| Autor: |
Cochran, Keith, Cohn, Clayton, Hastings, Peter, Tomuro, Noriko, Hughes, Simon |
| Předmět: |
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| Zdroj: |
International Journal of Artificial Intelligence in Education (Springer Science & Business Media B.V.); Sep2024, Vol. 34 Issue 3, p1248-1286, 39p |
| Abstrakt: |
To succeed in the information age, students need to learn to communicate their understanding of complex topics effectively. This is reflected in both educational standards and standardized tests. To improve their writing ability for highly structured domains like scientific explanations, students need feedback that accurately reflects the structure of their explanations — how they express causal relationships between concepts, and link them together. Unfortunately, providing that type of feedback is difficult and time-consuming for teachers. And Automated Essay Scoring (AES) systems generally aggregate the occurrence of features to provide holistic scores for essays, but give no information about the causal relationships identified by the students. In this paper, we evaluate a method for identifying causal relations in student scientific explanations based on recent advances in Natural Language Processing (NLP). Identification of the causal structure could be used to provide formative feedback to writers and their teachers about how the writing and reasoning could be improved. The identification method we evaluate here is based on Bidirectional Encoder Representations from Transformers (BERT, Devlin et al. , 2019), which uses unsupervised learning to create a general language model and then supervised learning to apply that model to a particular task. We compare a base BERT model to several of its special-purpose variants, and include two baseline approaches. The contributions of this study were that we determine which variants worked best along with their strengths and limitations for identifying causal structure in explanations. We found that the models detected the existence of a causal relationship with good performance, but identifying the particular causal relationship will require further research, largely due to the common challenge of imbalanced data classes. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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