SPLICE: Smart Content
A team of CS Education researchers gathered together as a Working Group at ACM SIGCSE 2014 and introduced the term “Smart Learning Content” to describe a diverse set of interactive learning resources that were becoming increasingly more popular in CS Education [1]. It became one of the key goals of the SPLICE project to promote broader development and reuse of smart learning content (SLC) in CS educational research and practice. Over the years, SPLICE team has been studying best practices in SLC and collecting various types of SLC for its live catalog of SLC [2]. This work encouraged us to develop the first taxonomy of smart learning content that we are presenting here. This taxonomy attempted to classify SLC by its type (worked examples vs problems), the knowledge that it helps to develop (code tracing vs program construction), and the target domain (i.e., Java, Python, SQL, algorithms, etc). Since the majority of the SLC types covered in this taxonomy are now available in the new version of SPLICE SLC catalog, we also specify the protocols that each specific type of SLC supports and infrastructures where this content can be re-used. We hope that this taxonomy will be useful for researchers and practitioners to understand which types of SLC are available for various kind of re-use and to integrate reusable SLC in their courses.
We understand that being the first attempt to classify and catalog SLC, this taxonomy misses many specific types of SLC and probably some whole categories. Are you a user or a developer of an SLC that is not mentioned in this taxonomy? Please use this form to contribute!
Summary by Institution
- Aalto University:
- Carnegie Mellon University:
- Knox College:
- Ramapo College:
- San Jose State University:
- Stanford University:
- University of California San Diego:
- University of Canterbury:
- University of Helsinki:
- University of Michigan:
- University of Pittsburgh:
- Animated Examples for SQL
- Annotated Worked Examples - WebEx
- Parameterized SQL Problems - SQL-KnoT
- Program Construction Examples - PCEX
- Python Trace Table Tutor (T3)
- QuizJET
- QuizPET
- Table Tracer
- University of Potsdam:
- University of St. Thomas:
- University of Toronto:
- Virginia Tech:
- York College of Pennsylvania:
Summary by Domain
- C/C++:
- C#:
- Java:
- Annotated Worked Examples - WebEx
- CloudCoder
- CodeCheck
- CodeOcean
- CodeWorkout
- CodingBat
- Epplets
- jsvee Animated Examples
- Parsons Problems
- PCRS
- Program Construction Examples - PCEX
- Python Tutor
- QuizJET
- Table Tracer
- TestMyCode
- JavaScript:
- Python:
- Annotated Worked Examples - WebEx
- CloudCoder
- CodeCheck
- CodeOcean
- CodeWorkout
- CodingBat
- jsParsons
- jsvee Animated Examples
- Parsons Problems
- PCRS
- Program Construction Examples - PCEX
- Python Trace Table Tutor (T3)
- Python Tutor
- QuizPET
- Relational Algebra:
- Ruby:
- SQL:
Summary by Content Type:
- Worked Examples:
- Code tracing (semantics):
- Code animations:
- Table tracing demos:
- Program construction (pragmatics):
- Problems:
- Code tracing:
- Predicting final results:
- Step-by-step tracing:
- Program construction:
- Completion problems:
- Parson’s problems:
- Restricted coding problems:
- Free coding problems:
[1] Brusilovsky, P., Edwards, S., Kumar, A., Malmi, L., Benotti, L., Buck, D., Ihantola, P., Prince, R., Sirkiä, T., Sosnovsky, S., Urquiza, J., Vihavainen, A., and Wollowski, M. (2014). "Increasing Adoption of Smart Learning Content for Computer Science Education". In: Proceedings of Working Group Reports of the 2014 on Innovation and Technology in Computer Science Education Conference, Uppsala, Sweden, ACM, pp. 31-57.
[2] Hicks, A., Akhuseyinoglu, K., Shaffer, C., and Brusilovsky, P. (2020). Live Catalog of Smart Learning Objects for Computer Science Education. In: Proceedings of Sixth SPLICE Workshop "Building an Infrastructure for Computer Science Education Research and Practice at Scale" at ACM Learning at Scale 2020, Virtual, August 12, 2020.
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