Abstract
Assessing the academic capabilities of students should play a key role in both
stimulating their learning process (formative assessment) and in the accurate
evaluation of their knowledge and capabilities in relation to a topic (summative
assessment). Therefore, according to the principle of constructive alignment, any
form of assessment needs to be carefully designed to match the learning outcomes of a course and needs to be delivered in an appropriate format (paper-based vs. computer-based) and graded in a suitable manner. However, this is a challenging task, due to the substantial amount of time teachers need to spend on grading open questions. From our experience, this results in using less appropriate assessment methods (e.g.: Multiple Choice questions) or in less time spent by teachers on innovating their courses (e.g.: implementation of formative assessment). Inspired by recent developments in academia and practice, we propose to investigate the application of machine learning technology for supporting grading of open questions, with a focus on summative assessment and exploring possibilities for formative assessment. Our expected results include the design of a method for supporting grading of open questions with machine learning, an investigation into the most suitable machine learning algorithms for small samples of tests.
stimulating their learning process (formative assessment) and in the accurate
evaluation of their knowledge and capabilities in relation to a topic (summative
assessment). Therefore, according to the principle of constructive alignment, any
form of assessment needs to be carefully designed to match the learning outcomes of a course and needs to be delivered in an appropriate format (paper-based vs. computer-based) and graded in a suitable manner. However, this is a challenging task, due to the substantial amount of time teachers need to spend on grading open questions. From our experience, this results in using less appropriate assessment methods (e.g.: Multiple Choice questions) or in less time spent by teachers on innovating their courses (e.g.: implementation of formative assessment). Inspired by recent developments in academia and practice, we propose to investigate the application of machine learning technology for supporting grading of open questions, with a focus on summative assessment and exploring possibilities for formative assessment. Our expected results include the design of a method for supporting grading of open questions with machine learning, an investigation into the most suitable machine learning algorithms for small samples of tests.
| Original language | English |
|---|---|
| Title of host publication | Engaging, Engineering, Education |
| Subtitle of host publication | Book of Abstracts, SEFI 48th Annual Conference University of Twente (online), 20-24 September, 2020 |
| Editors | Jan van der Veen, Natascha van Hattum-Janssen, Hannu-Matti Järvinen, Tinne de Laet, Ineke ten Dam |
| Place of Publication | Enschede |
| Publisher | University of Twente |
| Pages | 584-593 |
| Number of pages | 10 |
| ISBN (Electronic) | 978-2-87352-020-5 |
| Publication status | Published - 2020 |
| Event | 48th SEFI Annual Conference on Engineering Education, SEFI 2020 - Online, Enschede, Netherlands Duration: 20 Sept 2020 → 24 Sept 2020 Conference number: 48 https://www.sefi2020.eu |
Conference
| Conference | 48th SEFI Annual Conference on Engineering Education, SEFI 2020 |
|---|---|
| Abbreviated title | SEFI 2020 |
| Country/Territory | Netherlands |
| City | Enschede |
| Period | 20/09/20 → 24/09/20 |
| Internet address |
Keywords
- Automated grading
- Machine learning
- Natural language processing
- Open questions