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An examination of the interrelationships between workload, study time, learning approaches and academic outcomes

Datos Bibliográficos

ID4097422
AutoresDavid Kember (0000-0002-1134-3741, Hong Kong Polytechnic University, autor de correspondencia), Sandra Ng (Hong Kong Polytechnic University), Harrison Tse (Hong Kong Polytechnic University), Eric T T Wong, T T Wong (0000-0001-5382-6023, Hong Kong Polytechnic University), Mike Pomfret, M J Pomfret (Tuen Mun Hospital)
Año1996
Volumen21
Número3
Páginas347-358
Fecha de publicación1996-01-01
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaStudies in Higher Education (JOURNAL)
Identificadores de la revistaISSN: 0307-5079 • E-ISSN: 1470-174X
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/03075079612331381261
OpenAlexW2153587662
IdiomaEN
Citas recibidas13
Referencias citadas18

Interrelationships between learning approaches, study motivation, hours of study, perceived workload, English language ability and grade point average were investigated. A total of 174 students, who had enrolled in a mechanical engineering degree course, completed a study diary for 1 week. The diary was divided into hourly recording periods with spaces for both quantitative and qualitative responses. The quantitative data yielded variables for learning approaches and motivations, English ability, perceived workload, time spent in class, time spent on private study and grade point average. These variables were linked in a path model. Case-studies were used to illustrate and explain the model. The variables in the model were weakly interrelated. Perceived workload, therefore, depends upon motivation as well as the amount of work. Grade point average did improve somewhat with hours of work, but students could work long hours and still obtain poor grades because they used inappropriate learning approaches

Machine learning · Mathematics education · Workload · Computer Science · Engineering · Evaluation of Teaching Practices · Innovative Teaching and Learning Methods · Learning Styles and Cognitive Differences · Mathematics · Psychology · Social Psychology · Artificial Intelligence

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Obras citantes distintas13
Citas por año0,54
Intervalo de citas2002 - 2025 (24)
Velocidad de citaciónrecent
Altamente citadoNo
Tipos de citaNeutras: 12
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