Towards a multidimensional measure of self-efficacy in the adult learning ecosystem

ABSTRACT

For adults engaged in formal learning, self-efficacy judgements may have substantial impact on key learning attitudes and outcomes. A complex systems/person-centric perspective emphasises the importance of contextual features of adult life, yet extant study of self-efficacy is largely constrained to judgements of competence in course activities. This approach may omit critical information about adult learners’ motivational beliefs. We developed a multidimensional measure of adult learning self-efficacy and provided initial validation data using a sample of 1200 working adults in an online Masters of Computer Science degree program. Our results suggest that adult learning self-efficacy (SE) is a multidimensional construct assessing self-perceived competence in 1) integrating learning and non-learning demands (Adaptive SE), 2) achieving mastery of course content (Academic SE), and 3) leveraging learning for professional growth (Transfer SE). Academic SE was further distinguishable by efficacy for memory and problem solving (Academic-MP) and for maintaining attentional focus (Academic-F). Our measure contributes to the literature on adult learning ecosystems by expanding research on motivational beliefs to include self-efficacy for managing critical contextual features of adult learning.

KEYWORDS:

• Self-efficacy
• motivation
• online learning

Acknowledgements

This work would not have been possible without the support, insights, and access provided by the Georgia Institute of Technology College of Computing, especially Dr. David Joyner and Dr. Charles Isbell. We also thank the OMSCS program students who took the time to complete the survey, providing insight into their educational experience.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Alfred P. Sloan Foundation, Grant No. G-2019-12499. Preparation was also partially supported by the National Science Foundation under Grant No. 2247790 and Grant No. 2112532. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.