„A Novel Ensemble Method for Automated Short Answer Grading Based on Continuous Response IRT“: Automated Short Answer Grading (ASAG) is a field concerned with evaluating short answers written by students using various machine learning techniques. With the development of machine learning, several ASAG approaches have been proposed. According to prior surveys, an ASAG approach can be divided into two parts: language representation and learning algorithm. The choice of these two components can result in different theoretical underpinnings and use of information from short answers. To leverage the benefits of multiple approaches, ensemble methods that combine multiple approaches may lead to improved predictive performance than any one of the individual approaches alone. This study presents a novel ensemble method based on the Continuous Response Item Response Theory (IRT) model, which has been commonly used in psychometrics to combine the ratings from multiple human experts, for integrating different ASAG approaches. Using the validation with an open-accessed dataset ASAP-SAS, the performance of this new ensemble method will be compared to existing ensemble methods and individual approaches.