A refined model of ill-definedness in project-based learning

Arthur Rump, Vadim Zaytsev

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)
61 Downloads (Pure)

Abstract

Project-based courses are crucial to gain practically relevant knowledge in modelling and programming education. However, they fall into the "ill-defined"domain: There are many possible solutions; the quality of a deliverable is subjective and not formally assessable; reaching the goals means designing new artefacts and analysing new information; and the problem cannot always be divided into independent tasks. In this paper, we refine the existing two-dimensional (verifiability and solution space) classification of ill-defined classes of problems, contemplate methods and approaches for assessment of projects, and apply the model to analyse two study units of two different computer science programmes.

Original languageEnglish
Title of host publicationProceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022
Subtitle of host publicationCompanion Proceedings
PublisherAssociation for Computing Machinery
Pages115-122
Number of pages8
ISBN (Electronic)9781450394673
DOIs
Publication statusPublished - 23 Oct 2022
Event25th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2022 - Montreal, Canada
Duration: 23 Oct 202228 Oct 2022
Conference number: 25

Publication series

NameProceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022: Companion Proceedings

Conference

Conference25th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2022
Abbreviated titleMODELS 2022
Country/TerritoryCanada
CityMontreal
Period23/10/2228/10/22

Keywords

  • Learning objectives

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