The impact of learner characteristics on training transfer expectation: a survey of Thai teachers’ perception of cloud computing tools

Celestin, Bekolo Ngoa; Shao, Yufeng

doi:10.1111/ijtd.12137

Cited by 13 publications

(14 citation statements)

References 53 publications

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“…training design) or individual variables (e.g. learner characteristics) when studying self‐regulated learning in the workplace (Celestin & Yunfei, ). For instance, training design has been contemplated in many learning transfer theories (Baldwin & Ford, ; Holton et al , ) and has been shown to play an important role in promoting transfer motivation (Velada et al , ).…”

Section: Discussionmentioning

confidence: 99%

Self‐regulated learning and training effectiveness

Lourenco

Ferreira

2019

Int J Training Development

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In recent years, training has shifted towards a more learner‐centred perspective. At the same time, rapidly changing workplaces emphasize the importance of self‐regulatory processes in the workplace. In the light of these trends, this research investigates the role of self‐regulated learning in training effectiveness. A theoretical model in which self‐regulated learning plays a central role in training evaluation was developed and validated using the structural equation method with a sample of 137 individuals. The results indicate that self‐regulated learning mediates the relationship between training climate and both transfer motivation and training evaluation. These findings indicate that the concept of self‐regulated learning plays a key role in the relationship between training context variables and organizational outcomes and offer new insights into the way organizations can improve training effectiveness.

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Section: Discussionmentioning

confidence: 99%

Self‐regulated learning and training effectiveness

Lourenco

Ferreira

2019

Int J Training Development

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“…For example, in the Spanish context (Renta-Davids et al , 2014), it was found that insofar as the training courses were related to the workplace activities and the professional development of the participants, they positively influence the transfer of training. In the educational context, the work of Celestin and Yunfei (2018) showed that the personal characteristics of a teacher (preparation for learning, effectiveness of personal transfer, motivation for transfer, personal capacity and perceived validity of content) in the pre-training stage are significant predictors of the performance expectation of the transfer effort in the post-training stage. Additionally, whether other variables of a qualitative nature moderate the relationships proposed in the model could be analysed – for instance, whether there are differences that depend on the size of the schools, the level of education offered at the school or the location of the school (Lazarenko and Shamarica, 2019).…”

Section: Limitations and Future Researchmentioning

confidence: 99%

Towards an analysis of the transfer of training: empirical evidence from schools in Spain

Gil

Mataveli

García-Alcaráz

2021

EJTD

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Purpose The transfer of training has been identified with the effectiveness of training. The purpose of this work is to analyse the impact of training stages (training needs analysis, application and evaluation) as they relate to training transfer. Design/methodology/approach The study participants correspond to a sample of 116 teachers with managerial responsibilities (management teams and department heads) from 17 secondary schools in Spain. This work hypothesises five significant relationships: needs analysis and application of training, application and transfer of training, evaluation and analysis of training needs, evaluation and application of training and evaluation and transfer of training. The hypotheses were tested using structural equations, namely, the partial least squares–structural equation modelling technique and SmartPLS version 3.2.9. Findings The relationships between application and transfer of training, between evaluation of training and needs analysis, application and transfer of training are positively contrasted. The relationship between the analysis and application of training is not contrasted. Originality/value This work analyses the relationship between the training process’s different phases (analysis, application and evaluation) in the training output (training transfer) and emphasises the role of evaluation in the training process.

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“…Motivation to transfer, one of the core LTSI constructs, is “the trainees’ desire to use the knowledge and skills mastered in the training program on the job” (Noe, 1986 , p. 743) and is considered to be a key factor for the transfer of training (Holton et al, 2000 ; Burke & Hutchins, 2007 ; Gegenfurtner et al, 2009 ; Massenberg et al, 2017 ). Motivation to transfer plays the mediating role between other LTSI factors and actual learning transfer (Gegenfurtner et al, 2009 ; Hutchins et al, 2013 ; Nafukho et al, 2017 ; Celestin & Yunfei, 2018 ; Yaghi & Bates, 2020 ). The most influential factor in predicting actual training transfer of school innovation by the in-service teachers was their motivation to transfer (Dreer et al, 2017 ).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Key factors influencing teachers’ motivation to transfer technology-enabled educational innovation

2023

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In response to the digital transformation in education, teachers are expected to develop new competencies. Although teachers gained valuable experience in digital technology use during the COVID-19 pandemic, research and practice show that primary school teachers need to be supported and trained for the new normal of innovative, advanced use and adoption of digital technologies in educational practice. This study aims to identify the key factors that influence teachers’ motivation to transfer technology-enabled educational innovation in primary education. The Learning Transfer System Inventory (LTSI) factors and the adoption factors of technology-enabled educational innovation have been conceptually mapped. The LTSI model has been empirically validated with data collected from 12.7% of Lithuanian primary school teachers. The structural equation modeling technique was utilized to analyze causal relationships of factors influencing teachers’ motivation to transfer technology-enabled educational innovation. The qualitative research method was used to provide a deeper understanding of key factors that influence motivation to transfer. The conducted analysis shows that motivation to transfer is significantly influenced by all five domains of factors: perceived value, personal characteristics, social practices, organizational and technology-enabled innovation factors. Motivation to transfer innovation varies according to teachers’ perceived digital technology integration skills, which underpin the importance of applying different roles and strategies based on the teachers’ skills. This study provides implications for designing effective professional development for in-service teachers and creating a suitable environment in schools for the adoption of innovation in post-COVID-19 education.

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The impact of learner characteristics on training transfer expectation: a survey of Thai teachers’ perception of cloud computing tools

Cited by 13 publications

References 53 publications

Self‐regulated learning and training effectiveness

Self‐regulated learning and training effectiveness

Towards an analysis of the transfer of training: empirical evidence from schools in Spain

Key factors influencing teachers’ motivation to transfer technology-enabled educational innovation

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