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CodeStream: A dataset of iterative programming submissions with sequential verdict traces and attempt histories
Nazira Jesmin Lina1, Syed Mumtahin Mahmud1, Mahmudul Hasan1
1Department of Computer Science and Engineering, University of Dhaka, Dhaka 1000, Bangladesh.
Data in Brief
|May 28, 2026
Summary
CodeStream is a new dataset of student programming submissions. It enables research into how students learn to code through iterative problem-solving and error correction.
Area of Science:
- Computer Science Education
- Educational Data Mining
- Learning Analytics
Background:
- Programming learning environments generate valuable interaction data from student code submissions and automated evaluations.
- Analyzing this data is crucial for understanding student learning processes and improving educational tools.
Purpose of the Study:
- To introduce CodeStream, a comprehensive dataset of programming submissions from undergraduate computer science students.
- To provide a resource for research in educational data mining, learning analytics, and programming behavior modeling.
Main Methods:
- Collected 5482 programming submissions from 202 students across 46 problems in C, C++, and Java.
- Dataset includes source code, language, verdicts, attempt order, and sequential evaluation traces.
- Linked problem descriptions and test cases are provided, preserving temporal relationships.
Main Results:
- The CodeStream dataset captures detailed iterative problem-solving behavior.
- It allows for the reconstruction of submission histories and analysis of learning progression.
- The data structure supports studying error correction and sequential decision-making.
Conclusions:
- CodeStream is a valuable resource for researchers in computer science education and related fields.
- The dataset facilitates in-depth analysis of novice programming contexts and automated feedback systems.
- It supports the development of more effective programming learning environments.
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