Flemmings Ngwira is a Senior Lecturer at Malawi University of Business and Applied Sciences (MUBAS) and the current Head of the Academic Department; Language and Communication. He holds a PhD in Applied Psychology (Health Communication) and besides teaching and doing consultancies, he supervises and...
Medical and allied health Students’ self-regulated learning: the interplay between motivational beliefs and learning strategies.
Conference Proceeding
Published 1 year ago, 377 views
Author
Dr. Flemmings Ngwira
Co-authors
Mary Kamwaza, Sufyan Rashid, Grace Boby, Grace Kadzakumanja, Dr. Flemmings Ngwira
Abstract
Introduction: Medical institutions have an exceptional responsibility to train health providers fit for the practice; more independent of their teachers in extending and updating their
knowledge base. Research on academic self-regulation suggests that students’ self-efficacy, intrinsic goal orientation and deep approach to learning improve students’ academic performance. The primary goal of the study was to investigate the role intrinsic goal orientation plays on students’ deep and meta-cognitive learning strategies. Methodology: A sample of 205 first year students (121 males and 84 females) from College
of Medicine in Malawi responded to a questionnaire assessing their intrinsic goal orientation and learning strategies use. Data were analyzed using IBM SPSS Statistics, version 20. Results: Linear regression results indicate that intrinsic goal orientation positively predicted both deep (β = 0.53) and meta-cognitive (β = 0.55) learning strategies. Safe-efficacy positively predicted both deep (β = 0.43) and meta-cognitive (β = 0.51) learning strategies. Male students had higher levels of intrinsic goal orientation than their female counterparts (p < 0.05), and there were no gender differences between male and female students on both deep and meta- cognitive learning strategies and self-efficacy.
Conclusion: The results suggest that intrinsic goal orientation and self-efficacy has an important impact on medical and allied health students’ deep learning approach. Possible implications of the results and recommendations for future research are discussed.