A Prototype for an Intelligent Tutoring System for Students Learning to Program in Java™

Sykes, Edward R.; Franěk, František

doi:10.2316/journal.208.2004.1.202-1454

Cited by 42 publications

(30 citation statements)

References 3 publications

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“…This is a rather different approach since the aim in this case is to provide an intelligent courseware delivery service. Other more recent attempts are presented in [28,29,30,31]. The SQL tutor [28] is an intelligent but not adaptive webbased tutoring system that teaches SQL.…”

Section: Related Workmentioning

confidence: 99%

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Enhanced JavaScript Learning Using Code Quality Tools and a Rule-Based System in the FLIP Exploratory Learning Environment

Karkalas

Gutierrez-Santos

2014

2014 IEEE 14th International Conference on Advanced Learning Technologies

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Abstract-The 'FLIP Learning' (Flexible, Intelligent and Personalised Learning) is an Exploratory Learning Environment (ELE) for teaching elementary programming to beginners using JavaScript. This paper presents the subsystem that is used to generate individualised real-time support to students depending on their initial misconceptions. The subsystem is intended to be used primarily in the early stages of student engagement in order to help them overcome the constraints of their Zone of Proximal Development (ZPD) with minimal assistance from teachers.

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Section: Related Workmentioning

confidence: 99%

“…This system teaches Java and provides multi-level support that includes both design and implementation. An expert system supported by decision trees that also teaches Java is [29]. A different approach in terms of knowledge representation is used by [31].…”

Section: Related Workmentioning

confidence: 99%

Enhanced JavaScript Learning Using Code Quality Tools and a Rule-Based System in the FLIP Exploratory Learning Environment

Karkalas

Gutierrez-Santos

2014

2014 IEEE 14th International Conference on Advanced Learning Technologies

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“…There exist intelligent tutors for Lisp , Prolog (Hong, 2004), Pascal (Johnson and Soloway, 1985), Java (Sykes and Franek, 2004;Kölling et al, 2003), Haskell (López et al, 2002), and many more programming languages. Some of these tutors are well-developed tutors extensively tested in classrooms, others have not outgrown the research prototype phase yet.…”

Section: Intelligent Tutoring Systems For Programmingmentioning

confidence: 99%

Constructing Strategies for Programming

Gerdes

Heeren

Jeuring

2009

Proceedings of the First International Conference on Computer Supported Education

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Abstract:Learning to program is difficult. To support learning programming, many intelligent tutoring systems for learning programming have been developed. Research has shown that such tutors have positive effects on learning. However, intelligent tutors for learning programming are not widely used. Building an intelligent tutor for a programming language is a substantial amount of work, and utilising it in a course is often hard for a teacher. In this paper we illustrate how to construct strategies for solving programming exercises and how these strategies can be used to automatically support students using an intelligent programming tutor to incrementally develop a program. Using strategies for programming, specifying an exercise becomes relatively easy, and more flexible.

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“…[14] for use in learning processes) and Flow Charts (see e.g. [16] for use in learning processes). Items that make this concept more expressive for didactic knowledge than representations as mentioned above are…”

Section: The Storyboarding Conceptmentioning

confidence: 99%

Knowledge mining for supporting learning processes

Knauf

Böck

Sakurai

et al. 2008

2008 IEEE International Conference on Systems, Man and Cybernetics

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Abstract-AI technologies for knowledge mining are commonly used in technical environments. Their application for social processes like learning processes, for example, is a quite a new challenge, which is characterized by having "humans in the loop". Humans' desires, preferences and decisions may be unpredictable and thus, not appropriate for modeling -at a first glance. However, in learning processes didactic variants can be anticipated and can become a subject of AI technologies. A semiformal modeling approach called storyboarding, is outlined here. A storyboard represents various opportunities for composing a learning process according to individual circumstances, such as topical prerequisites (educational history), mental prerequisites (preferred learning styles, etc.), performance prerequisites (a requested success level in former learning activities, etc.), and personal aspects (needs, wishes, talents, aims). By storyboarding, various didactic variants can be validated by considering the average learning success associated with the different paths through a storyboard in a case study. Based on validation results, success chances can be derived for the different paths. Here, a concept and an implementation to pre-estimate success chances of intended (future) learning paths through a storyboard are introduced. They are based on a Data Mining technology, and construct a decision tree by analyzing former learners' paths and their degrees of success. Furthermore, this technology generates a supplement to a submitted path, which is optimal according to the success chances. This technology has been tested at a Japanese university, in which students had to compose their individual plan (subject sequences) in advance, and the technology helped them by predicting success chances and suggesting alternatives.

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A Prototype for an Intelligent Tutoring System for Students Learning to Program in Java™

Cited by 42 publications

References 3 publications

Enhanced JavaScript Learning Using Code Quality Tools and a Rule-Based System in the FLIP Exploratory Learning Environment

Enhanced JavaScript Learning Using Code Quality Tools and a Rule-Based System in the FLIP Exploratory Learning Environment

Constructing Strategies for Programming

Knowledge mining for supporting learning processes

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