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SPLICE Working Group:
Reusable Code Examples
Peter Brusilovsky, Vasile Rus
https://cssplice.github.io/codex/index.html
“Smart Content” in CS Education
• Many domains use of “static” content (text, images, videos) and
tested with simple MCQ
• CS Educators developed a variety of different of “smart content”
– interactive, dynamic, provides feedback
• ITiCSE 2014 Working Group revied SLC:
– Program visualization
– Coding problems with automatic assessment
– Problem-solving support with “tutors”
Brusilovsky, P., Edwards, S., Kumar, A., Malmi, L., et al. (2014) Increasing Adoption of Smart Learning Content for
Computer Science Education. In: Working Group Reports of the 2014 on Innovation & Technology in Computer
Science Education Conference, Uppsala, Sweden, ACM, pp. 31-57.
Smart Content: Forms
• Problems
– Learning by doing
– Mastering domain knowledge
• Worked-out examples
– Demonstrating how to solve problems in a domain
– Step-by-step, with explanations
– Acquiring knowledge
• Expertise reversal
– Reversal in the relative effectiveness of instructional
methods as levels of learner knowledge in a domain change
– Worked examples more efficient on early stages, problems
should be preferred in later stages
A Simple Guide to SLC
Worked examples Problems
Comprehension
(behavior, tracing)
Program animation
Program Tracing Demo
Code tracing problems
Tracing ITS
Construction Annotated code
Codecasts
Parson’s problems
Coding problems
• Demo: PAWS Lab Sandbox
• http://adapt2.sis.pitt.edu/kt
• Log in: adl02 (adl03, adl04, adl05…)
• Password – same as log in
WebEx – Annotated Code Examples
Brusilovsky,
P.
and
Yudelson,
M.
(2008)
From
WebEx
to
NavEx:
Interactive
Access
to
Annotated
Program
Examples.
Proceedings
of
the
IEEE
96
(6),
990-999.
Explorable Code Examples: PCX
Hosseini, R., Akhuseyinoglu, K., Brusilovsky, P., Malmi, L., Pollari-Malmi, K., Schunn, C., and Sirkiä, T. (2020) Improving Engagement in
Program Construction Examples for Learning Python Programming. International Journal of Artificial Intelligence in Education 30 (2), 299-336.
Example-Based Challenges in PCX
Hosseini, R., Akhuseyinoglu, K., Brusilovsky, P., Malmi, L., Pollari-Malmi, K., Schunn, C., and Sirkiä, T. (2020) Improving Engagement in Program
Construction Examples for Learning Python Programming. International Journal of Artificial Intelligence in Education 30 (2), 299-336.
ACOS Server Annotated Examples
Deep Tutor: Problems from Examples
Oli, P., Banjade, R., Lekshmi-Narayanan, A.-B., Brusilovsky, P., and Rus, V. (2024) Exploring The Effectiveness of
Reading vs. Tutoring For Enhancing Code Comprehension For Novices. In: Proceedings of ACM Symposium on Applied
Computing, SAC 2024, Avila, Spain, April 8–12, 2024, ACM, pp. 38-47.
WorkedGen
Jury, B., Lorusso, A., Leinonen, J., Denny, P., and Luxton-Reilly, A. (2024) Evaluating LLM-generated Worked Examples
in an Introductory Programming Course. In: Proceedings of Proceedings of the 26th Australasian Computing Education
Conference, New York, NY, USA, Association for Computing Machinery, pp. 77–86.
Self-Explaining Examples

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SPLICE Working Group: Reusable Code Examples

  • 1. SPLICE Working Group: Reusable Code Examples Peter Brusilovsky, Vasile Rus https://cssplice.github.io/codex/index.html
  • 2. “Smart Content” in CS Education • Many domains use of “static” content (text, images, videos) and tested with simple MCQ • CS Educators developed a variety of different of “smart content” – interactive, dynamic, provides feedback • ITiCSE 2014 Working Group revied SLC: – Program visualization – Coding problems with automatic assessment – Problem-solving support with “tutors” Brusilovsky, P., Edwards, S., Kumar, A., Malmi, L., et al. (2014) Increasing Adoption of Smart Learning Content for Computer Science Education. In: Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference, Uppsala, Sweden, ACM, pp. 31-57.
  • 3. Smart Content: Forms • Problems – Learning by doing – Mastering domain knowledge • Worked-out examples – Demonstrating how to solve problems in a domain – Step-by-step, with explanations – Acquiring knowledge • Expertise reversal – Reversal in the relative effectiveness of instructional methods as levels of learner knowledge in a domain change – Worked examples more efficient on early stages, problems should be preferred in later stages
  • 4. A Simple Guide to SLC Worked examples Problems Comprehension (behavior, tracing) Program animation Program Tracing Demo Code tracing problems Tracing ITS Construction Annotated code Codecasts Parson’s problems Coding problems • Demo: PAWS Lab Sandbox • http://adapt2.sis.pitt.edu/kt • Log in: adl02 (adl03, adl04, adl05…) • Password – same as log in
  • 5. WebEx – Annotated Code Examples Brusilovsky, P. and Yudelson, M. (2008) From WebEx to NavEx: Interactive Access to Annotated Program Examples. Proceedings of the IEEE 96 (6), 990-999.
  • 6. Explorable Code Examples: PCX Hosseini, R., Akhuseyinoglu, K., Brusilovsky, P., Malmi, L., Pollari-Malmi, K., Schunn, C., and Sirkiä, T. (2020) Improving Engagement in Program Construction Examples for Learning Python Programming. International Journal of Artificial Intelligence in Education 30 (2), 299-336.
  • 7. Example-Based Challenges in PCX Hosseini, R., Akhuseyinoglu, K., Brusilovsky, P., Malmi, L., Pollari-Malmi, K., Schunn, C., and Sirkiä, T. (2020) Improving Engagement in Program Construction Examples for Learning Python Programming. International Journal of Artificial Intelligence in Education 30 (2), 299-336.
  • 9. Deep Tutor: Problems from Examples Oli, P., Banjade, R., Lekshmi-Narayanan, A.-B., Brusilovsky, P., and Rus, V. (2024) Exploring The Effectiveness of Reading vs. Tutoring For Enhancing Code Comprehension For Novices. In: Proceedings of ACM Symposium on Applied Computing, SAC 2024, Avila, Spain, April 8–12, 2024, ACM, pp. 38-47.
  • 10. WorkedGen Jury, B., Lorusso, A., Leinonen, J., Denny, P., and Luxton-Reilly, A. (2024) Evaluating LLM-generated Worked Examples in an Introductory Programming Course. In: Proceedings of Proceedings of the 26th Australasian Computing Education Conference, New York, NY, USA, Association for Computing Machinery, pp. 77–86.