scholarly journals Effectiveness of a numeracy intelligent tutoring system in kindergarten: A conceptual replication

2021 ◽  
Vol 7 (3) ◽  
pp. 388-410 ◽  
Author(s):  
Ka Rene Grimes ◽  
Soyoung Park ◽  
Amanda McClelland ◽  
Jiyeon Park ◽  
Young Ri Lee ◽  
...  
Keyword(s):  
Intrinsic Motivation ◽  
Repeated Measures ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Original Study ◽  
Future Research ◽  
Control Group ◽  
Mathematical Language ◽  
Tutoring System ◽  
Tutoring Systems

Intelligent Tutoring Systems are a genre of highly adaptive software providing individualized instruction. The current study was a conceptual replication of a previous randomized control trial that incorporated the intelligent tutoring system Native Numbers, a program designed for early numeracy instruction. As a conceptual replication, we kept the method of instruction, the demographics, the number of kindergarten classrooms (n = 3), and the same numeracy and intrinsic motivation screeners as the original study. We changed the time of year of instruction, changed the control group to a wait-control group, added a maintenance assessment for the first group of participants, and included a mathematical language assessment. Analysis of within- and between-group differences using repeated measures ANOVA indicated gains of numeracy were significant only after using Native Numbers (Partial Eta Square = 0.147). Results of intrinsic motivation and mathematical language were not significant. The effect size of numeracy achievement did not reach that of the original study (Partial Eta Square = 0.622). Here, we compared the two studies, discussed plausible reasons for differences in the magnitude of effect sizes, and provided suggestions for future research.

scholarly journals Effectiveness of a Numeracy Intelligent Tutoring System in Kindergarten: A Conceptual Replication

2020 ◽  
Author(s):  
K. Rene Grimes ◽  
Soyoung Park ◽  
Amanda McClelland ◽  
Jiyeon Park ◽  
Young Ri Lee ◽  
...  
Keyword(s):  
Intrinsic Motivation ◽  
Repeated Measures ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Original Study ◽  
Future Research ◽  
Control Group ◽  
Mathematical Language ◽  
Tutoring System ◽  
Tutoring Systems

Intelligent Tutoring Systems are a genre of highly adaptive software providing individualized instruction. The current study was a conceptual replication of a previous randomized control trial that incorporated the intelligent tutoring system Native Numbers, a program designed for early numeracy instruction. As a conceptual replication, we kept the method of instruction, the demographics, the number of kindergarten classrooms (n = 3), and the same numeracy and intrinsic motivation screeners as the original study. We changed the time of year of instruction, changed the control group to a wait-control group, added a maintenance assessment for the first group of participants, and included a mathematical language assessment. Analysis of within- and between-group differences using repeated measures ANOVA indicated gains of numeracy were significant only after using Native Numbers (η_p^2 = .147). Results of intrinsic motivation and mathematical language were not significant. The effect size of numeracy achievement did not reach that of the original study (η_p^2 = .622). Here, we compared the two studies, discussed plausible reasons for differences in the magnitude of effect sizes, and provided suggestions for future research.

Online Homework Managers and Intelligent Tutoring Systems: A Study of Their Impact on Student Learning in the Introductory Financial Accounting Classroom

2013 ◽  
Vol 28 (3) ◽  
pp. 513-535 ◽  
Author(s):  
William Hahn ◽  
Chris Fairchild ◽  
William B. Dowis
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Financial Accounting ◽  
Future Research ◽  
Control Group ◽  
Online Homework ◽  
Research Directions ◽  
Tutoring Systems ◽  
Future Research Directions

ABSTRACT: The online homework manager (OHM) and the intelligent tutoring system (ITS) are two supplemental teaching tools available for accounting educators' use in the introductory financial accounting course. While research related to these systems is limited, prior studies find a tenuous performance advantage related to their use. To advance the literature in this area, this paper evaluates the performance benefit related to an OHM and an ITS, each employed independently as an additional study aid during the first course unit in one of two sections of the introductory financial accounting course. A third section used paper-and-pencil only and served as a control group. Results of tests on several performance measures did not identify a learning advantage associated with either the OHM or the ITS. Nor was a learning advantage identified when this study's results were compared to exam results from 14 previous semesters. Implications for accounting educators and future research directions are discussed.

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.

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.

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.

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.

Online Homework versus Intelligent Tutoring Systems: Pedagogical Support for Transaction Analysis and Recording

2011 ◽  
Vol 26 (1) ◽  
pp. 87-97 ◽  
Author(s):  
Fred Phillips ◽  
Benny G. Johnson
Keyword(s):  
Education Research ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Accounting Education ◽  
Online Homework ◽  
Learning Effects ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Transaction Analysis

ABSTRACT: Prior research demonstrates that students learn more from homework practice when using online homework or intelligent tutoring systems than a paper-and-pencil format. However, no accounting education research directly compares the learning effects of online homework systems with the learning effects of intelligent tutoring systems. This paper presents a quasi-experiment that compares the two systems and finds that students’ transaction analysis performance increased at a significantly faster rate when they used an intelligent tutoring system rather than an online homework system. Implications for accounting instructors and researchers are discussed.

MAKING DECISIONS IN AN INTELLIGENT TUTORING SYSTEM

2005 ◽  
Vol 04 (02) ◽  
pp. 207-233 ◽  
Author(s):  
PENG-KIAT PEK ◽  
KIM-LENG POH
Keyword(s):  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Learning Objectives ◽  
Tutoring System ◽  
Web Based ◽  
Tutoring Systems ◽  
Test Items ◽  
Optimal Policies ◽  
Separability Condition ◽  
Engineering Mechanics

In computerized tutoring, the pace of instruction is related to the student's mastery levels of the learning objectives. The observable student's behavior that can be used to measure his knowledge is usually his responses to test items. Unobservable variables that are related to learner's motivation can affect learning but are difficult to quantify. In comparison with other decision-theoretic tutoring systems, the novelties of this research are: (1) the efficiency-centric approach to develop the Bayesian networks; (2) the formulation of utility values for different tutoring outcomes that are independent of past actions and to satisfy the separability condition; (3) the development of a common measure for student's mastery levels and item difficulties; and (4) the generation of optimal policies in polynomial time. A prototype web-based tutoring system, known as iTutor, incorporating the novelties has been developed for engineering mechanics. Formative evaluations of iTutor have shown encouraging results.

scholarly journals Advances in Intelligent Tutoring Systems: Problem-solving Modes and Model of Hints

2007 ◽  
Vol 2 (1) ◽  
pp. 48 ◽  
Author(s):  
Alla Anohina
Keyword(s):  
Problem Solving ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Tutoring System ◽  
Layer Model ◽  
Tutoring Systems ◽  
Adaptive Support ◽  
Two Layer Model ◽  
Adaptive Abilities

The paper focuses on the issues of providing an adaptive support for learners in intelligent tutoring systems when learners solve practical problems. The results of the analysis of support policies of learners in the existing intelligent tutoring systems are given and the revealed problems are emphasized. The concept and the architectural parts of an intelligent tutoring system are defined. The approach which provides greater adaptive abilities of systems of such kind offering two modes of problem-solving and using a two-layer model of hints is described. It is being implemented in the intelligent tutoring system for the Minimax algorithm at present. In accordance with the proposed approach the learner solves problems in the mode which is the most appropriate for him/her and receives the most suitable hint.

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