Intelligent Tutoring Systems (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
In the 21st century MOOC (Massive Open Online Courses) have proliferated like Coursera, Udemy, Edx and several applications like Duolingo, Quizzlet became quite popular. The researchers have a keen interest to acquire this systems with more intelligence, so that learning process could be overseen.
As it was described in the paper Computer-Assisted Instruction can increase students performance by 0.3 std while human tutors by 2.
Intelligent Tutoring Systems combine AI, cognitive science and educational theory which makes researches in this field arduous.
In the earlier studies 3 main modules have been defined in ITS:
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The expert Knowledge module - this module can be thought as a source of the knowledge. The purpose of this module is to generate questions, answers and the solutions for the particular problem.
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The student model module - the student skills and knowledge which varies constantly. It is very important for the tutor module to know about students current level and skills.
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The tutor module - the teaching strategy. This module has to decide which lesson to present to the student.
Recently, fourth module was added The user interface module:
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Data Science
Related
Intelligent Tutoring Systems (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Reinforcement Learning
Background (Accelerating Human Learning With Deep Reinforcement Learning)
Spaced Repetition
Leitner System
Supermemo System
Reinforcement Learning
Intelligent Tutoring Systems (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Relation between Tutoring Systems and Student learning
Trust Region Policy Optimization
Truncated Natural Policy Gradient
Recurrent Neural Network (RNN)