deployment of an Intelligent Environment for the Teaching and Learning of Computer Programming

Hunter

Livingstone

Neve

et al. 2013

2013 9th International Conference on Intelligent Environments

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We discuss the demand for intelligent systems to assist both teachers and students in the teaching and learning of computer programming, then describe the design, implementation of Nooblab, an integrated system offering instruction, a programming test bed and assessment for several programming languages, and its deployment in real university classroom situations. The system also logs every interaction which each student has with it, providing a rich set of data for analysis. Initial results from its use within real courses are presented and discussed, along with proposals for future developments involving incorporating machine learning paradigms into the system.

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Section: Introductionmentioning

confidence: 99%

Learn Programming++: The Design, Implementation and Deployment of an Intelligent Environment for the Teaching and Learning of Computer Programming

Hunter

Livingstone

Neve

et al. 2013

2013 9th International Conference on Intelligent Environments

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“…Thus far, several intelligent tutoring systems have been developed to support programming learners. These include RoboProf (Daly & Horgan, 2004), JITS (Sykes & Franek, 2003), J-LATTE (Holland et al 2003), and BITS (Butz et al 2006). Moreover, several learning support systems based on visualizing algorithms have received attention, including TRAKLA2 (Malmi et al 2004), Jeliot 3 (Moreno et al 2004;Čisar et al 2011), and ViLLE (Rajala et al 2008).…”

Section: Related Workmentioning

confidence: 99%

Code-reading support environment visualizing three fields and educational practice to understand nested loops

Yamashita

Nagao

Kogure

et al. 2016

RPTEL

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In this paper, we describe a code-reading support environment and practical classroom applications using this environment to understand nested loops. Previously, we developed a code-reading support system based on visualization of the relationships among the program code, target domain world, and operations. We implemented the proposed system in exercises with nested loops. The evaluation results suggested that students could frequently fulfill learning objectives using the proposed system. However, we also discovered that some students experienced a learning impasse in the classroom. We attempted to address these students with two supporting approaches: bridging the gap between the generalization structures in the program code and their corresponding operations and enabling learners to predict the behavior of the nested loops. In this paper, we extend our previous system with new functions based on our two supporting approaches. Further, we implement the system in another classroom for nested loops. We describe a correlation between the proposed system and an understanding of nested loops using pre-/post-test comparisons. We discuss how code reading using the proposed system allows learners to cultivate a superior understanding of the program code.

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“…In this case, the hypothesis set H is: [10 − 12]}} which means that one of these three hypotheses corresponds to the error made by the student. Notice that this hypothesis set involves lines and abstract components, e.g., AlternativeSelection [10][11][12], which means that the elementary pattern Alternative Selection from lines 10 to 12 is possibly faulty (component CA2 in Fig. 10a).…”

Section: An Example Of Debugging With Hmbsdmentioning

confidence: 99%

“…The RobotProf [11], an automated programming learning system for Java, provides assistance to a student in two ways:…”

Section: Evaluation Of Dr Java Pro With a Group Of Studentsmentioning

confidence: 99%

Learning to program using hierarchical model-based debugging

Barros

Pinheiro²,

Delgado

2015

Appl Intell

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Model-based Diagnosis is a well known AI technique that has been applied to software debugging for senior programmers, called Model-Based Software Debugging (MBSD). In this paper we describe the basis of MBSD and show how it can be used for educational purposes. By extending the classical diagnosis technique to a hierarchical approach, we built a programming learning system to allow a student to debug his program in different abstraction levels.

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An Automated Learning System for Java Programming

Cited by 34 publications

References 3 publications

Learn Programming++: The Design, Implementation and Deployment of an Intelligent Environment for the Teaching and Learning of Computer Programming

Learn Programming++: The Design, Implementation and Deployment of an Intelligent Environment for the Teaching and Learning of Computer Programming

Code-reading support environment visualizing three fields and educational practice to understand nested loops

Learning to program using hierarchical model-based debugging

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