Paper Info
Title
Automatic assessment of OpenGL computer graphics assignments.
Abstract
Teaching and learning computer graphics is often considered challenging due to it requiring a diverse range of skills such as mathematics, programming, problem solving, and art and design. Assignments are a popular tool to support learning and to assess students' understanding. The value of such assignments depends on the ability to give fast (and ideally formative) feedback, and enabling students to interactively explore the solution space. This is often a problem, in particular for large classes, where assignment marking can take many days or even weeks. By the time feedback is received students often don't remember details, and there is usually no opportunity to resubmit and hence little motivation to reflect on and correct mistakes. Previous work on assessing Computer Graphics assignments is rare and restricted to evaluating the quality of 3D models produced by students - usually using some form of image or mesh comparison, which only considers the final result, but not how it was obtained. In this paper we describe how to adapt CodeRunner, a free open-source question-type plug-in for Moodle, to OpenGL assignments, and our experience of using it with a class of about 300 students. Results were overwhelmingly positive and students perceived the tool as having significantly improved their learning.
Year
DOI
Venue
2018
10.1145/3197091.3197112
ITiCSE
Keywords
Field
DocType
automarking, automatic assessment, OpenGL, teaching, learning
Computer science,Computer graphics,OpenGL,Multimedia,Formative assessment
Conference
ISBN
Citations 
PageRank 
978-1-4503-5707-4
1
0.35
References 
Authors
14
9
Name
Order
Citations
PageRank
Burkhard C. Wünsche110522.75
Chen, Z.2227.11
Lindsay Alexander Shaw331.73
Thomas Suselo411.37
Kai-Cheung Leung510.69
Davis Dimalen610.69
Wannes Van Der Mark7381.93
Andrew Luxton-Reilly839851.48
Richard Lobb9253.43