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.

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.

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.

scholarly journals PERANCANGAN INTELLIGENT TUTORING SYSTEM UNTUK PEMBELAJARAN JARINGAN KOMPUTER

SISTEMASI ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 28
Author(s):  
Aris Budianto ◽  
Rosihan Ari Yuana
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Naïve Bayes ◽  
Tutoring System ◽  
Video Tutorial ◽  
E Learning ◽  
One To One

Proses pembelajaran mata kuliah Jaringan Komputer di prodi Pendidikan Teknik Informatika (PTIK), Fakultas Keguruan dan Ilmu Pendidikan (FKIP), Universitas Sebelas Maret (UNS) terjadi sebuah gap antara Mahasiswa dari SMA dan SMK. Sebagian mahasiswa dengan latar belakang SMA belum memiliki konsep-konsep dasar Jaringan Komputer, sedangkan mahasiswa dari SMK sudah pada tahap sudah jauh lebih advance. Untuk menjembatani hal tersebut maka salah satu solusi yang bisa diimplementasikan adalah sebuah e-learning yang menyediakan sistem pembelajaran mandiri. E-learning yang dikembangkan akan menyediakan materi yang lengkap, latihan dan dan video tutorial. Pada penelitian ini, berbeda dengan E-Learning konvensional yang bersifat sama (flat) untuk setiap pengguna, E-Learning yang akan dikembangkan dilengkapi dengan Machine Learning metode Naïve bayes. Machine Learning akan membantu proses pembelajaran dengan pendekatan one-to-one, dimana sistem akan memiliki kemampuan mendeteksi kemampuan mahasiswa yang menggunakan E-Learning. Sistem menyesuaikan materi dan latihan sesuai dengan kemampuan pengguna dalam proses belajar mandiri. Dengan tutorial yang bertahap dan pendekatan yang berbeda dalam proses belajar setiap siswa diharapkan mampu meningkatkan pemahaman siswa mengenai mata kuliah Jaringan Komputer di PTIK, FKIP, UNS.

A Novel Architecture for Learner-Centric Curriculum Sequencing in Adaptive Intelligent Tutoring System

2018 ◽  
Vol 20 (3) ◽  
pp. 1-20
Author(s):  
Ninni Singh ◽  
Neelu Jyothi Ahuja ◽  
Amit Kumar
Keyword(s):  
Learning Style ◽  
Seismic Data ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Data Interpretation ◽  
Tutoring System ◽  
Face To Face ◽  
Learning Path ◽  
Curriculum Sequencing

An ideal face-to-face tutor learner interaction aims to offer learning to the learner in a manner that best suits an individual learner's learning level and learning style. This ability of differentiated instruction has been built in Seis-Tutor Intelligent Tutoring system, developed to offer subject matter knowledge of ‘Seismic Data Interpretation,' a field of geo-physics. The detailed architecture of learner-centric curriculum sequencing module, built to this effect, with its components, sub-components, their interconnected functioning, to generate exclusive learning path, have been described. An algorithm for learner-centric curriculum sequencing, a mathematical model and proposed implementation using a case study has been elaborated.

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