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ACcoding: A graph-based dataset for online judge programming.

Kairui Chen1, Fuqun Huang2, Zejing Liu1

  • 1Beihang University, School of Software, Beijing, 100191, China.

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Summary
This summary is machine-generated.

We introduce ACcoding, a large-scale graph-based dataset for programming education, featuring millions of records from thousands of students over six years. This dataset aids in understanding student learning behaviors and improving programming education quality.

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Area of Science:

  • Computer Science Education
  • Educational Data Mining

Background:

  • Existing programming education datasets often lack sufficient scale and longitudinal data, hindering in-depth analysis of student learning patterns.
  • Effective programming education requires comprehensive datasets for performance tracking and pedagogical enhancement.

Purpose of the Study:

  • To introduce ACcoding, a novel, large-scale, graph-based dataset for programming education.
  • To provide a rich data source for analyzing student learning behaviors on Online Judge platforms.
  • To support educators in tracing student progress and selecting targeted programming tasks.

Main Methods:

  • Construction of a graph-based dataset from student submissions on an Online Judge platform.
  • Collection of over 4 million task-solving records from nearly 28,000 students.
  • Data spans 6 years and includes 4,559 programming tasks.

Main Results:

  • The ACcoding dataset contains 4,046,652 task-solving records as of May 6, 2022.
  • The dataset encompasses 27,444 students and 4,559 programming tasks over a 6-year period.
  • Demonstration of dataset applications for tracing student progress and task selection.

Conclusions:

  • ACcoding offers a valuable, large-scale resource for advancing research in programming education.
  • The dataset's size and features enable detailed investigation of student learning dynamics.
  • ACcoding empowers educators with tools for personalized learning and curriculum development.