An artificial intelligence (AI) model is created to more effectively predict how students are learning through educational games. The improved model called multi-task learning utilizing AI training concepts can be applied to boost the advantages of instructional and learning results.
Multi-task learning is an approach in which a model is required to perform various tasks. For example, the model is applied to predict students' ability to correctly answer test questions and the task of the model is based on the questions and the overall score of the test.
The AI model identified common behaviors of students
In this study, 181 students who are the subjects of the research are examined and data mining by AI through how students play games and answer Question 1 in the test. Common patterns of students in answering right or wrong to Question 1 will be the background for AI to determine a new student's way of answering Question 1. This function is performed for every question at the same time with the same gameplay but the different questions.
The results obtained from the model application are completely different. Relied on typical AI training methods, the researchers noticed that the multi-task model was about 10 percent more accurate than other models. Specifically, the model can send a notification to the teacher if the student needs further instruction through student participation in the gameplay. The model can also aid in-game features such as plot changes to help students clarify struggle issues.
In psychology, each question posed has a different value. With deep learning and machine learning approaches to AI, the researchers have the ability to take an interdisciplinary approach that combines this aspect of psychology. This also provides the opportunity for more complicated modeling techniques to be incorporated into educational software, adapting to the requirements of students in learning.
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