Automatic assessment of students' software models using a simple heuristic and machine learning

Abstract

Software models are increasingly popular. To educate the next generation of software engineers, it is important that they learn how to model software systems well, so that they can design them effectively in industry. It is also important that instructors have the tools that can help them assess students' models more effectively. In this paper, we investigate how a tool that combines a simple heuristic with machine learning techniques can be used to help assess student submissions in model-driven engineering courses. We apply our proposed technique to first identify submissions of high quality and second to predict approximate letter grades. The results are comparable to human grading and a complex rule-based technique for the former and surprisingly accurate for the latter. CCS CONCEPTS • Software and its engineering → Unified Modeling Language (UML); • Applied computing → Education; • Computing methodologies → Machine learning.

Automated Assessment in Computer Science Education: A State-of-the-Art Review

“…The automated assessment of software modeling and visual programming is, thus, a research focus empowered by the existence of multiple correct solutions. On the software modeling side, the proposed solutions are mainly for Uniied Modelling Language (UML) (class diagrams [34], activity diagrams [228], use-case diagrams [239], general-purpose [57,200], and extensions to UML class diagram [38]), but there are also solutions for Entity-Relationship (ER) diagrams [57,211]. The evaluation is typically done by graph similarity between a model solution or a set of model solutions.…”