Основні поняття
Deep learning algorithms have emerged as a powerful tool for tackling a wide range of educational data science tasks, from predicting student performance and detecting affect to automating assessment and recommendation systems. While deep learning offers significant advantages in terms of predictive accuracy, flexibility, and automatic feature engineering, it also faces limitations around interpretability, model complexity, and data requirements that must be addressed for successful real-world adoption.
Анотація
This article provides an overview of the use of deep learning in educational data science. It first introduces the context of machine learning and deep learning, highlighting the key architectural differences between various neural network models. The authors then discuss the advantages of deep learning, including its increased predictive accuracy, automatic feature engineering capabilities, flexible input handling, and ability for continuous model training and transfer learning.
The article also outlines the limitations of deep learning, such as diminished interpretability, heightened model complexity, and the need for large datasets. These challenges can make it difficult for deep learning models to be adopted outside of research settings and into real-world educational applications.
The main body of the article surveys the direct and indirect uses of deep learning in education. Direct uses include predicting future student actions, knowledge tracing, automated assessment, affect detection, recommendation systems, and behavior detection. Indirect uses leverage deep learning for feature extraction, computer vision, automatic speech recognition, and automated qualitative coding.
Finally, the authors discuss the future of deep learning in education, focusing on three key areas: increasing trust through greater model transparency, contributing to learning theory, and expanding the use of deep neural networks beyond research labs to directly impact learners and instructors. Overcoming the interpretability challenge through explainable AI methods, conducting more real-world experiments, and integrating deep learning into existing educational technologies are identified as important next steps.
Статистика
"Deep learning models are often used for the time- and effort-savings promised by their automatic feature engineering."
"It is generally accepted that the more parameters a model has, the more data is needed to adequately train it."
"There are documented cases of algorithmic bias and unfairness in education, in which biased data disproportionally and negatively affects students from historically disadvantaged populations."
Цитати
"Due to their complexity, it is often impossible to determine why a model is making specific decisions."
"Because of the bigger data requirements, privacy and security breaches have the potential to affect more people, more quickly, and more deeply."
"Until researchers find ways to appropriately do so, the expanded risks and diminished interpretability of deep learning suggest that it may be unwise—and in some cases even unethical—to use them for high-stakes educational tasks."