Towards an Interoperable Model-driven Automated Assessment System for Computer Science Education

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

Abstract

In recent years, the number of computer science students has steadily risen, but the time for educators to give feedback to the students remains the same. Because of this predicament, many systems for the automatic or semi-automatic assessment of student tasks were developed. A fundamental problem of this development is that most of these assessment systems deploy a different type of data representation, which leads to a lack of interoperability between the approaches. This hinders the reuse of teaching materials that need to match the targeted system, leading to situations in which instructors need to recreate their materials. In this work, we aim to close this gap by introducing a model-driven approach called Assisted Assessment. The approach uses a technology-independent assessment model to bridge instructors' and assessment systems' technical spaces, helping instructors to transform their material for various systems. We introduce Assisted Assessment by describing the scenario of an undergraduate course for software technology and how the approach can help to manage the different available assessment systems.

Details

Original languageEnglish
Title of host publicationMODELS Companion '24: Proceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems
Place of PublicationLinz, Austria
PublisherAssociation for Computing Machinery
Pages95-192
Number of pages8
ISBN (electronic)9798400706226
Publication statusPublished - 22 Sept 2024
Peer-reviewedYes

Conference

Title27th ACM / IEEE International Conference on Model Driven Engineering Languages and Systems
Abbreviated titleMODELS 2024
Conference number27
Duration22 - 27 September 2024
Website
Degree of recognitionInternational event
LocationJohannes Kepler Universität Linz
CityLinz
CountryAustria

External IDs

ORCID /0000-0003-1537-7815/work/173054517
ORCID /0000-0002-3513-6448/work/173054767

Keywords

DFG Classification of Subject Areas according to Review Boards

Subject groups, research areas, subject areas according to Destatis

Keywords

  • education, model-driven engineering, model-driven engineering, education, student assessment