Información - Machine Learning - # Explainable Automatic Short Answer Grading

Explainable Automatic Grading of Short Answers Using Neural Additive Models

Q: How could the NAM approach be extended to handle more complex student responses, such as those that express multiple ideas or use more nuanced language?

The NAM approach can be extended to handle more complex student responses by incorporating techniques that allow for the modeling of multiple ideas within a single response. One way to achieve this is by implementing a hierarchical feature engineering process. Instead of treating each feature independently, the model can be designed to capture relationships between features that represent different ideas. This can involve creating feature combinations that reflect the presence of multiple concepts or ideas in a response. By considering the interactions between features, the NAM can better capture the complexity of student responses that express multiple ideas. Additionally, the NAM model can be enhanced by incorporating contextual information. This can involve utilizing contextual embeddings or contextualized representations of student responses to capture the nuanced language used by students. By leveraging contextual information, the model can better understand the meaning and context of the language used in student responses, enabling it to handle more complex and nuanced expressions of ideas.

Q: What are the potential limitations of relying on predefined features, and how could the NAM model be adapted to better capture the nuances of student understanding?

Relying solely on predefined features can limit the flexibility and adaptability of the NAM model, as it may struggle to capture the full range of nuances present in student responses. Predefined features may not encompass all the variations in language and expression that students use, leading to potential gaps in the model's ability to accurately assess student understanding. To address this limitation, the NAM model can be adapted in the following ways: Dynamic Feature Generation: Instead of relying solely on predefined features, the model can be designed to dynamically generate features based on the content of student responses. This can involve using techniques like attention mechanisms to identify important parts of the response and generate features accordingly. Semantic Embeddings: By incorporating semantic embeddings or contextualized word representations, the NAM model can better capture the nuances of student understanding. These embeddings can provide a more comprehensive representation of the language used in student responses, allowing the model to capture subtle variations in meaning and expression. Fine-tuning with Feedback: The NAM model can be fine-tuned iteratively based on feedback from human graders. By incorporating feedback on where the model's predictions align or diverge from human assessments, the model can learn to better capture the nuances of student understanding over time.

Q: Given the potential benefits of explainable AI in education, how might NAMs or similar interpretable models be applied to other educational tasks beyond short answer grading, such as providing personalized feedback or identifying learning difficulties?

NAMs and similar interpretable models can be applied to various educational tasks beyond short answer grading to enhance the learning experience and support student success. Here are some ways these models can be utilized: Personalized Feedback: NAMs can be used to provide personalized feedback to students based on their responses to assignments, quizzes, or exams. By analyzing student responses and identifying areas of strength and weakness, the model can generate tailored feedback that helps students understand their performance and areas for improvement. Identifying Learning Difficulties: Interpretable models like NAMs can be employed to identify learning difficulties or misconceptions in student responses. By analyzing patterns in student answers, the model can flag areas where students are struggling and provide insights to educators on how to address these challenges effectively. Adaptive Learning: NAMs can support adaptive learning systems by analyzing student responses in real-time and adjusting the learning materials or tasks based on individual student needs. By providing interpretable insights into student performance, these models can help educators tailor instruction to meet the diverse learning requirements of students. Assessment Design: Interpretable models can assist in designing assessments that effectively measure student understanding and skills. By analyzing the features that contribute to student performance, educators can create assessments that align with learning objectives and provide meaningful insights into student learning outcomes. Overall, the application of NAMs and similar interpretable models in education can revolutionize the way educators assess student learning, provide feedback, and support individualized learning paths for students.

Conceptos Básicos

Neural Additive Models (NAMs) can provide intelligible automatic grading of short answers while maintaining strong predictive performance compared to legacy explainable models.

Resumen

The researchers explore the use of Neural Additive Models (NAMs) for explainable automatic short answer grading (ASAG). NAMs combine the performance of neural networks with the interpretability of additive models, allowing stakeholders to understand which features of a student response are important for the predicted grade.

The researchers use a Knowledge Integration (KI) framework to guide feature engineering, creating inputs that reflect whether a student includes certain ideas in their response. They hypothesize that the inclusion (or exclusion) of these predefined KI ideas as features will be sufficient for the NAM to have good predictive power and interpretability.

The performance of the NAM is compared to a logistic regression (LR) model using the same features, and a non-explainable neural model, DeBERTa, that does not require feature engineering. The results show that the NAM outperforms the LR model in terms of the Quadratic Weighted Cohen's Kappa (QWK) metric, a standard ASAG evaluation metric, on the KI data at a statistically significant level. While the DeBERTa model performs better than the NAM, the difference is not statistically significant.

The researchers provide visualizations of the NAM's feature importance and shape functions, which allow stakeholders to understand which ideas in the student responses are most indicative of the assigned grade and how the model makes its predictions. This interpretability is a key advantage of the NAM over black-box neural models.

