scholarly journals Domain Model Discovery from Textbooks for Computer Programming Intelligent Tutors

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Rabin Banjade ◽  
Priti Oli ◽  
Lasang Jimba Tamang ◽  
Jeevan Chapagain ◽  
Vasile Rus
Keyword(s):  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Domain Model ◽  
Tutoring Systems ◽  
Novel Approach ◽  
Ranking Strategy ◽  
Domain Models ◽  
Text Ranking ◽  
Automated Discovery ◽  
Key Phrases

We present a novel approach to intro-to-programming domain model discovery from textbooks using an over-generation and ranking strategy. We first extract candidate key phrases from each chapter in a Computer Science textbook focusing on intro-to-programming and then rank those concepts according to a number of metrics such as the standard tf-idf weight used in information retrieval and metrics produced by other text ranking algorithms. Specifically, we conduct our work in the context of developing an intelligent tutoring system for source code comprehension for which a specification of the key programming concepts is needed - the system monitors students' performance on those concepts and scaffolds their learning process until they show mastery of the concepts. Our experiments with programming concept instruction from Java textbooks indicate that the statistical methods such as KP Miner method are quite competitive compared to other more sophisticated methods. Automated discovery of domain models will lead to more scalable Intelligent Tutoring Systems (ITSs) across topics and domains, which is a major challenge that needs to be addressed if ITSs are to be widely used by millions of learners across many domains.

Agent-Based Distributed Intelligent Tutoring System Using Case-Based Reasoning

2018 ◽  
pp. 901-928
Author(s):  
Shweta ◽  
Praveen Dhyani ◽  
O. P. Rishi
Keyword(s):  
Intelligent System ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Domain Model ◽  
Student Modeling ◽  
Specific Situation ◽  
Pedagogical Strategies ◽  
Tutoring Systems ◽  
Agent Based ◽  
Case Based

Intelligent Tutoring Systems have proven their worth in multiple ways and in multiple domains in education. In this chapter, the proposed Agent-Based Distributed ITS using CBR for enhancing the intelligent learning environment is introduced. The general architecture of the ABDITS is formed by the three components that generally characterize an ITS: the Student Model, the Domain Model, and the Pedagogical Model. In addition, a Tutor Model has been added to the ITS, which provides the functionality that the teacher of the system needs. Pedagogical strategies are stored in cases, each dictating, given a specific situation, which tutoring action to make next. Reinforcement learning is used to improve various aspects of the CBR module: cases are learned and retrieval and adaptation are improved, thus modifying the pedagogical strategies based on empirical feedback on each tutoring session. The student modeling is a core component in the development of proposed ITS. In this chapter, the authors describe how a Multi-Agent Intelligent system can provide effective learning using Case-Based Student Modeling.

Agent-Based Distributed Intelligent Tutoring System Using Case-Based Reasoning

2015 ◽  
pp. 211-236 ◽  
Author(s):  
Shweta ◽  
Praveen Dhyani ◽  
O. P. Rishi
Keyword(s):  
Intelligent System ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Domain Model ◽  
Student Modeling ◽  
Specific Situation ◽  
Pedagogical Strategies ◽  
Tutoring Systems ◽  
Agent Based ◽  
Case Based

Intelligent Tutoring Systems have proven their worth in multiple ways and in multiple domains in education. In this chapter, the proposed Agent-Based Distributed ITS using CBR for enhancing the intelligent learning environment is introduced. The general architecture of the ABDITS is formed by the three components that generally characterize an ITS: the Student Model, the Domain Model, and the Pedagogical Model. In addition, a Tutor Model has been added to the ITS, which provides the functionality that the teacher of the system needs. Pedagogical strategies are stored in cases, each dictating, given a specific situation, which tutoring action to make next. Reinforcement learning is used to improve various aspects of the CBR module: cases are learned and retrieval and adaptation are improved, thus modifying the pedagogical strategies based on empirical feedback on each tutoring session. The student modeling is a core component in the development of proposed ITS. In this chapter, the authors describe how a Multi-Agent Intelligent system can provide effective learning using Case-Based Student Modeling.

