Likelihood of observing transformative learning amongst profession changers: a predictive analysis. (17th March 2023)

• Record Type: Journal Article
• Authors: Negi, Tanuj; Jain, Shashi
• Is Part Of: International journal of quantitative research in education. Volume 5:Number 4(2022)
• Page Start: 379
• Page End: 398
• Publication Date: 2023-03-17
• Subjects: transformative learning -- profession change -- career change -- machine learning -- prediction; Education -- Research -- Methodology -- Periodicals; Training -- Research -- Periodicals; Quantitative research -- Periodicals; 370.721
• Languages: English
• ISSNs: 2049-5986

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.