Linguistics and Literature Studies Vol. 6(1), pp. 27 - 34
DOI: 10.13189/lls.2018.060104
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Autonomous Learning and Principles for Deep Knowledge

I-Chin Nonie Chiang *
Foreign Language Center, National Chengchi University, Taiwan


This study aims to understand whether students have the ability to interpret the connotation of deep knowledge based on afterschool autonomous learning activities, habitual domains and principles for deep knowledge, and to use deep knowledge principles to analyze the relationship between the common autonomous learning activities and knowledge as well as students' rating on various types of deep knowledge. There were 71 participants in this study, and data were collected from group discussions and written records. The results showed that the students have the ability to understand the connotation of deep knowledge and propose a variety of autonomous learning methods. This study explored how these autonomous learning methods are closely connected to the deep knowledge of habitual domains based on the methods proposed by the students. The author also gave teaching suggestions in accordance with the participants' rating on the deep knowledge.

Autonomous Learning, EFL, Habitual Domains, Principles for Deep Knowledge

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] I-Chin Nonie Chiang , "Autonomous Learning and Principles for Deep Knowledge," Linguistics and Literature Studies, Vol. 6, No. 1, pp. 27 - 34, 2018. DOI: 10.13189/lls.2018.060104.

(b). APA Format:
I-Chin Nonie Chiang (2018). Autonomous Learning and Principles for Deep Knowledge. Linguistics and Literature Studies, 6(1), 27 - 34. DOI: 10.13189/lls.2018.060104.