scholarly journals Intelligent Tutoring System

2014 ◽  
Vol 5 (1) ◽  
pp. 1-7
Author(s):  
Fitria Amastini
Keyword(s):  
Neural Network ◽  
Bayesian Network ◽  
Adaptive System ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Tutoring System ◽  
Development Method ◽  
Distant Learning ◽  
E Learning ◽  
Index Terms

Intelligent Tutoring System is a tutor behaviour system  which can be used as an alternative goal for interactive e-learning and distant learning. This system can provide an adaptive system to support student’s learning and retention process based on their characteristic and needed. There are development method such as bayesian network, and neural network that can build fundamental component of Intelligent Tutoring System. This paper will give some concepts and examples for implementing those method from other papers. Index Terms—intelligent tutoring system, artificial intelligent, neural network, bayesian network, ontology

scholarly journals Intelligent Tutoring System

2020 ◽  
pp. 121-144
Author(s):  
Abhishek Singh Rathore ◽  
Siddhartha Kumar Arjaria
Keyword(s):  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
The Internet ◽  
Tutoring System ◽  
Time Data ◽  
Self Regulated Learning ◽  
Formal And Informal Learning ◽  
Real Time Data ◽  
E Learning ◽  
Teaching Learning

With digitization, a rapid growth is seen in educational technology. Different formal and informal learning contents are available on the internet. Intelligent tutoring system provides personalized e-learning to the learners. Different attributes like historical data, real-time data, behavioral, and cognitive are usually used for personalization. Based on the personalization, the intelligent tutoring system aims to provide easy and effective understanding. Recent research highlights the effect of learner's behavior and emotions on effective teaching-learning process. This chapter provides a brief description of the intelligent tutoring system, current developments, instructional techniques, proposed solution, and future recommendations. The emphasis of the study is to provide insights on self-regulated learning.

scholarly journals Perancangan Intelligent Tutoring System Menggunakan Chatbot pada Mata Pelatihan Barang Dalam Keadaan Terbungkus

2020 ◽  
Vol 1 (1) ◽  
pp. 10-20
Author(s):  
Wicaksono Febriantoro ◽  
Achmad Nurhadi
Keyword(s):  
Distance Learning ◽  
Design Science ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Science Research ◽  
Design Science Research ◽  
Tutoring System ◽  
Framework Design ◽  
E Learning

ASN mempunyai kewajiban pengembangan kompetensi selama minimal 20 JP/tahun. Akan tetapi tidak semua ASN dapat mengikuti pelatihan sebagai bentuk pengembangan kompetensi dikarenakan terbatasnya kuota/jumlah pelatihan tatap muka (klasikal) yang tersedia. Regulasi terbaru memberikan alternatif pembelajaran non klasikal yang tidak harus bertatap muka antara lain e-learning, distance learning dan belajar mandiri. Belajar mandiri dapat digunakan siswa untuk meningkatkan kompetensinya menggunakan berbagai sumber belajar yang tersedia baik konvensional dan berbasis pada teknologi informasi. Dalam paper ini akan dibahas salah satu tools pendukung belajar mandiri berupa perancangan intelligent tutoring system menggunakan chatbot pada mata pelatihan barang dalam keadaan terbungkus (BDKT). Perancangan menggunakan framework Design Science Research, mulai dari penetapan tujuan, perancangan desain alur percakapan chatbot, pengembangan aplikasi menggunakan chatfuel, demonstrasi melalui simulasi via Facebook Messenger dan Evaluasi Blackbox. Prototype yang dihasilkan telah dapat mengikuti alur percakapan yang dirancang dan dapat digunakan sebagai pendukung belajar mandiri kapan saja dan dimana saja (domain pelajari materi dan evaluasi) asalkan terkoneksi dengan internet.

Hybrid Intelligent Tutoring System With Didactic Transposition Of The Subjects Guided By Expert Knowlegdment And Self Organizing Maps Neural Network

2014 ◽  
Vol 12 (8) ◽  
pp. 1539-1544 ◽  
Author(s):  
Sirlon Diniz de Carvalho ◽  
Edna Lucia Flores ◽  
Francisco Ramos de Melo ◽  
Luiz Fernando Batista Loja ◽  
Milena Bueno Pereira Carneiro
Keyword(s):  
Neural Network ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Tutoring System ◽  
Self Organizing Maps ◽  
Didactic Transposition ◽  
Self Organizing

A Heuristic Method for Learning Path Sequencing for Intelligent Tutoring System (ITS) in E-learning

2011 ◽  
Vol 7 (4) ◽  
pp. 65-80 ◽  
Author(s):  
Sami A. M. Al-Radaei ◽  
R. B. Mishra
Keyword(s):  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Heuristic Method ◽  
Tutoring System ◽  
Dynamic Learning ◽  
Java Programming ◽  
Learning Path ◽  
Course Sequencing ◽  
E Learning ◽  
Semantic Values

Course sequencing is one of the vital aspects in an Intelligent Tutoring System (ITS) for e-learning to generate the dynamic and individual learning path for each learner. Many researchers used different methods like Genetic Algorithm, Artificial Neural Network, and TF-IDF (Term Frequency- Inverse Document Frequency) in E-leaning systems to find the adaptive course sequencing by obtaining the relation between the courseware. In this paper, heuristic semantic values are assigned to the keywords in the courseware based on the importance of the keyword. These values are used to find the relationship between courseware based on the different semantic values in them. The dynamic learning path sequencing is then generated. A comparison is made in two other important methods of course sequencing using TF-IDF and Vector Space Model (VSM) respectively, the method produces more or less same sequencing path in comparison to the two other methods. This method has been implemented using Eclipse IDE for java programming, MySQL as database, and Tomcat as web server.

A Heuristic Method for Learning Path Sequencing for Intelligent Tutoring System (ITS) in E-learning

2012 ◽  
pp. 285-301
Author(s):  
Sami A. M. Al-Radaei ◽  
R. B. Mishra
Keyword(s):  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Heuristic Method ◽  
Tutoring System ◽  
Dynamic Learning ◽  
Java Programming ◽  
Learning Path ◽  
Course Sequencing ◽  
E Learning ◽  
Semantic Values

Course sequencing is one of the vital aspects in an Intelligent Tutoring System (ITS) for e-learning to generate the dynamic and individual learning path for each learner. Many researchers used different methods like Genetic Algorithm, Artificial Neural Network, and TF-IDF (Term Frequency- Inverse Document Frequency) in E-leaning systems to find the adaptive course sequencing by obtaining the relation between the courseware. In this paper, heuristic semantic values are assigned to the keywords in the courseware based on the importance of the keyword. These values are used to find the relationship between courseware based on the different semantic values in them. The dynamic learning path sequencing is then generated. A comparison is made in two other important methods of course sequencing using TF-IDF and Vector Space Model (VSM) respectively, the method produces more or less same sequencing path in comparison to the two other methods. This method has been implemented using Eclipse IDE for java programming, MySQL as database, and Tomcat as web server.

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