Journal ArticleDOI
Example-based feedback provision using structured solution spaces
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TLDR
The quantitative evidence suggests that the proposed feedback strategies and automatic example assignment are viable in principle, further user studies in large-scale learning environments being the subject of future research.Abstract:
Intelligent tutoring systems (ITSs) typically rely on a formalised model of the underlying domain knowledge in order to provide feedback to learners adaptively to their needs. This approach implies two general drawbacks: the formalisation of a domain-specific model usually requires a huge effort, and in some domains it is not possible at all. In this paper, we propose feedback provision strategies in absence of a formalised domain model, motivated by example-based learning approaches. We demonstrate the feasibility and effectiveness of these strategies in several studies with experts and students. We discuss how, in a set of solutions, appropriate examples can be automatically identified and assigned to given student solutions via machine learning techniques in conjunction with an underlying dissimilarity metric. The plausibility of such an automatic selection is evaluated in an expert survey, while possible choices for domain-agnostic dissimilarity measures are tested in the context of real solution sets of Java programs. The quantitative evidence suggests that the proposed feedback strategies and automatic example assignment are viable in principle, further user studies in large-scale learning environments being the subject of future research.read more
Citations
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Journal ArticleDOI
Data-Driven Hint Generation in Vast Solution Spaces: a Self-Improving Python Programming Tutor
TL;DR: The results show that ITAP is capable of producing hints for almost any given state after being given only a single reference solution, and that it can improve its performance by collecting data over time.
Journal ArticleDOI
A Systematic Literature Review of Automated Feedback Generation for Programming Exercises
TL;DR: It is found that feedback mostly focuses on identifying mistakes and less on fixing problems and taking a next step, and teachers cannot easily adapt tools to their own needs.
Proceedings ArticleDOI
A feasibility study of using automated program repair for introductory programming assignments
TL;DR: This paper adopts a new repair policy akin to the hint generation policy employed in the existing ITSP, and admits partial repairs that address part of failing tests, which results in 84% improvement of repair rate.
Self-Explanations: How Students Study and Use Examples in Learning To Solve Problems. Technical Report No. 9.
TL;DR: This article analyzed the self-generated explanations that "Good" and "Poor" students produce while studying worked-out examples of mechanics problems, and their subsequent reliance on examples during problem solving.
Journal ArticleDOI
Cognition and Thought: An Information- Processing Approach
TL;DR: In this paper, the Committee on the College Student of the Group for the Advancement of Psychiatry proposed guidelines for college policy toward sexual behavior, with a focus on the development and integration of sexuality in the personality.
References
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Journal ArticleDOI
Self‐Explanations: How Students Study and Use Examples in Learning to Solve Problems
TL;DR: The present paper analyzes the self-generated explanations (from talk-aloud protocols) that “Good” and “Poor” students produce while studying worked-out examples of mechanics problems, and their subsequent reliance on examples during problem solving.
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