Syntactic and semantic analysis for extended feedback on computer graphics assignments

©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Modern computer graphics courses require students to complete assignments involving computer programming. The evaluation of student programs, either by the student (self-assessment) or by the instructors (grading) can take a considerable amount of time and does not scale well with large groups. Interactive judges giving a pass/fail verdict do constitute a scalable solution, but they only provide feedback on output correctness. In this article, we present a tool to provide extensive feedback on student submissions. The feedback is based both on checking the output against test sets, as well as on syntactic and semantic analysis of the code. These analyses are performed through a set of code features and instructor-defined rubrics. The tool is built with Python and supports shader programs written in GLSL. Our experiments demonstrate that the tool provides extensive feedback that can be useful to support self-assessment, facilitate grading, and identify frequent programming mistakes.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness and FEDER Grant TIN2017-88515-C2-1-R.Peer ReviewedPostprint (author's final draft

Avaluació semi-automàtica de problemes de gràfics per computador

La nostra API en Python permet als professors compondre rúbriques de correció basades en anàlisis sintàctics i semàntics sobre els codis tramesos pels estudiants de cursos de gràfics per computador, analitzant les components més importants del Llenguatge de Shading d'OpenGL.Our high-level Python API allows instructors to compose correction rubrics based on a syntactic and semantic analysis of the source code of student submissions to computer graphics problems in computer science courses, analysing the most relevant components of the OpenGL Shading Language

Avaluació semi-automàtica de problemes de gràfics per computador

La nostra API en Python permet als professors compondre rúbriques de correció basades en anàlisis sintàctics i semàntics sobre els codis tramesos pels estudiants de cursos de gràfics per computador, analitzant les components més importants del Llenguatge de Shading d'OpenGL.Our high-level Python API allows instructors to compose correction rubrics based on a syntactic and semantic analysis of the source code of student submissions to computer graphics problems in computer science courses, analysing the most relevant components of the OpenGL Shading Language

A Parser-based tool to assist instructors in grading computer graphics assignments

Although online e-learning environments are increasingly used in university courses, manual assessment still dominates the way students are graded. Interactive judges providing a pass/fail verdict based on test sets are valuable tools both for learning and assessment, but still rely on human review of the code for output-independent issues such as readability and efficiency. In this paper we present a tool to assist instructors in grading programming exercises in Computer Graphics (CG) courses. In contrast to other grading solutions, assessment is based both on checking the output against test sets, and through a set of instructor-defined rubrics based on syntax analysis of the source code. Our current prototype runs in Python and supports the assessment of shaders written in GLSL language. We tested the tool in a CG course involving more than one hundred Computer Science students per year. Our first experiments show the tool can be useful to support both self-assessment and grading, as well as detecting grading mistakes through anomaly detection techniques based on features extracted from the syntax analysis.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness and FEDER Grant TIN2017-88515-C2-1-R.Peer ReviewedPostprint (published version