scholarly journals Improving prediction of students’ performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources

2021 ◽  
Author(s):  
Wilson Chango ◽  
Rebeca Cerezo ◽  
Miguel Sanchez-Santillan ◽  
Roger Azevedo ◽  
Cristóbal Romero
Keyword(s):  
Learning Strategies ◽  
Domain Knowledge ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Numerical Data ◽  
Attribute Selection ◽  
Learning Performance ◽  
Multimodal Data ◽  
Tutoring Systems ◽  
System Logs

AbstractThe aim of this study was to predict university students’ learning performance using different sources of performance and multimodal data from an Intelligent Tutoring System. We collected and preprocessed data from 40 students from different multimodal sources: learning strategies from system logs, emotions from videos of facial expressions, allocation and fixations of attention from eye tracking, and performance on posttests of domain knowledge. Our objective was to test whether the prediction could be improved by using attribute selection and classification ensembles. We carried out three experiments by applying six classification algorithms to numerical and discretized preprocessed multimodal data. The results show that the best predictions were produced using ensembles and selecting the best attributes approach with numerical data.

scholarly journals Improving Comprehension: Intelligent Tutoring System Explaining the Domain Rules When Students Break Them

2021 ◽  
Vol 11 (11) ◽  
pp. 719
Author(s):  
Oleg Sychev ◽  
Nikita Penskoy ◽  
Anton Anikin ◽  
Mikhail Denisov ◽  
Artem Prokudin
Keyword(s):  
Learning Strategies ◽  
Cognitive Skills ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Control Flow ◽  
Subject Domain ◽  
Learning Gains ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Subject Domains

Intelligent tutoring systems have become increasingly common in assisting students but are often aimed at isolated subject-domain tasks without creating a scaffolding system from lower- to higher-level cognitive skills, with low-level skills often neglected. We designed and developed an intelligent tutoring system, CompPrehension, which aims to improve the comprehension level of Bloom’s taxonomy. The system features plug-in-based architecture, easily adding new subject domains and learning strategies. It uses formal models and software reasoners to solve the problems and judge the answers, and generates explanatory feedback about the broken domain rules and follow-up questions to stimulate the students’ thinking. We developed two subject domain models: an Expressions domain for teaching the expression order of evaluation, and a Control Flow Statements domain for code-tracing tasks. The chief novelty of our research is that the developed models are capable of automatic problem classification, determining the knowledge required to solve them and so the pedagogical conditions to use the problem without human participation. More than 100 undergraduate first-year Computer Science students took part in evaluating the system. The results in both subject domains show medium but statistically significant learning gains after using the system for a few days; students with worse previous knowledge gained more. In the Control Flow Statements domain, the number of completed questions correlates positively with the post-test grades and learning gains. The students’ survey showed a slightly positive perception of the system.

ANN based performance measure of students in learning a course work

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Bhavtosh Mishra ◽  
Kiran Mishra
Keyword(s):  
Domain Knowledge ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Performance Measure ◽  
Interactive System ◽  
Tutoring System ◽  
Design And Development ◽  
Tutoring Systems ◽  
Course Work

Intelligent Tutoring System (ITS) is concerned with the design and development of an automatic and interactive system which can communicate among different entities such as student, pedagogue and subject as well as compute the various parameters which govern the dynamics of leaning through the interaction of pedagogue and domain knowledge. Classical methods have been developed which use mathematics and heuristics for the design of such tutoring systems

scholarly journals DomainBuilder: the knowledge authoring system for SlideTutor Intelligent Tutoring system

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1721
Author(s):  
Eugene Tseytlin ◽  
Faina Linkov ◽  
Melissa Castine ◽  
Elizabeth Legowski ◽  
Rebecca S. Jacobson
Keyword(s):  
Language Processing ◽  
Domain Knowledge ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Software Systems ◽  
Efficient Manner ◽  
Tutoring Systems ◽  
Content Creation ◽  
Study Participants ◽  
Feedback Module

One of the major challenges in the development of medical Intelligent Tutoring Systems (ITS) is the development of authored content, a time-consuming process that requires participation of discipline experts. In this publication, we describe the development of software systems called DomainBuilder and TutorBuilder, designed to streamline and simplify the authoring process for general medical ITSs. The aim of these systems is to allow physicians without programming or ITSs background to create a domain knowledge base and author tutor cases in a time efficient manner.  DomainBuilder combined knowledge authoring, case authoring, and validation tasks into a single work environment, enabling multiple authoring strategies. Natural Language Processing (NLP) methods were integrated for parsing existing clinical reports to speed case authoring. Similarly, TutorBuilder was designed to allow users to customize all aspects of ITSs, including user interface, pedagogic module, feedback module, etc. Both systems underwent formal usability studies with physicians specializing in dermatology. Open-ended questions assessed usability of the system and satisfaction with its features. Incorporating feedback from usability studies, DomainBuilder and TutorBuilder systems were deployed and used across multiple universities to create customized medical tutoring curriculum. Overall, both systems were well received by medical professionals participating in usability studies with participants highlighting ease of utilization and clarity of presentation. Usability study participants were able to successfully use the system for the authoring tasks. DomainBuilder and TutorBuilder are novel tools that combine comprehensive aspects of content creation, including creation of domain ontologies, case authoring, and validation.

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.

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.

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.

Intelligent tutoring systems and learning performance

2019 ◽  
Vol 43 (4) ◽  
pp. 600-616 ◽  
Author(s):  
Ali Yuce ◽  
A. Mohammed Abubakar ◽  
Mustafa Ilkan
Keyword(s):  
Student Satisfaction ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Knowledge System ◽  
Learning Performance ◽  
Equation Modeling ◽  
Content Type ◽  
Tutoring Systems ◽  
Constructive Feedback ◽  
The Impact

Purpose Intelligent tutoring systems (ITS) are a supplemental educational tool that offers great benefits to students and teachers. The systems are designed to focus on an individual’s characteristics, needs and preferences in an effort to improve student outcomes. Despite the potential benefits of such systems, little work has been done to investigate the impact of ITS on users. To provide a more nuanced understanding of the effectiveness of ITS, the purpose of this paper is to explore the role of several ITS parameters (i.e. knowledge, system, service quality and task–technology fit (TTF)) in motivating, satisfying and helping students to improve their learning performance. Design/methodology/approach Data were obtained from students who used ITS, and a structural equation modeling was deployed to analyze the data. Findings Data analysis revealed that the quality of knowledge, system and service directly impacted satisfaction and improved TTF for ITS. It was found that TTF and student satisfaction with ITS did not generate higher learning performance. However, student satisfaction with ITS did improve learning motivation and resulted in superior learning performance. Data suggest this is due to students receiving constant and constructive feedback while simultaneously collaborating with their peers and teachers. Originality/value This study verifies that there was a need to assess the benefits of ITS. Based on the study’s findings, theoretical and practical implications are proposed.

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