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.

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.

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.

Implementing Multiple Tutoring Strategies in an Intelligent Tutoring System for Music Learning

1995 ◽  
Vol 10 (1) ◽  
pp. 52-62
Author(s):  
Marios C. Angelides ◽  
Amelia K.Y. Tong
Keyword(s):  
Music Theory ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Individual Student ◽  
Tutoring System ◽  
Music Learning ◽  
Tutoring Systems

Variation in tutoring strategies plays an important part in intelligent tutoring systems. The potential for providing an adaptive intelligent tutoring system depends on having a range of tutoring strategies to select from. In order to react effectively to the student's needs, an intelligent tutoring system has to be able to choose intelligently among the strategies and determine which strategy is best for an individual student at a particular moment. This paper describes, through the discussion pertaining to the implementation of SONATA, a music theory tutoring system, how an intelligent tutoring system can be developed to support multiple tutoring strategies during the course of interaction. SONATA has been implemented using a hypertext tool, HyperCard II. 1.

AutoTutor

2012 ◽  
pp. 169-187 ◽  
Author(s):  
Arthur C. Graesser ◽  
Sidney D’Mello ◽  
Xiangen Hu ◽  
Zhiqiang Cai ◽  
Andrew Olney ◽  
...  
Keyword(s):  
Learning Environments ◽  
Speech Act ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Conversational Agent ◽  
Learning Gains ◽  
Tutoring System ◽  
Agent Based ◽  
Electronic Documents ◽  
Dialogue Management

AutoTutor is an intelligent tutoring system that helps students learn science, technology, and other technical subject matters by holding conversations with the student in natural language. AutoTutor’s dialogues are organized around difficult questions and problems that require reasoning and explanations in the answers. The major components of AutoTutor include an animated conversational agent, dialogue management, speech act classification, a curriculum script, semantic evaluation of student contributions, and electronic documents (e.g., textbook and glossary). This chapter describes the computational components of AutoTutor, the similarity of these components to human tutors, and some challenges in handling smooth dialogue. We describe some ways that AutoTutor has been evaluated with respect to learning gains, conversation quality, and learner impressions. AutoTutor is sufficiently modular that the content and dialogue mechanisms can be modified with authoring tools. AutoTutor has spawned a number of other agent-based learning environments, such as AutoTutor-lite, Operation Aries!, and Guru.

Student Modeling in an Intelligent Tutoring System

2011 ◽  
pp. 208-236 ◽  
Author(s):  
Mingyu Feng ◽  
Neil Heffernan ◽  
Kenneth Koedinger
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Student Modeling ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Fine Grained ◽  
Knowledge States ◽  
Complex Models ◽  
Cognitively Diagnostic

Student modeling and cognitively diagnostic assessment are important issues that need to be addressed for the development and successful application of intelligent tutoring systems (its). Its needs the construction of complex models to represent the skills that students are using and their knowledge states, and practitioners want cognitively diagnostic information at a finer grained level. This chapter reviews our effort on modeling student’s knowledge in the ASSISTment project. Intelligent tutors have been mainly used to teach students. In the ASSISTment project, we have emphasized using the intelligent tutoring system as an assessment system that provides instructional assistance during the test. Usually it is believed that assessment get harder if students are allowed to learn during the test, as its then like try to hit a moving target. So our results are surprising that by providing tutoring to students while they are assessed we actually prove the assessment of students’ knowledge. Additionally, in this article, we present encouraging results about a fine-grained skill model with that system that is able to predict state test scores. We conclude that using intelligent tutoring systems to do assessment seems like a reasonable way of dealing with the dilemma that every minute spent testing students takes time away from instruction.

Text-Based Affect Detection in Intelligent Tutors

2012 ◽  
pp. 284-304
Author(s):  
Sidney D’Mello ◽  
Arthur C. Graesser
Keyword(s):  
Learning Environments ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Affective States ◽  
Learning Gains ◽  
Tutoring Systems ◽  
Detection Systems ◽  
Intelligent Tutors ◽  
Cognitive States ◽  
Affect Detection

Affect-sensitive Intelligent Tutoring Systems are an exciting new educational technology that aspire to heighten motivation and enhance learning gains in interventions that are dynamically adaptive to learners’ affective and cognitive states. Although state of the art affect detection systems rely on behavioral and physiological measures for affect detection, we show that a textual analysis of the tutorial discourse provides important cues into learners’ affective states. This chapter surveys the existing literature on text-based affect sensing and focuses on how learners’ affective states (boredom, flow/engagement, confusion, and frustration) can be automatically predicted by variations in the cohesiveness of tutorial dialogues during interactions with AutoTutor, an intelligent tutoring system with conversational dialogues. The authors discuss the generalizability of findings to other domains and tutoring systems, the possibility of constructing real-time cohesion-based affect detectors, and implications for text-based affect detection for the next generation affect-sensitive learning environments.

Designing an Intelligent Tutoring System Interface to Account for Personality Differences

1994 ◽  
Vol 38 (13) ◽  
pp. 789-792
Author(s):  
Pauline K. Cushman
Keyword(s):  
Subject Matter ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Personality Differences ◽  
The Way

Intelligent Tutoring Systems have been designed for a variety of purposes. Much of the design effort has been aimed at the actual subject matter. Often ignored has been the critical nature of the interface. If the way people interact with computers is directly related to their personality, then systems should respond differently to different people. This paper describes the design of an interface for an Intelligent Tutoring System that, given the student's personality, will make adjustments in the style of interaction.

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.

scholarly journals Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Hsuan-Ta Lin ◽  
Po-Ming Lee ◽  
Tzu-Chien Hsiao
Keyword(s):  
Online Learning ◽  
Reinforcement Learning ◽  
Real World ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Rule Discovery ◽  
Learning Gains ◽  
Tutoring System

Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in students’ learning gains. However, the Reinforcement Learning (RL) techniques that were used in previous studies to induce tutorial tactics are insufficient when encountering large problems and hence were used in offline manners. Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones. This increases the scalability of a RL learner for larger problems. The results support our hypothesis about the capability of the GBML method to induce tutorial tactics. This suggests that the GBML method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction.

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