The findings suggest that NAMs may be a suitable alternative to legacy explainable models for ASAG, providing intelligibility without sacrificing too much predictive performance. The researchers note that further investigation is needed to generalize the results to different question types and domains.

Personalizar resumen

Reescribir con IA

Generar citas

Traducir fuente

A otro idioma

Generar mapa mental

del contenido fuente

Ver fuente

arxiv.org

Estadísticas

"The use of open-ended (OE) items is beneficial for student learning due to the generation effect or in combination with self-explanation."
"Many of the best performing ASAG models include some variation of a deep neural network (NN)."
"NNs are impressive predictors for high dimensional inputs like text embeddings, but predictive power tends to come at the cost of intelligibility."
"The inexplicable nature of ASAG models can be frustrating to both teachers and students when trying to make sense of, or learn from an automated grade."
"The research questions we seek to answer include, (1) can NAMs provide intelligible automatic grading such that stakeholders can understand which features of a response are important for its prediction, and (2) is the predictive performance of NAMs better than that of legacy explainable models like a LR and commensurate with that of an LLM classifier?"

Citas

"NAMs allow us to visually examine the contribution of each feature to the final predicted score for each response, similar to testing the significance of a regression coefficient."
"We hypothesize that the inclusion (or exclusion) of predefined KI ideas as features will be sufficient for the NAM to have good predictive power, as this is precisely what would guide a human scorer using the KI rubric."

Ideas clave extraídas de

Explainable Automatic Grading with Neural Additive Models

by Aubrey Condo... a las arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00489.pdf

Explainable Automatic Grading with Neural Additive Models

Consultas más profundas

How could the NAM approach be extended to handle more complex student responses, such as those that express multiple ideas or use more nuanced language?

The NAM approach can be extended to handle more complex student responses by incorporating techniques that allow for the modeling of multiple ideas within a single response. One way to achieve this is by implementing a hierarchical feature engineering process. Instead of treating each feature independently, the model can be designed to capture relationships between features that represent different ideas. This can involve creating feature combinations that reflect the presence of multiple concepts or ideas in a response. By considering the interactions between features, the NAM can better capture the complexity of student responses that express multiple ideas.
Additionally, the NAM model can be enhanced by incorporating contextual information. This can involve utilizing contextual embeddings or contextualized representations of student responses to capture the nuanced language used by students. By leveraging contextual information, the model can better understand the meaning and context of the language used in student responses, enabling it to handle more complex and nuanced expressions of ideas.

What are the potential limitations of relying on predefined features, and how could the NAM model be adapted to better capture the nuances of student understanding?

Relying solely on predefined features can limit the flexibility and adaptability of the NAM model, as it may struggle to capture the full range of nuances present in student responses. Predefined features may not encompass all the variations in language and expression that students use, leading to potential gaps in the model's ability to accurately assess student understanding. To address this limitation, the NAM model can be adapted in the following ways:

Dynamic Feature Generation: Instead of relying solely on predefined features, the model can be designed to dynamically generate features based on the content of student responses. This can involve using techniques like attention mechanisms to identify important parts of the response and generate features accordingly.

Semantic Embeddings: By incorporating semantic embeddings or contextualized word representations, the NAM model can better capture the nuances of student understanding. These embeddings can provide a more comprehensive representation of the language used in student responses, allowing the model to capture subtle variations in meaning and expression.

Fine-tuning with Feedback: The NAM model can be fine-tuned iteratively based on feedback from human graders. By incorporating feedback on where the model's predictions align or diverge from human assessments, the model can learn to better capture the nuances of student understanding over time.

Given the potential benefits of explainable AI in education, how might NAMs or similar interpretable models be applied to other educational tasks beyond short answer grading, such as providing personalized feedback or identifying learning difficulties?

NAMs and similar interpretable models can be applied to various educational tasks beyond short answer grading to enhance the learning experience and support student success. Here are some ways these models can be utilized:

Personalized Feedback: NAMs can be used to provide personalized feedback to students based on their responses to assignments, quizzes, or exams. By analyzing student responses and identifying areas of strength and weakness, the model can generate tailored feedback that helps students understand their performance and areas for improvement.

Identifying Learning Difficulties: Interpretable models like NAMs can be employed to identify learning difficulties or misconceptions in student responses. By analyzing patterns in student answers, the model can flag areas where students are struggling and provide insights to educators on how to address these challenges effectively.

Adaptive Learning: NAMs can support adaptive learning systems by analyzing student responses in real-time and adjusting the learning materials or tasks based on individual student needs. By providing interpretable insights into student performance, these models can help educators tailor instruction to meet the diverse learning requirements of students.

Assessment Design: Interpretable models can assist in designing assessments that effectively measure student understanding and skills. By analyzing the features that contribute to student performance, educators can create assessments that align with learning objectives and provide meaningful insights into student learning outcomes.

Overall, the application of NAMs and similar interpretable models in education can revolutionize the way educators assess student learning, provide feedback, and support individualized learning paths for students.