Researchers from North Carolina State University have designed an artificial intelligence (AI) model that is better able to predict how much students are learning in educational games. The improved model, which uses an AI training concept named multi-task learning, could be used to improve both instruction and learning outcomes.
“We wanted the model to be able to predict whether a student would answer each question on a test correctly, based on the student’s behavior while playing an educational game called Crystal Island,” says Jonathan Rowe, co-author of a paper on the work.
“The standard approach for solving this problem looks only at overall test score,” Rowe says. “In our multi-task learning framework, the model has 17 tasks — because the test has 17 questions.”
The researchers collected gameplay and testing data from 181 students. The AI could look at each student’s game- play and how each student answered Question 1 on the test. By identifying common behaviors of students who answered Question 1 correctly, and those of students who got Question 1 wrong, the AI could determine how a new student would answer Question 1.
This function is performed for every question at the same time. The gameplay being reviewed for a given student is the same, but the AI looks at that behavior in Question 2, Question 3, and so on.
The researchers found that the multi-task model was about 10 percent more accurate than other models that relied on conventional AI training methods.
“We expect that this type of model could tell teachers when a student’s gameplay suggests the student may need additional instruction. It’s also expected to facilitate adaptive gameplay features in the game itself. For example, altering a storyline in order to revisit the concepts that a student is struggling with.” says Michael Geden, first author of the paper.
“Psychology has long recognized that different questions have different values,” Geden says. “Our work here takes an interdisciplinary ( 跨学科的) approach that marries this aspect of psychology with deep learning and machine learning approaches to AI.”
“This also opens the door to incorporating more complex modeling techniques into educational software.” says Andrew Emerson, co-author of the paper.
【小题1】What makes the multi-task model different from conventional ones?| A．It involves an educational game. | B．It researches on adequate samples. |
| C．It analyzes the testing data separately. | D．It applies a new psychological theory. |
| A．Students played different games in the task. |
| B．Students should answer 17 questions after playing the game. |
| C．The multi-task model is the first to predict students’ learning performance. |
| D．The higher accuracy in prediction lies in the identification of common behaviors in every question. |
| A．Provide students with personalized guidance. |
| B．Help the game adapt itself to meet various needs. |
| C．Combine deep learning with machine learning further. |
| D．Incorporate more advanced technology into educational software. |
| A．AI experts. | B．Psychologists. | C．Teachers. | D．Test writers. |
