Computer-Based Learning Environment and Automatic Diagnosis System for Superposition of Motion

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.

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Affective Computational Model to Extract Natural Affective States of Students With Asperger Syndrome (AS) in Computer-Based Learning Environment

IEEE Access ◽

10.1109/access.2018.2879619 ◽

2018 ◽

Vol 6 ◽

pp. 67026-67034 ◽

Cited By ~ 5

Author(s):

Amina Dawood ◽

Scott Turner ◽

Prithvi Perepa

Keyword(s):

Learning Environment ◽

Asperger Syndrome ◽

Computational Model ◽

Affective States ◽

Computer Based Learning ◽

Computer Based

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

Computers & Education ◽

10.1016/j.compedu.2020.103816 ◽

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

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

ReCALL ◽

10.1017/s0958344000000318 ◽

2000 ◽

Vol 12 (1) ◽

pp. 19-26 ◽

Cited By ~ 1

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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