Likelihood of observing transformative learning amongst profession changers: a predictive analysis
by Tanuj Negi; Shashi Jain
International Journal of Quantitative Research in Education (IJQRE), Vol. 5, No. 4, 2022

Abstract: In this paper, we explore the possibility of whether the likelihood of observing transformative learning may be predicted using information related to personal history and current and previous professions. We examine empirical data collected from a group of Indian profession changers using a machine learning method: random forest algorithm. Results indicate that the following variables play an important role in prediction: 'overall formality (previous profession)', 'community sanction (previous profession)', 'professional authority (current profession)', 'bridge course', and 'gender'. Additionally, this provides empirical support to the position that profession change may have a transformative effect. A discussion and a list of areas for further research are provided.

Online publication date: Tue, 28-Mar-2023

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