Using a Knapsack Model to Optimize Continuous Building of a Hybrid Intelligent Tutoring System

2016 ◽  
Vol 7 (2) ◽  
pp. 1-18 ◽  
Author(s):  
Maha Khemaja
Keyword(s):  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Time Budget ◽  
Core Competencies ◽  
Portable Devices ◽  
Dynamic Learning ◽  
Tutoring Systems ◽  
Fine Grained ◽  
Intelligent Decision Making ◽  
Knapsack Model

Intelligent Tutoring Systems (ITS) provide an alternative to the traditional “one size fits all” approach. Their main aim is to adapt learning content, activities and paths to support learners. Meanwhile, during the last decades, advances in lightweight, portable devices and wireless technologies had drastically impacted Mobile and Ubiquitous environments' development which has driven opportunities towards more personalized, context-aware and dynamic learning processes. Moreover, mobile and hand held devices could be advantageous to incremental learning, based on very short and fine grained activities and resources delivery. However, measuring efficiency and providing the most relevant combination/orchestration of learning activities, resources and paths remains and open and challenging problem especially for enterprises where choices and decisions face several constraints as time, budget, targeted core competencies, etc. This paper, attempts to provide a knapsack based model and solution in order to implement ITS's intelligent decision making about best combination and delivery of e-training activities and resources especially in the context of fast changing Information and Communication Technology (ICT) domain and its required skills. An android and OSGi based prototype is implemented to validate the proposal through some realistic use cases.

scholarly journals Intelligent Tutoring Systems

2017 ◽  
Vol 26 (4) ◽  
pp. 717-727 ◽  
Author(s):  
Vladimír Bradáč ◽  
Kateřina Kostolányová
Keyword(s):  
Intelligent Tutoring Systems ◽  
Technological Development ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Point Of View ◽  
Tertiary Sector ◽  
Tutoring Systems ◽  
Number Of Ends ◽  
E Learning ◽  
Education Of Students

AbstractThe importance of intelligent tutoring systems has rapidly increased in past decades. There has been an exponential growth in the number of ends users that can be addressed as well as in technological development of the environments, which makes it more sophisticated and easily implementable. In the introduction, the paper offers a brief overview of intelligent tutoring systems. It then focuses on two types that have been designed for education of students in the tertiary sector. The systems use elements of adaptivity in order to accommodate as many users as possible. They serve both as a support of presence lessons and, primarily, as the main educational environment for students in the distance form of studies – e-learning. The systems are described from the point of view of their functionalities and typical features that show their differences. The authors conclude with an attempt to choose the best features of each system, which would lead to creation of an even more sophisticated intelligent tutoring system for e-learning.

Dynamic Decision Networks Applications in Active Learning Simulators

2012 ◽  
pp. 248-270
Author(s):  
Julieta Noguez ◽  
Karla Muñoz ◽  
Luis Neri ◽  
Víctor Robledo-Rella ◽  
Gerardo Aguilar
Keyword(s):  
Active Learning ◽  
Intelligent Tutoring ◽  
Learning Experience ◽  
Intelligent Tutoring System ◽  
Cognitive State ◽  
Tutoring Systems ◽  
Decision Networks ◽  
Probabilistic Relational Models ◽  
Entity Relationship ◽  
Definition Of

Active learning simulators (ALSs) allow students to practice and carry out experiments in a safe environment – anytime, anywhere. Well-designed simulations may enhance learning, and provide the bridge from concept to practical understanding. Nevertheless, learning with ALS depends largely on the student’s ability to explore and interpret the performed experiments. By adding an Intelligent Tutoring System (ITS), it is possible to provide individualized personal guidance to students. The challenges are how an ITS properly assesses the cognitive state of the student based on the results of experiments and the student’s interaction, and how it provides adaptive feedback to the student. In this chapter we describe how an ITS based on Dynamic Decision Networks (DDNs) is applied in an undergraduate Physics scenario where the aim is to adapt the learning experience to suit the learners’ needs. We propose employing Probabilistic Relational Models (PRMs) to facilitate the construction of the model. These are frameworks that enable the definition of Probabilistic Graphical and Entity Relationship Models, starting from a domain, and in this case, environments of ALSs. With this representation, the tutor can be easily adapted to different experiments, domains, and student levels, thereby minimizing the development effort for building and integrating Intelligent Tutoring Systems (ITS) for ALSs. A discussion of the methodology is addressed, and preliminary results are presented.

