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Examining the effect of time constraint on the online mastery learning approach towards improving postgraduate students' achievement

Dados Bibliográficos

ID14690339
AutoresMong Shan Ee (0000-0001-8366-1098, Department of Finance, Deakin Business School, Deakin University, Geelong, VIC, Australia, autor correspondente), William Yeoh (0000-0002-2964-4518, Deakin University), Yee Ling Boo (0000-0001-7188-9597, RMIT University), Terry Boulter (RMIT University)
Ano2018
Volume43
Fascículo2
Páginas217-233
Data de publicação2018-02-01
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoStudies in Higher Education (JOURNAL)
Identificadores do periódicoISSN: 0307-5079 • E-ISSN: 1470-174X
EditoraInforma UK Limited (PUBLISHER • GB)
DOI10.1080/03075079.2016.1161611
OpenAlexW2332451575
IdiomaEN
Citações recebidas1
Referências citadas39

Time control plays a critical role within the online mastery learning (OML) approach. This paper examines the two commonly implemented mastery learning strategies – personalised system of instructions and learning for mastery (LFM) – by focusing on what occurs when there is an instructional time constraint. Using a large data set from a postgraduate finance course offered at an Australian university, we explore students' online quiz-completion patterns, then empirically investigate whether the imposition of an instructional time constraint in the OML approach has an impact on their final-examination performance. Our results suggest that the LFM strategy with an instructional time constraint has a positive impact on students' learning behaviour and contributes to better overall academic performance. Further, our findings suggest that facilitators should be encouraged to implement an instructional time constraint when adopting an OML approach

Academic achievement · Higher education · Instructional design · Mastery Learning · Mathematics education · Multimedia · Online learning · Time constraint · Time management · Computer Science · Innovative Teaching and Learning Methods · Intelligent Tutoring Systems and Adaptive Learning · Mathematics · Online Learning and Analytics · Psychology · Artificial Intelligence

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Obras citantes distintas1
Citações por ano0,33
Intervalo de citações2023 - 2023 (1)
Velocidade de citaçãohistorical
Altamente citadoNão
Tipos de citaçãoNeutras: 1
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