AI in Education: An Initiative to Assist Student in Learning
AI in Education: An Initiative to Assist Student in Learning
By Astha Oriel
September 3, 20120
Summary
It becomes difficult for a teacher, to selectively aid student in learning, especially when they also feel the burden of collective performance.
To address this issue, an artificial intelligence is designed by the researchers from North Carolina State University, from predicting the position of educational games in inducing learning amongst students.
Cited as the Predictive Student Modelling in Educational Games with Multi Tasking, the paper describes the utilization of predictive analysis as the basis for formulating the performance of a student.
Predictive Student Modelling is an assessment test that determines the performance of a student based on his/her past interaction with the learning environment.
It is essential for tailoring the experiences of students in a range of learning environments like intelligent tutoring and educational games.
The Predictive Student Models represents the student knowledge as an aggregate of students performance.
The MTL model involves assessment of a students' performance based on only a standard neural network and not on the sequence of students behaviour in an adaptive learning environment.
The researchers deployed the Item Response Theory which models the probability that the student will deliver an answer based on the characteristics incorporated by the test-taker and the questions.
The paper also states that with the help of educational games like Crystal Island, students are more prone in gaining knowledge in an adaptive learning environment.
This model can thus be employed by educators or teachers as an "Early warning system", which would enable the re-allocation of assistance of those students that requires attention for learning.
Reference
Oriel, A. (2020, September 03). AI in Education: An Initiative to Assist Student in Learning. Retrieved September 08, 2020, from https://www.analyticsinsight.net/ai-education-initiative-assist-student-learning/