Multiagent Based Selection of Tutor-Subject-Student Paradigm in an Intelligent Tutoring System

2012 ◽  
pp. 47-71
Author(s):  
Kiran Mishra ◽  
R. B. Mishra
Keyword(s):  
Intelligent Tutoring ◽  
Teaching Strategy ◽  
Intelligent Tutoring System ◽  
Selection Index ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Analytical Reasoning ◽  
The Subject ◽  
Effective Models ◽  
Selection Of

Intelligent tutoring systems (ITS) aim at development of two main interconnected modules: pedagogical module and student module .The pedagogical module concerns with the design of a teaching strategy which combines the interest of the student, tutor’s capability and characteristics of subject. Very few effective models have been developed which combine the cognitive, psychological and behavioral components of tutor, student and the characteristics of a subject in ITS. We have developed a tutor-subject-student (TSS) paradigm for the selection of a tutor for a particular subject. A selection index of a tutor is calculated based upon his performance profile, preference, desire, intention, capability and trust. An aptitude of a student is determined based upon his answering to the seven types of subject topic categories such as Analytical, Reasoning, Descriptive, Analytical Reasoning, Analytical Descriptive, Reasoning Descriptive and Analytical Reasoning Descriptive. The selection of a tutor is performed for a particular type of topic in the subject on the basis of a student’s aptitude.

Multiagent Based Selection of Tutor-Subject-Student Paradigm in an Intelligent Tutoring System

2010 ◽  
Vol 6 (1) ◽  
pp. 46-70 ◽  
Author(s):  
Kiran Mishra ◽  
R.B. Mishra
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Teaching Strategy ◽  
Intelligent Tutoring System ◽  
Selection Index ◽  
Tutoring Systems ◽  
Analytical Reasoning ◽  
The Subject ◽  
Effective Models ◽  
Selection Of

Intelligent tutoring systems (ITS) aim at development of two main interconnected modules: pedagogical module and student module .The pedagogical module concerns with the design of a teaching strategy which combines the interest of the student, tutor’s capability and characteristics of subject. Very few effective models have been developed which combine the cognitive, psychological and behavioral components of tutor, student and the characteristics of a subject in ITS. We have developed a tutor-subject-student (TSS) paradigm for the selection of a tutor for a particular subject. A selection index of a tutor is calculated based upon his performance profile, preference, desire, intention, capability and trust. An aptitude of a student is determined based upon his answering to the seven types of subject topic categories such as Analytical, Reasoning, Descriptive, Analytical Reasoning, Analytical Descriptive, Reasoning Descriptive and Analytical Reasoning Descriptive. The selection of a tutor is performed for a particular type of topic in the subject on the basis of a student’s aptitude.

Social and Affective Agents to Motivate Collaboration on Agent-Based Intelligent Tutoring Systems

2008 ◽  
pp. 201-227
Author(s):  
Elisa Boff ◽  
Cecília Dias Flores
Keyword(s):  
Learning Style ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Affective State ◽  
Educational Systems ◽  
Probabilistic Networks ◽  
Tutoring Systems ◽  
Agent Based ◽  
Social Agent ◽  
The Social

This chapter presents a social and affective agent, named social agent, that has been modeled using probabilistic networks in order to support and motivate collaboration in an intelligent tutoring system (ITS). The social agent suggests to students a workgroup to join in. Our testbed ITS is called AMPLIA, a probabilistic multiagent environment to support the diagnostic reasoning development and the diagnostic hypotheses modeling of domains with complex and uncertain knowledge, as the medical area. The AMPLIA environment is one of the educational systems, integrated in Portedu, which is a Web portal that provides access to educational contents and systems. The social agent belongs to Portedu platform and it is used by AMPLIA. The social agent reasoning is based on individual aspects, such as learning style, performance, affective state, personality traits, and group aspects, as acceptance and social skills. The chapter also presents some experiments using AMPLIA, and results obtained by the social agent.

scholarly journals Increasing the Adaptivity of an Intelligent Tutoring System with Educational Data Mining: A System Overview

2016 ◽  
Vol 11 (03) ◽  
pp. 67 ◽  
Author(s):  
Igor Jugo ◽  
Božidar Kovačić ◽  
Vanja Slavuj
Keyword(s):  
Data Mining ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Educational Data Mining ◽  
Knowledge Domain ◽  
Systems Architecture ◽  
Tutoring Systems ◽  
E Learning ◽  
Mining Tools

Intelligent Tutoring Systems (ITSs) are inherently adaptive e-learning systems usually created for teaching well-defined domains (e.g., mathematics). Their objective is to guide the student towards a predefined goal such as completing a lesson, task, or mastering a skill. Defining goals and guiding students is more complex in ill-defined domains where the expert defines the model of the knowledge domain or the students have freedom to follow their own path through it. In this paper we present an overview of our systems architecture that integrates the ITS with data mining tools and performs a number of educational data mining processes to increase the adaptivity and, consequently, the efficiency of the ITS.

Sign in / Sign up

Export Citation Format

Share Document