Toward more robust automatic analysis of student program outputs for assessment and learning

Chung Keung POON, Tak Lam WONG, Yuen Tak YU, Victor Chung Sing LEE, Chung Man TANG

Research output: Contribution to conferencePapers

4 Citations (Scopus)

Abstract

Automated analysis and assessment of students' programs, typically implemented in automated program assessment systems (APASs), are very helpful to both students and instructors in modern day computer programming classes. The mainstream of APASs employs a black-box testing approach which compares students' program outputs with instructor-prepared outputs. A common weakness of existing APASs is their inflexibility and limited capability to deal with admissible output variants, that is, outputs produced by acceptable correct programs that differ from the instructor's. This paper proposes a more robust framework for automatically modelling and analysing student program output variations based on a novel hierarchical program output structure called HiPOS. Our framework assesses student programs by means of a set of matching rules tagged to the HiPOS, which produces a better verdict of correctness. We also demonstrate the capability of our framework by means of a pilot case study using real student programs.
Original languageEnglish
Publication statusPublished - Jun 2016

Citation

Poon, C. K., Wong, T.-L., Yu, Y. T., Lee, V. C. S., & Tang, C. M. (2016, June). Toward more robust automatic analysis of student program outputs for assessment and learning. Paper presented at The 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC 2016): Connected World: New Challenges for Data, Systems & Applications, Sheraton Atlanta Hotel, Atlanta, Georgia.

Keywords

  • Automated assessment technology
  • Computer science education
  • Learning computer programming
  • Program output variant
  • Student program analysis

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