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Cited by 2Teaching and Learning ProgrammingIntelligent Tutoring Systems and Adaptive LearningVisual and Cognitive Learning Processes

Exploring Data-driven Worked Examples for Block-based Programming

Zhi, Rui·2018·Proceedings of the 2018 ACM Conference on International Computing Education Research
DOI 10.1145/3230977.3231018
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Abstract

Empirical studies show that worked examples (WEs) are effective in improving students' learning efficiency in a variety of domains. I aim to create and evaluate data-driven intelligent WEs for novices using the Snap! block-based programming environment. First, I will design and evaluate WEs with self-explanation prompts in Snap!. Then I will develop a data-driven method to generate WEs based on student solutions and compare it with manually-curated WEs.

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Crossref · 10.1145/3230977.3231018

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4 of 4 references verified0 citation errors · 0 not checkable

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  • Our prior work with worked examples in a novice programming game showed that when students are given only worked examples, they may skip through them quickly without learning.

    “Our prior work with WEs in a novice programming game showed that when students are given only WEs, they may skip through them quickly without learning [26].”

    From Exploring the Impact of Worked Examples in a Novice Programming Environment (2019) · cites this paper as [26]

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Authors
Zhi, R.
Affiliations
North Carolina State University (US)
Corresponding author
Zhi, Rui
ORCID
Zhi, Rui
Venue
Proceedings of the 2018 ACM Conference on International Computing Education Research
Publisher
ACM
Published
2018-08-08
Pages
294-295
DOI
10.1145/3230977.3231018
OpenAlex
W2887892084
Cited by
2
References
4
Citations by year
2019: 2
FWCI
0.57
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
ACM Conference on International Computing Education Research (ICER) (publishedVersion)
Funders
National Science Foundation (1623470)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
Keywords
worked examples, block-based programming, SNAP, self-explanation prompts, novice programmers, programming education, student solutions

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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