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.

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.

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.

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.

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.

Workshop on Dialog-Based Intelligent Tutoring Systems: State of the Art and New Research Directions

2004 ◽  
pp. 914-914
Author(s):  
Neil Heffernan (Co-chair) ◽  
Peter Wiemer-Hastings (Co-chair) ◽  
Greg Aist ◽  
Vincent Aleven ◽  
Ivon Arroyo ◽  
...  
Keyword(s):  
Intelligent Tutoring Systems ◽  
State Of The Art ◽  
Intelligent Tutoring ◽  
Research Directions ◽  
Tutoring Systems ◽  
New Research

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.

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