Abstract
This paper analyses the role of academic preparation and learning strategies in the prediction of first-year Portuguese college students' academic achievement, considering students' sex and academic field attended. A sample of 445 first-year college students (68.5% female) from the University of Minho (25.8% enrolled in economics, 35.3% in science/technology, and 38.9% in humanities degrees) participated in the study. Students answered a questionnaire on learning strategies in the classroom at the end of the first semester, which consisted of 44 items organized in five dimensions: comprehensive approach, surface approach, personal competency perceptions, intrinsic motivation, and organization of study activities. Academic achievement (grade point average at the end of first year) and academic preparation (students' higher education access mark) were obtained through the academic records of the university. Results showed that academic preparation was the strongest predictor of first-year academic achievement, and only marginal additional variance was explained by learning strategies as assessed by the self-reported questionnaire. There were sex and academic field differences, but these variables do not seem strong enough to affect the results, although the different percentages of variance captured by each model and the different weights associated to higher education access mark, stimulate the use of these and/or other personal and contextual variables when analysing the phenomenon.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.