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This purpose of this paper is to use autoethnographic methods to enable an individual to reflect on their learning practices and habits in their professional role in computing and data science.

Action reflection cycles with autoethnographic methods are used in this enquiry to examine and transform the self-driven learning in the role of a professional in the context of computing and data science. Data are collected using personal thoughts and emotions in order to determine to what degree the changes that are brought about are improving their learning practice.

An incongruity between espoused theory and theory-in-use is identified. In this paper the authors explore how engaging in reflection within the structure of an action research framework can change the learning behaviour of an individual, motivating them to engage more positively and consistently with self-directed learning in their workplace environment.

Through analysis of reflective journals key aspects of personal and professional life that influences the individual’s approach to the learning tasks is identified. In addition, activity logs are maintained which collect information on the tasks that are undertaken. These activity logs are automatically generated using specific task-tracking software. Double-loop learning (Argyris and Schön, 1974) helps to identify the values that underpin the learning practice.

By resolving the tension created due to the incongruity of values, the individual has been able to find more motivation for learning and thus become more engaged in the learning process.

Participation in the knowledge economy requires individuals to engage in continuous learning. Organisations that support individuals in continuous learning are best adapted to take advantage of emergent and evolving knowledge and skill requirements.

This paper examined the behaviour of an individual engaging in self-directed learning and showed how through engagement with reflection and critical self-analysis, he increased his motivation and efficiency for self-directed learning. The authors show how this skill becomes increasingly important in modern knowledge economy workplaces.