Initializing the student model using stereotypes and machine learning

Using Machine Learning to Advise a Student Model

Student Modelling: The Key to Individualized Knowledge-Based Instruction ◽

10.1007/978-3-662-03037-0_5 ◽

1994 ◽

pp. 127-146 ◽

Cited By ~ 3

Author(s):

Beverly Park Woolf ◽

Tom Murray

Keyword(s):

Machine Learning ◽

Student Model

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The Application Design of Machine Learning in Intelligent Learning Support System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.403-408.1535 ◽

2011 ◽

Vol 403-408 ◽

pp. 1535-1538

Author(s):

Hui Bin Yin ◽

Jun Han ◽

Jing Liu ◽

Jing Dong

Keyword(s):

Machine Learning ◽

Cognitive Ability ◽

Support System ◽

Cognitive Abilities ◽

Skills Training ◽

Learning Support ◽

Student Model ◽

Personalized Service ◽

Learning Support System ◽

Different Levels

The key of intelligent learning support system lied in providing personalized service according to different students. Based on cognitive student model, the article designed an intelligent learning support system based on machine learning, which automatically diagnosed students’ cognitive abilities in order to make students in different levels get different depth’s answers, and appropriate level skills training. This would stimulate students' interest in learning and improve students' cognitive ability.

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A New Student Modeling Technique With Convolutional Neural Networks: LearnerPrints

Journal of Educational Computing Research ◽

10.1177/0735633120969216 ◽

2020 ◽

pp. 073563312096921

Author(s):

Şeyhmus Aydoğdu

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Management System ◽

Learning Management System ◽

Modeling Technique ◽

Machine Learning Techniques ◽

Student Modeling ◽

Student Model ◽

Learning Techniques

Student modeling is one of the most important processes in adaptive systems. Although learning is individual, a model can be created based on patterns in student behavior. Since a student model can be created for more than one student, the use of machine learning techniques in student modeling is increasing. Artificial neural networks (ANNs), which form one group of machine learning techniques, are among the methods most frequently used in learning environments. Convolutional neural networks (CNNs), which are specific types of these networks, are used effectively for complex problems such as image processing, computer vision and speech recognition. In this study, a student model was created using a CNN due to the complexity of the learning process, and the performance of the model was examined. The student modeling technique used was named LearnerPrints. The navigation data of the students in a learning management system were used to construct the model. Training and test data were used to analyze the performance of the model. The classification results showed that CNNs can be used effectively for student modeling. The modeling was based on the students’ achievement and used the students’ data from the learning management system. The study found that the LearnerPrints technique classified students with an accuracy of over 80%.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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