Enhancing learners' metacognition for smart learning: effects of deep and surface learning, disorganisation, achievement goals and self-efficacy Online publication date: Tue, 07-May-2019
by Kam Cheong Li; Billy Tak-Ming Wong
International Journal of Smart Technology and Learning (IJSMARTTL), Vol. 1, No. 3, 2019
Abstract: Learners' metacognition has been recognised as a key factor in their success in smart learning. However, for enhancing their metacognition for smart learning, the effects of other variables have been rarely addressed. This paper presents a study on the effects of deep and surface learning, disorganisation, achievement goals and self-efficacy on metacognition. Questionnaires measuring these variables were administered to 311 Hong Kong undergraduate students. Correlational analysis showed significant correlations among all these variables. Regression analysis showed that deep and surface learning, and self-efficacy, were significant predictors of metacognition. Among all the predictors, deep learning was shown to be the strongest, followed by surface learning, disorganisation and self-efficacy. The implications of the results are discussed in respect of the strategies to enhance metacognition to facilitate learners' success in a smart learning environment.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Smart Technology and Learning (IJSMARTTL):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook