DIGITAL LIBRARY
EVALUATION OF COGNITIVE ENGAGEMENT OF ADULT, DISTANCE LEARNERS: A PILOT STUDY
1 Universiti Teknologi MARA (UiTM) (MALAYSIA)
2 Institute of Educational Development (MALAYSIA)
About this paper:
Appears in: INTED2010 Proceedings
Publication year: 2010
Pages: 1125-1129
ISBN: 978-84-613-5538-9
ISSN: 2340-1079
Conference name: 4th International Technology, Education and Development Conference
Dates: 8-10 March, 2010
Location: Valencia, Spain
Abstract:
Working adults pursuing higher education should be getting good, quality education that would last a lifetime. Realizing that higher education would result in better career prospects, many working adults are returning to school to read for a higher degree. Furthermore, with the current economic slowdown, there has been a noticeable increase in adult students registered with various programs at the Institute of Educational Development (InED), Universiti Teknologi MARA (UiTM), Malaysia. Quality education pursued should be reflected in these traditional learners’ academic performance, their cumulative grade point average (CGPA). Better grades are reflected if the student’s level of cognitive engagement is significant. Thus, the purpose of this paper is to report the preliminary findings on the level of cognitive engagement among adult, distance learners registered in three programs. Cognitive engagement is the aggregate construct studied with sub-pedagogical variables such as perceived course value, deep learning and surface learning. Literature on previous research within the scope of e-learning has showed mixed findings. For this pilot study the response rate was 70%, and the analysis showed that there is some integration and utilization of students’ motivations and strategies in the course of their learning. However, attempts to tap the students’ full learning potential or cognitive engagement requires a bigger sample as well as the investigation of deep approaches to learning.
Keywords:
Cognitive engagement, e-learning, adult learners, distance education, deep learning.