The Relationship Between Deep and Surface Learning Approaches and GPA.

The purpose of my study is to look at the relationship between rote learning and academic achievement among college students via study process questions. Prior research found that rote style learning approaches, which emphasize memorization and reproduction of information, may be associated with lower academic performance, while greater academic interest is generally associated with higher achievement. Herrmann, McCune, & Bager-Elsborg (2017) found that GPA was negatively related to a surface approach to learning and positively related to organized effort. My study is trying to look at whether students who rely more heavily on rote learning differ in academic performance or GPA. Murayama et al. (2016) found that students with greater academic interest tended to have higher achievement across several subject areas. I believe that academic interest will play a part in this process as the middle ground between rote learning and academic achievement. My study will be comparing the data between surface learning and deep learning compared alongside current GPA. My hypothesis is that rote learning, or surface learning, will correlate with lower GPA, and higher academic interest tied with deeper learning, will be associated with better GPA. Examining these variables together may provide a better understanding of how students’ learning strategies and motivation relate to academic success in higher education. There were 103 participants in the study, with their ages ranging from 18 to 32, with an average age of 19. There were 78 females and 24 males. 51 participants (50.0%) were freshmen, 35 (34.3%) were sophomores, 10 (9.8%) were juniors, and six (5.9%) were seniors. Most participants reported their ethnicity as Caucasian (n = 92, 90.2%). The remaining responses were Hispanic (n = 4, 3.9%), African American (n = 2, 2.0%), White (n = 2, 2.0%), Multiracial (n = 1, 1.0%), and Black (n = 1, 1.0%). Their Murray State University students from all different majors and years in school. They completed a series of questionnaires, including measures of deep and surface learning (Revised Two-Factor Study Process Questionnaire; Biggs et al., 2001) as well as current and expected GPA. I ran a series of Pearson correlations. I found that surface learning approaches were negatively associated with lower academic performance expectations (r = −.173, p = .136) and current GPA (r = −.053, p = .640). Deep-learning approaches show positive relationships with expected GPA (r = .107, p = .357), and the relationship between overall deep learning and current GPA was negative (r = −.028, p = .806). None of the relationships between learning approaches and GPA were statistically significant (p > .05). However, the negative relationship between overall deep learning and current GPA is an inconsistency worth investigating.