scholarly journals Development of an Intelligent Tutoring System Using Bayesian Networks and Fuzzy Logic for a Higher Student Academic Performance

2020 ◽  
Vol 10 (19) ◽  
pp. 6638
Author(s):  
Meltem Eryılmaz ◽  
Afaf Adabashi
Keyword(s):  
Fuzzy Logic ◽  
Academic Performance ◽  
Academic Success ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Learning System ◽  
Analysis Of Covariance ◽  
Tutoring System ◽  
Post Test ◽  
E Learning

In this experimental study, an intelligent tutoring system called the fuzzy Bayesian intelligent tutoring system (FB-ITS), is developed by using artificial intelligence methods based on fuzzy logic and the Bayesian network technique to adaptively support students in learning environments. The effectiveness of the FB-ITS was evaluated by comparing it with two other versions of an Intelligent Tutoring System (ITS), fuzzy ITS and Bayesian ITS, separately. Moreover, it was evaluated by comparing it with an existing traditional e-learning system. In order to evaluate whether the academic performance of the students in different learning groups differs or not, analysis of covariance (ANCOVA) was used based on the students’ pre-test and post-test scores. The study was conducted with 120 undergraduate university students. Results showed that students who studied using FB-ITS had significantly higher academic performance on average compared to other students who studied with the other systems. Regarding the time taken to perform the post-test, the results indicated that students who used the FB-ITS needed less time on average compared to students who used the traditional e-learning system. From the results, it could be concluded that the new system contributed in terms of the speed of performing the final exam and high academic success.

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 Refactoring the Whitby Intelligent Tutoring System for Clean Architecture

2021 ◽  
pp. 1-17
Author(s):  
PAUL S. BROWN ◽  
VANIA DIMITROVA ◽  
GLEN HART ◽  
ANTHONY G. COHN ◽  
PAULO MOURA
Keyword(s):  
Process Analysis ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Learning System ◽  
Third Party ◽  
Situation Calculus ◽  
Tutoring System ◽  
Code Reuse ◽  
Server Side ◽  
Systems Model

Abstract Whitby is the server-side of an Intelligent Tutoring System application for learning System-Theoretic Process Analysis (STPA), a methodology used to ensure the safety of anything that can be represented with a systems model. The underlying logic driving the reasoning behind Whitby is Situation Calculus, which is a many-sorted logic with situation, action, and object sorts. The Situation Calculus is applied to Ontology Authoring and Contingent Scaffolding: the primary activities within Whitby. Thus many fluents and actions are aggregated in Whitby from these two sub-applications and from Whitby itself, but all are available through a common situation query interface that does not depend upon any of the fluents or actions. Each STPA project in Whitby is a single situation term, which is queried for fluents that include the ontology, and to determine what pedagogical interventions to offer. Initially Whitby was written in Prolog using a module system. In the interest of a cleaner architecture and implementation with improved code reuse and extensibility, the initial application was refactored into Logtalk. This refactoring includes decoupling the Situation Calculus reasoner, Ontology Authoring framework, and Contingent Scaffolding framework into third-party libraries that can be reused in other applications. This extraction was achieved by inverting dependencies via Logtalk protocols and categories, which are reusable interfaces and components that provide functionally cohesive sets of predicate declarations and predicate definitions. In this paper the architectures of two iterations of Whitby are evaluated with respect to the motivations behind the refactor: clean architecture enabling code reuse and extensibility.

The Role of Affect in an Agent-Based Collaborative E-Learning System Used for Engineering Education

2009 ◽  
pp. 510-528
Author(s):  
Mohamed Ben Ammar ◽  
Mahmoud Neji ◽  
Adel M. Alimi
Keyword(s):  
Facial Expressions ◽  
Affective Computing ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Learning Systems ◽  
Learning System ◽  
Agent Based ◽  
Affective Behaviors ◽  
E Learning

Affective computing is a new artificial intelligence area that deals with the possibility of making computers able to recognize human emotions in different ways. This chapter represents an implemented framework, which integrates this new area with an intelligent tutoring system. The authors argue that tutor agents providing socially appropriate affective behaviors would provide a new dimension for collaborative learning systems. The main goal is to analyse learner facial expressions and show how affective computing could contribute to learning interactions, both by recognizing learner emotions during learning sessions and by responding appropriately.

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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