“Automatic assessment of student answers for geometric theorem proving questions,”
Abstract
In this paper, we present a system to automatically assess multi-step answers for geometric theorem proving questions in high school Mathematics. The system is capable of allocating partial marks for steps considering a marking rubric. Moreover, the system evaluates the natural language reasoning part in each step. Currently, 30 theorems related to straight lines have been implemented as inference rules. The system has been tested with 100 student answers for two geometric theorem proving questions.
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In MLP-based assessment, grades for previously unseen student solutions are predicted or propagated from expert-provided grades, sometimes along with partial credit assignment.
“where grades for previously unseen student solutions are predicted (or propagated from expert-provided grades), sometimes along with partial credit assignment.”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Mendis et al., 2017
Mathematical language processing has been used for automatic assessment of geometric theorem proving.
“and geometric theorem proving (Mendis et al., 2017)”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Mendis et al., 2017
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References 10 · Cited by 3
- Authors
- Mendis, C., Lahiru, D., Pamudika, N., Madushanka, S., Ranathunga, S., & Dias, G.
- Affiliations
- University of Moratuwa (LK)
- Venue
- 2017 Moratuwa Engineering Research Conference (MERCon)
- Publisher
- IEEE
- Published
- 2017-05
- Pages
- 413-418
- OpenAlex
- W2734770013
- Cited by
- 3
- References
- 10
- Citations by year
- 2024: 1 · 2023: 2
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Moratuwa Engineering Research Conference (MERCon) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- automated assessment, geometric theorem proving, marking rubrics, natural language reasoning, inference rules, high school mathematics, student response evaluation
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