Virtual Manipulatives in a Computer-Based Learning Environment: How Experimental Data Informs the Design of Future Systems

2011 ◽  
pp. 510-512
Author(s):  
Maria Mendiburo ◽  
Gautam Biswas
Keyword(s):  
Experimental Data ◽  
Learning Environment ◽  
Virtual Manipulatives ◽  
Computer Based Learning ◽  
Computer Based

Estimating student ability and problem difficulty using item response theory (IRT) and TrueSkill

2019 ◽  
Vol 47 (2) ◽  
pp. 67-75 ◽  
Author(s):  
Youngjin Lee
Keyword(s):  
Data Mining ◽  
Item Response Theory ◽  
Problem Solving ◽  
Learning Environment ◽  
Item Response ◽  
Predictive Power ◽  
Response Theory ◽  
Content Type ◽  
Computer Based Learning ◽  
Computer Based

Purpose The purpose of this paper is to investigate an efficient means of estimating the ability of students solving problems in the computer-based learning environment. Design/methodology/approach Item response theory (IRT) and TrueSkill were applied to simulated and real problem solving data to estimate the ability of students solving homework problems in the massive open online course (MOOC). Based on the estimated ability, data mining models predicting whether students can correctly solve homework and quiz problems in the MOOC were developed. The predictive power of IRT- and TrueSkill-based data mining models was compared in terms of Area Under the receiver operating characteristic Curve. Findings The correlation between students’ ability estimated from IRT and TrueSkill was strong. In addition, IRT- and TrueSkill-based data mining models showed a comparable predictive power when the data included a large number of students. While IRT failed to estimate students’ ability and could not predict their problem solving performance when the data included a small number of students, TrueSkill did not experience such problems. Originality/value Estimating students’ ability is critical to determine the most appropriate time for providing instructional scaffolding in the computer-based learning environment. The findings of this study suggest that TrueSkill can be an efficient means for estimating the ability of students solving problems in the computer-based learning environment regardless of the number of students.

Effects of a computer-based learning environment that teaches older adults how to install a smart home system

2020 ◽  
Vol 149 ◽  
pp. 103816
Author(s):  
Yang Hu ◽  
Rachel Min Wong ◽  
Olusola Adesope ◽  
Matthew E. Taylor
Keyword(s):  
Older Adults ◽  
Learning Environment ◽  
Smart Home ◽  
Computer Based Learning ◽  
Computer Based

Towards a computer-based learning environment: a pilot study in the use of FirstClass

ReCALL ◽  
2000 ◽  
Vol 12 (1) ◽  
pp. 19-26 ◽  
Author(s):  
JOHN GILLESPIE
Keyword(s):  
Pilot Study ◽  
Learning Environment ◽  
Computer Conferencing ◽  
Computer Based Learning ◽  
Wide Range ◽  
Computer Based ◽  
Student Reactions ◽  
The University ◽  
Further Development

This article outlines the nature of the FirstClass computer conferencing program and considers its effectiveness in helping to develop a computer-based learning environment in modern languages at the University of Ulster. A wide range of different transactions is outlined and surveys of staff and student reactions are assessed. Their positive responses demonstrate that FirstClass is effective in establishing such an environment, enable us to draw encouraging lessons from our experience and indicate exciting possibilities of immediate further development.

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