Automatic short answer grading is an important research direction
in the exploration of how to use artificial intelligence
(AI)-based tools to improve education. Current state-of-theart
approaches use neural language models to create vectorized
representations of students responses, followed by classifiers
to predict the score. However, these approaches have
several key limitations, including i) they use pre-trained language
models that are not well-adapted to educational subject
domains and/or student-generated text and ii) they almost
always train one model per question, ignoring the linkage
across question and result in a significant model storage
problem due to the size of advanced language models. In this
paper, we study the problem of automatic short answer grading
for students’ responses to math questions and propose
a novel framework for this task. First, we use MathBERT,
a variant of the popular language model BERT adapted to
mathematical content, as our base model and fine-tune it
on the downstream task of student response grading. Second,
we use an in-context learning approach that provides
scoring examples as input to the language model to provide
additional context information and promote generalization
to previously unseen questions. We evaluate our framework
on a real-world dataset of student responses to open-ended
math questions and show that our framework (often significantly)
outperform existing approaches, especially for new
questions that are not seen during training.
more »
« less
Automatic Short Math Answer Grading via In-context Meta-learning
Automatic short answer grading is an important research direction in the exploration of how to use artificial intelligence
(AI)-based tools to improve education. Current state-of-theart approaches use neural language models to create vectorized representations of students responses, followed by classifiers to predict the score. However, these approaches have
several key limitations, including i) they use pre-trained language models that are not well-adapted to educational subject domains and/or student-generated text and ii) they almost always train one model per question, ignoring the linkage across question and result in a significant model storage
problem due to the size of advanced language models. In this
paper, we study the problem of automatic short answer grading for students’ responses to math questions and propose
a novel framework for this task. First, we use MathBERT,
a variant of the popular language model BERT adapted to
mathematical content, as our base model and fine-tune it
on the downstream task of student response grading. Second, we use an in-context learning approach that provides
scoring examples as input to the language model to provide
additional context information and promote generalization
to previously unseen questions. We evaluate our framework
on a real-world dataset of student responses to open-ended
math questions and show that our framework (often significantly) outperform existing approaches, especially for new
questions that are not seen during training.
more »
« less
- Award ID(s):
- 1822830
- NSF-PAR ID:
- 10386540
- Editor(s):
- Mitrovic, A; Bosch, N.
- Date Published:
- Journal Name:
- s, Proceedings of the 15th International Conference on Educational Data Mining
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Mitrovic, A ; Bosch, N (Ed.)Automatic short answer grading is an important research direction in the exploration of how to use artificial intelligence (AI)-based tools to improve education. Current state-of-theart approaches use neural language models to create vectorized representations of students responses, followed by classifiers to predict the score. However, these approaches have several key limitations, including i) they use pre-trained language models that are not well-adapted to educational subject domains and/or student-generated text and ii) they almost always train one model per question, ignoring the linkage across question and result in a significant model storage problem due to the size of advanced language models. In this paper, we study the problem of automatic short answer grading for students’ responses to math questions and propose a novel framework for this task. First, we use MathBERT, a variant of the popular language model BERT adapted to mathematical content, as our base model and fine-tune it on the downstream task of student response grading. Second, we use an in-context learning approach that provides scoring examples as input to the language model to provide additional context information and promote generalization to previously unseen questions. We evaluate our framework on a real-world dataset of student responses to open-ended math questions and show that our framework (often significantly) outperform existing approaches, especially for new questions that are not seen during training.more » « less
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Automatic short answer grading is an important research direction in the exploration of how to use artificial intelligence (AI)-based tools to improve education. Current state-of-theart approaches use neural language models to create vectorized representations of students responses, followed by classifiers to predict the score. However, these approaches have several key limitations, including i) they use pre-trained language models that are not well-adapted to educational subject domains and/or student-generated text and ii) they almost always train one model per question, ignoring the linkage across question and result in a significant model storage problem due to the size of advanced language models. In this paper, we study the problem of automatic short answer grading for students’ responses to math questions and propose a novel framework for this task. First, we use MathBERT, a variant of the popular language model BERT adapted to mathematical content, as our base model and fine-tune it on the downstream task of student response grading. Second, we use an in-context learning approach that provides scoring examples as input to the language model to provide additional context information and promote generalization to previously unseen questions. We evaluate our framework on a real-world dataset of student responses to open-ended math questions and show that our framework (often significantly) outperform existing approaches, especially for new questions that are not seen during training.more » « less
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Automatic short answer grading is an important research direction in the exploration of how to use artificial intelligence (AI)-based tools to improve education. Current state-of-theart approaches use neural language models to create vectorized representations of students responses, followed by classifiers to predict the score. However, these approaches have several key limitations, including i) they use pre-trained language models that are not well-adapted to educational subject domains and/or student-generated text and ii) they almost always train one model per question, ignoring the linkage across question and result in a significant model storage problem due to the size of advanced language models. In this paper, we study the problem of automatic short answer grading for students’ responses to math questions and propose a novel framework for this task. First, we use MathBERT, a variant of the popular language model BERT adapted to mathematical content, as our base model and fine-tune it on the downstream task of student response grading. Second, we use an in-context learning approach that provides scoring examples as input to the language model to provide additional context information and promote generalization to previously unseen questions. We evaluate our framework on a real-world dataset of student responses to open-ended math questions and show that our framework (often significantly) outperform existing approaches, especially for new questions that are not seen during training.more » « less
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Automatic short answer grading is an important research direction in the exploration of how to use artificial intelligence (AI)-based tools to improve education. Current state-of-theart approaches use neural language models to create vectorized representations of students responses, followed by classifiers to predict the score. However, these approaches have several key limitations, including i) they use pre-trained language models that are not well-adapted to educational subject domains and/or student-generated text and ii) they almost always train one model per question, ignoring the linkage across question and result in a significant model storage problem due to the size of advanced language models. In this paper, we study the problem of automatic short answer grading for students’ responses to math questions and propose a novel framework for this task. First, we use MathBERT, a variant of the popular language model BERT adapted to mathematical content, as our base model and fine-tune it on the downstream task of student response grading. Second, we use an in-context learning approach that provides scoring examples as input to the language model to provide additional context information and promote generalization to previously unseen questions. We evaluate our framework on a real-world dataset of student responses to open-ended math questions and show that our framework (often significantly) outperform existing approaches, especially for new questions that are not seen during training.more » « less