Building a Corpus of Task-Based Grading and Feedback Systems for Learning and Teaching Programming

Abstract

Using grading and feedback systems in the context of learning and teaching programming is quite common. During the last 20 to 40 years research results on several hundred systems and approaches have been published. Existing papers may tell researchers what works well in terms of educational support and how to make a grading and feedback system stable, extensible, secure, or sustainable. However, finding a solid basis for such kind of research is hard due to the vast amount of publications from a very diverse community. Hardly any recent systematic review includes data from more than 100 systems (most include less than 30). Hence, the authors started an endeavor to build a corpus of all task-based grading and feedback systems for learning and teaching programming that deal with source code and have been published in recent years. The intention is to provide the community with a solid basis for their research. The corpus is also designed to be updated and extended by the community with future systems. This paper describes the process of building the corpus and presents some meta-analysis that shed light on the involved research communities.