A study of e-learning technology integration by preservice science teachers

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dc.contributor.authorOlugbara, Cecilia Temilola
dc.date.accessioned2018-08-01T15:13:35Z
dc.date.available2018-08-01T15:13:35Z
dc.date.issued2017
dc.descriptionA thesis submitted to the Faculty of Education in fulfillment of the requirements for the Degree of Doctor Of Education in Science Education in the Department of Mathematics, Science and Technology Education at the University of Zululand, 2017en_US
dc.description.abstractThis study investigated possible factors predicting e-learning technology integration into the teaching and learning of science subjects by preservice science teachers. An E-learning technology integration model was developed in which factors such as intention (INT), attitude (ATT), Skill (SKL) and Flow Experience (FLW) served as possible precursors of e-learning technology integration. This was done against the gap that continued to exist between intention to integrate e-learning technology and actual integration of e-learning technologies. To close the gap, the study developed a model to predict e-learning technology integration by the research sample. More specifically, the model hypothesised that quality consciousness and innovation consciousness moderated the intention-integration gap. The proposed model was first pilot-tested on a sample of 30 preservice science teachers (PSSTs) before it was applied to the main study, which comprised a research sample of 100 final year PSSTs at the University of Zululand, KwaZulu-Natal Province, South Africa. The study was located within the mixed-methods research paradigm, based on a survey research design. Data collection was carried out using a semi-structured questionnaire which allowed for the collection of both quantitative and qualitative data. Quantitative data were analysed using the Partial Least Squares (PLS) Structural Equation Modelling (SEM), while qualitative data were analysed using a hermeneutic content analysis approach. The results of the study were, firstly, that the proposed model explained 44% of the PSSTs integration of e-learning technologies into the teaching and learning of science subjects and that skill was the most significant and strongest factor predicting the PSSTs integration of e-learning technologies; flow experience was the second important factor predicting the PSSTs integration of e-learning technologies, followed by intention and lastly, attitude. Secondly, the study revealed that quality consciousness and innovation consciousness significantly moderated the gap between intention to integrate e-learning technologies and the actual integration of e-learning technologies, with quality consciousness having the stronger moderating effect. Thirdly, the study revealed that some preservice science teachers were able to utilise e-learning technologies during the period of teaching practice for instructional preparation, instructional delivery, and to facilitate learning. However, some PSSTs were unable to utilised e-learning technologies during teaching practice, ostensibly because of a lack of e-learning facilities in the schools. Some recommendations are made based on the findings of the study. These relate to the management of e-learning at the university, schools and implications for policy.en_US
dc.identifier.urihttps://hdl.handle.net/10530/1659
dc.publisherUniversity of Zululanden_US
dc.subjecte-learning technology --pedagogy --science --preservice --South Africaen_US
dc.titleA study of e-learning technology integration by preservice science teachersen_US
dc.typeThesisen_US
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