scholarly journals An Intelligent framework for Blended Learning

2018 ◽  
Vol 19 (2) ◽  
pp. 37-45
Author(s):  
Armando Ordóñez ◽  
Martha Giraldo G. ◽  
Freddy Muñoz ◽  
Hugo Ordoñez ◽  
Yeni Rosero
Keyword(s):  
Student Performance ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Online Course ◽  
Tutoring Systems ◽  
Personalized Education ◽  
Open Source Framework ◽  
Implementation Costs

Personalized education contributes to the motivation of the students and improves student performance. Some tools such as the Intelligent Tutoring Systems have been proposed to this purpose with excellent results. However, most of the existing works have given little attention to the role of the teachers. In this article, an open source framework based on a standard intelligent tutoring system is presented. The framework aims at reducing the implementation costs and the complexity of the interfaces, in addition, the framework considers the participation of teachers. The framework was used to create a math course for an elementary school student, and will be used as a basis for the personalization of a Small Private Online Course.

scholarly journals Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems

2021 ◽  
Author(s):  
Ekaterina Kochmar ◽  
Dung Do Vu ◽  
Robert Belfer ◽  
Varun Gupta ◽  
Iulian Vlad Serban ◽  
...  
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Language Processing ◽  
Intelligent Tutoring Systems ◽  
Large Scale ◽  
Intelligent Tutoring ◽  
Performance Outcomes ◽  
Data Driven ◽  
Personalized Feedback ◽  
Tutoring Systems

AbstractIntelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we investigate how feedback in a large-scale ITS can be automatically generated in a data-driven way, and more specifically how personalization of feedback can lead to improvements in student performance outcomes. First, in this paper we propose a machine learning approach to generate personalized feedback in an automated way, which takes individual needs of students into account, while alleviating the need of expert intervention and design of hand-crafted rules. We leverage state-of-the-art machine learning and natural language processing techniques to provide students with personalized feedback using hints and Wikipedia-based explanations. Second, we demonstrate that personalized feedback leads to improved success rates at solving exercises in practice: our personalized feedback model is used in , a large-scale dialogue-based ITS with around 20,000 students launched in 2019. We present the results of experiments with students and show that the automated, data-driven, personalized feedback leads to a significant overall improvement of 22.95% in student performance outcomes and substantial improvements in the subjective evaluation of the feedback.

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.

Artificial Intelligence in Education

2021 ◽  
pp. 256-277
Author(s):  
Rashmi Khazanchi ◽  
Pankaj Khazanchi
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Pedagogical Practices ◽  
Student Centered ◽  
Tutoring Systems ◽  
Artificial Intelligence In Education ◽  
K 12 ◽  
Insight Into

Current educational developments in theories and practices advocate a more personalized, student-centered approach to teach 21st-century skills. However, the existing pedagogical practices cannot provide optimal student engagement as they follow a ‘one size fits all' approach. How can we provide high-quality adaptive instructions at a personalized level? Intelligent tutoring systems with embedded artificial intelligence can assist both students and teachers in providing personalized support. This chapter highlights the role of artificial intelligence in the development of intelligent tutoring systems and how these are providing personalized instructions to students with and without disabilities. This chapter gives insight into the challenges and barriers posed by the integration of intelligent tutoring systems in K-12 classrooms.

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.

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.

Artificial Tutoring Systems: What Computers Can and Can't Know

1997 ◽  
Vol 16 (2) ◽  
pp. 107-124 ◽  
Author(s):  
Theodore W. Frick
Keyword(s):  
Intelligent Tutoring Systems ◽  
Clinical Evidence ◽  
Intelligent Tutoring ◽  
Computer Systems ◽  
Classroom Teachers ◽  
Language Understanding ◽  
Tutoring Systems ◽  
Natural Systems ◽  
Teachers And Students

After more than four decades, development of artificially intelligent tutoring systems has been constrained by two interrelated problems: knowledge representation and natural language understanding. G. S. Maccia's epistemology of intelligent natural systems implies that computer systems will need to develop qualitative intelligence before these problems can be solved. Recent research on how human nervous systems develop provides evidence for the significance of qualitative intelligence. Qualitative intelligence is required for understanding of culturally bound meanings of signs used in communication among intelligent natural systems. S. I. Greenspan provides neurological and clinical evidence that emotion and sensation are vital to the growth of mind—capabilities that computer systems do not currently possess. Therefore, we must view computers in education as media through which a multitude of teachers can convey their messages. This does not mean that the role of classroom teachers is diminished. Teachers and students can be empowered by these additional learning resources.

scholarly journals Multi-Relational and Social-Influence Model for Predicting Student Performance in Intelligent Tutoring Systems (ITS)

2020 ◽  
Vol 9 (3) ◽  
pp. 2058-2066
Keyword(s):  
Student Performance ◽  
Performance Prediction ◽  
Matrix Factorization ◽  
Intelligent Tutoring Systems ◽  
Mean Squared Error ◽  
Mutual Influence ◽  
Intelligent Tutoring ◽  
Essential Elements ◽  
Tutoring Systems ◽  
Predicting Student Performance

Recent studies have shown that Matrix Factorization (MF) method, deriving from recommendation systems, can predict student performance as part of Intelligent Tutoring Systems (ITS). In order to improve the accuracy of this method, we hypothesize that taking into account the mutual influence effect in the relations of student groups would be a major asset. This criterion, coupled with those of the different relationships between the students, the tasks and the skills, would thus be essential elements for a better performance prediction in order to make personalized recommendations in the ITS. This paper proposes an approach for Predicting Student Performance (PSP) that integrates not only friendship relationships such as workgroup relationships, but also mutual influence values into the Weighted Multi-Relational Matrix Factorization method. By applying the Root Mean Squared Error (RMSE) metric to our model, experimental results from KDD Challenge 2010 database show that this approach allows to refine student performance prediction accuracy.

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