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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
PubMed
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.

Keywords:
Automated code assessmentCoding practice platformsComputer science educationEducational Data MiningSubmission behavior analysis

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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.