Text mining and natural language processing in construction

Shamshiri, Alireza; Ryu, Kyeong Rok; Park, June Young

doi:10.1016/j.autcon.2023.105200

Cited by 2 publications

References 130 publications

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Using Large Language Models for Automated Grading of Student Writing about Science

Impey,

Wenger,

Garuda

et al. 2024

Preprint

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A challenge in teaching large classes for formal or informal learners is assessing writing. As a result, most large classes, especially in science, use objective assessment tools like multiple choice quizzes. The rapid maturation of AI has created the possibility of using large language models (LLMs) to assess student writing. An experiment was carried out using GPT-3.5 and GPT-4 to see if machine learning methods based on LLMs can rival peer grading for reliability and automation in evaluating short writing assignments on topics in astronomy. The audience was lifelong learners in three massive open online courses (MOOCs) offered through Coursera. However, the results should also be applicable to non-science majors in university settings. The data was answers from 120 students on 12 questions across the three courses. The LLM was fed with total grades, model answers, and rubrics from an instructor for all three questions. In addition to seeing how reliably the LLMs reproduced instructor grades, the LLMs were asked to generate their own rubrics. Overall, the LLMs were more reliable than peer grading, both in the aggregate and by individual student, and they came much closer to the instructor grades for all three of the online courses. GPT-4 generally outperformed GPT-3.5. The implication is that LLMs can be used for automated, reliable, and scalable grading of student science writing.

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Using Large Language Models for Automated Grading of Student Writing about Science

Impey,

Wenger,

Garuda

et al. 2024

Preprint

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Automatic Reconstruction of 3D Models from 2D Drawings: A State-of-the-Art Review

Feist,

Jacques de Sousa,

Sanhudo

et al. 2024

Eng

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Among the methods of 3D reconstruction, the automatic generation of 3D models from building documentation is one of the most accessible and inexpensive. For 30 years, researchers have proposed multiple methods to automatically generate 3D models from 2D drawings. This study compiles this research and discusses the different methods used to generate 3D models from 2D drawings. It offers a critical review of these methods, focusing on the coverage and completeness of the reconstruction process. This review allows us to identify the research gaps in the literature, and opportunities for improvement are identified for future research.

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Text mining and natural language processing in construction

Cited by 2 publications

References 130 publications

Using Large Language Models for Automated Grading of Student Writing about Science

Using Large Language Models for Automated Grading of Student Writing about Science

Automatic Reconstruction of 3D Models from 2D Drawings: A State-of-the-Art Review

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