Adaptation of tutoring to students' emotions in emotionally intelligent tutoring systems

2013 ◽  
Author(s):  
Sintija Petrovica
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Tutoring Systems ◽  
Emotionally Intelligent

Tutoring Process in Emotionally Intelligent Tutoring Systems

2016 ◽  
pp. 1094-1110 ◽  
Author(s):  
Sintija Petrovica
Keyword(s):  
Intelligent Tutoring Systems ◽  
Emotional State ◽  
Intelligent Tutoring ◽  
Point Of View ◽  
Research Groups ◽  
Tutoring Systems ◽  
Knowledge States ◽  
Close Relationship ◽  
Emotionally Intelligent ◽  
Selection Of

Research has shown that emotions can influence learning in situations when students have to analyze, reason, make conclusions, apply acquired knowledge, answer questions, solve tasks, and provide explanations. A number of research groups inspired by the close relationship between emotions and learning have been working to develop emotionally intelligent tutoring systems. Despite the research carried out so far, a problem how to adapt tutoring not only to a student's knowledge state but also to his/her emotional state has been disregarded. The paper aims to examine to what extent the tutoring process and tutoring strategies are adapted to students' emotional and knowledge states in these systems. It also presents a study on how to influence student's emotions looking from the pedagogical point of view and provides general guidelines for selection of tutoring strategies to influence and regulate student's emotions.

Tutoring Process in Emotionally Intelligent Tutoring Systems

2014 ◽  
Vol 4 (1) ◽  
pp. 72-85 ◽  
Author(s):  
Sintija Petrovica
Keyword(s):  
Intelligent Tutoring Systems ◽  
Emotional State ◽  
Intelligent Tutoring ◽  
Point Of View ◽  
Research Groups ◽  
Tutoring Systems ◽  
Knowledge States ◽  
Close Relationship ◽  
Emotionally Intelligent ◽  
Selection Of

Research has shown that emotions can influence learning in situations when students have to analyze, reason, make conclusions, apply acquired knowledge, answer questions, solve tasks, and provide explanations. A number of research groups inspired by the close relationship between emotions and learning have been working to develop emotionally intelligent tutoring systems. Despite the research carried out so far, a problem how to adapt tutoring not only to a student's knowledge state but also to his/her emotional state has been disregarded. The paper aims to examine to what extent the tutoring process and tutoring strategies are adapted to students' emotional and knowledge states in these systems. It also presents a study on how to influence student's emotions looking from the pedagogical point of view and provides general guidelines for selection of tutoring strategies to influence and regulate student's emotions.

scholarly journals DESIGN OF THE PEDAGOGICAL MODULE FOR AN EMOTIONALLY INTELLIGENT TUTORING SYSTEM / PEDAGOGINIO MODULIO EMOCIJAS SUPRANTANČIAI MOKYMO SISTEMAI KŪRIMAS

2014 ◽  
Vol 6 (2) ◽  
pp. 138-146 ◽  
Author(s):  
Sintija Petrovica
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Point Of View ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Emotional Component ◽  
Human Teachers ◽  
New Generation ◽  
Emotionally Intelligent

Since 1970-ties the research is being carried out for the development of intelligent tutoring systems (ITS) that aretrying to imitate human-teachers and their teaching methods. However, over the last decade researchers inspired by the closerelationship between emotions and learning have been working on the addition of an emotional component to human-computerinteraction. This has led to creation of a new generation of intelligent tutoring systems – emotionally intelligent tutoring systems(EITS). Despite the research carried out so far, a problem how to adapt tutoring not only to a student’s knowledge state butalso to his/her emotional state has been disregarded. The paper presents study on how to use the determined student’s emotionalstate further in order to change behaviour of the intelligent tutoring system looking from the pedagogical point of view and toimplement this as a part of the pedagogical module. The architecture of the planned tutoring system that adapts the tutoring bothto student’s emotions and knowledge is also described in the paper. Straipsnyje nagrinėjami klausimai, susiję su informacijos apienustatytą studento emocinę būklę taikymu sumaniosios mokymosistemos elgsenai keisti, taip pat emocinės būklės poveikis mokymoprocesui pedagoginiu požiūriu. Siūlomas pedagoginiamsaspektams įgyvendinti specializuotas informacinės sistemosmodulis. Parodoma pedagoginio modulio vieta sumaniosiosmokymo sistemos, pritaikančios mokymo procesą konkretausstudento žinių ir emociniam lygmenims, architektūroje.

Intelligent tutoring systems for aviation automation

2000 ◽  
Author(s):  
Christine Mitchel ◽  
Alan Chappell ◽  
W. Gray ◽  
Alex Quinn ◽  
David Thurman
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Tutoring Systems

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.

Sign in / Sign up

Export Citation Format

Share Document