ONline Assessment of Computational Thinking (ONACT) is an embedded, formative, real-time graphical assessment tool that quickly gives teachers insight into student mastery of computational thinking constructs as they are creating games and simulations. ONACT provides teachers with the most useful representations of class and individual progress, allowing them to make effective instructional decisions.

ONACT works by breaking down all collectable student project information and recording it in the ONACT database. ONACT then analyzes the student project information stored in this database through Computational Thinking Pattern Analysis (CTPA) in real time. This analysis extracts semantic meaning out of the code by interpreting which Computational Thinking Patterns have been implemented by students. The analyzed data are then illustrated through different levels of visualization:

• Computational Thinking Pattern Analysis Graph

• Computational Thinking Pattern Analysis Forensics

• Assessment Dashboard

The Computational Thinking Pattern Analysis Graph provides a visual representation of how students are using Computational Thinking Patterns in their projects. The graph shows the frequency of each pattern being used.This information can be used by teachers to identify patterns that are being used frequently, as well as patterns that are not being used enough.

The Computational Thinking Pattern Analysis Forensics provides a more detailed look at how students are using each Computational Thinking Pattern over time. The forensics show the specific functions that are being called. This information can be used by teachers to provide feedback to students on how they can improve their use of Computational Thinking Patterns.

The Assessment Dashboard provides a summary of student progress in their programming tasks. The dashboard shows the programming progression for each student in the class through CTPA. Green indicates students who are completing the program correctly, orange indicates students who may need some help with their program, and red indicates students who are in significant need of scaffolding. The Dashboard clearly shows students that might be in trouble. By selecting a specific student in the Dashboard, a teacher can see in-depth representations of that student’s progression in their Computational Thinking Pattern Analysis Graph and Computational Thinking Pattern Analysis Forensics.

ONACT is a powerful tool that can help teachers assess student mastery of computational thinking constructs. The tool provides teachers with a variety of visualizations that can be used to identify students who are struggling, as well as students who are excelling. ONACT can also be used to provide feedback to students on how they can improve their use of computational thinking patterns.

Here are some of the benefits of using ONACT:

• ONACT is an embedded assessment tool, which means that it is integrated into the learning environment. This makes it easy for teachers to use ONACT to assess student progress without having to interrupt the flow of instruction.

• ONACT is a formative assessment tool, which means that it is used to provide feedback to students on their learning. This feedback can be used by students to improve their understanding of computational thinking concepts.

• ONACT is a real-time assessment tool, which means that it provides feedback to students immediately after they have completed a programming task. This feedback can help students to identify and correct errors in their code.

• ONACT is a graphical assessment tool, which means that it provides feedback to students in a visual format. This can be helpful for students who learn best visually.

Overall, ONACT is a powerful tool that can help teachers assess student mastery of computational thinking constructs. The tool provides teachers with a variety of visualizations that can be used to identify students who are struggling, as well as students who are excelling. ONACT can also be used to provide feedback to students on how they can improve their use of computational thinking patterns.

1. Koh, K. H., Basawapatna, A.,Bennett, V., Repenning, A., Towards the Automatic Recognition of Computational Thinking for Adaptive Visual Language Learning, IEEE International Symposium on Visual Languages and Human-Centric Computing 2010, Leganés-Madrid, Spain, September 21-25, 2010

2. Ioannidou, A., Repenning, A. and Webb, D., AgentCubes: Incremental 3D end-user development, Journal of Visual Language and Computing (2009)

3. Koh, K. H., Basawapatna, A., Nickerson, H., Repenning, A., Real Time Assessment of Computational Thinking, IEEE International Symposium on Visual Languages and Human-Centric Computing, Melbourne, Australia, July 28-Aug 1, 2014

•  Learning Transfer 

• Creativity Assessment

• Skill Trajectory