Works (6)
Updated: July 5th, 2023 15:36
2019 conference paper
Exploring the Impact of Worked Examples in a Novice Programming Environment
Proceedings of the 50th ACM Technical Symposium on Computer Science Education - SIGCSE '19, 98–104.
Event: the 50th ACM Technical Symposium at New York, NY, USA
TL;DR:
A Peer Code Helper system to display WEs, along with scaffolded self-explanation prompts, in a block-based, novice programming environment called \snap found that WEs did not significantly impact students' learning, but may have impacted students' intrinsic cognitive load.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Sources: ORCID, Crossref
Added: May 19, 2019
2018 conference paper
Exploring Data-driven Worked Examples for Block-based Programming
(2018, August 13).
author keywords: block-based programming environment; data-driven; worked examples; self-explanation
TL;DR:
This paper aims to create and evaluate data-driven intelligent WEs for novices using the Snap! block-based programming environment and develop a data- driven method to generate WEs based on student solutions and compare it with manually-curated WEs.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: ORCID
Added: May 19, 2019
2018 conference paper
Exploring Instructional Support Design in an Educational Game for K-12 Computing Education
Proceedings of the 49th ACM Technical Symposium on Computer Science Education, 747–752.
Event: at New York, NY, USA
TL;DR:
The motivations, design process, and study's results provide insight into the design of Supports for programming games, suggesting Bugs may be a promising strategy, as demonstrated by the lower completion time and solution code length in assessment puzzles.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: ORCID
Added: May 19, 2019
2018 chapter
The Impact of Data Quantity and Source on the Quality of Data-Driven Hints for Programming
In Lecture Notes in Computer Science (pp. 476–490).
author keywords: Data-driven hints; Programming; Hint quality; Cold start
TL;DR:
It is found that with student training data, hint quality stops improving after 15–20 training solutions and can decrease with additional data, and that student data outperforms a single expert solution but that a comprehensive set of expert solutions generally performs best.
(via Semantic Scholar)
Sources: ORCID, Crossref
Added: March 25, 2019
2017 article
Hint Generation Under Uncertainty: The Effect of Hint Quality on Help-Seeking Behavior
ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2017, Vol. 10331, pp. 311–322.
author keywords: Intelligent Tutoring Systems; Hints; Help-seeking; Programming
TL;DR:
It is argued that hint quality, especially when using data-driven hint generation techniques, is inherently uncertain, and the quality of the first few hints on an assignment is positively associated with future hint use on the same assignment.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science)
Sources: ORCID, Web Of Science
Added: August 6, 2018
2017 journal article
Understanding problem solving behavior of 6-8 graders in a debugging game
COMPUTER SCIENCE EDUCATION, 27(1), 1–29.
author keywords: Debugging; K-12; educational games; computational thinking
TL;DR:
It was found that in the authors' programming game, debugging required deeper understanding than writing new codes, and problem solving behaviors were significantly correlated with students’ self-explanation quality, number of code edits, and prior programming experience.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Sources: Web Of Science, ORCID
Added: August 6, 2018
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