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
This content will become publicly available on January 1, 2025
Do These Students Have Similar Strategies? Clustering Math Work in Uploaded Images on an Online Learning Platform.
This exploratory study delves into the complex challenge of analyzing and interpreting student responses to mathematical problems,
typically conveyed through image formats within online learning platforms. The main goal of this research is to identify and differentiate
various student strategies within a dataset comprising image-based mathematical work. A comprehensive approach is implemented,
including various image representation, preprocessing, and clustering techniques, each evaluated to fulfill the study’s objectives.
The exploration spans several methods for enhanced image representation, extending from conventional pixel-based approaches
to the innovative deployment of CLIP embeddings. Given the prevalent noise and variability in our dataset, an ablation study is
conducted to meticulously evaluate the impact of various preprocessing steps, assessing their potency in eradicating extraneous
backgrounds and noise to more precisely isolate relevant mathematical content. Two clustering approaches—k-means and hierarchical
clustering—are employed to categorize images based on student strategies that underlies their responses. Preliminary results underscore
the hierarchical clustering method could distinguish between student strategies effectively. Our study lays down a robust framework
for characterizing and understanding student strategies in online mathematics problem-solving, paving the way for future research
into scalable and precise analytical methodologies while introducing a novel open-source image dataset for the learning analytics
research community.
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- Award ID(s):
- 1903304
- NSF-PAR ID:
- 10470438
- Publisher / Repository:
- LAK 2024 (accepted)
- Date Published:
- 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 di- rection in the exploration of how to use artificial intelligence (AI)-based tools to improve education. Current state-of-the- art approaches use neural language models to create vector- ized representations of students responses, followed by clas- sifers to predict the score. However, these approaches have several key limitations, including i) they use pre-trained lan- guage models that are not well-adapted to educational sub- ject domains and/or student-generated text and ii) they al- most always train one model per question, ignoring the link- age 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 grad- ing 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. Sec- ond, 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 signif- icantly) outperform existing approaches, especially for new questions that are not seen during training.more » « less
