抄録
Offer Organization: Japan Society for the Promotion of Science, System Name: Grants-in-Aid for Scientific Research Grant-in-Aid for Challenging Research (Exploratory), Category: Grant-in-Aid for Challenging Research (Exploratory), Fund Type: -, Overall Grant Amount: - (direct: 4900000, indirect: 1470000)
In automated essay scoring (AES), essays are automatically graded without human raters. Many AES models based on various manually designed features or various architectures of deep neural networks have been proposed over the past few decades. Each AES model has unique advantages and characteristics. Therefore, rather than using a single AES model, appropriate integration of predictions from various AES models is expected to achieve higher scoring accuracy. In the present paper, we develops 1) a new item response theory model that can estimate scores while considering characteristics of individual human-raters and rubric-items, and 2) a method that uses the item response theory model to integrate prediction scores from various AES models while taking into account differences in the characteristics of scoring behavior.