EXECUTIVE FUNCTIONING AND GRADE POINT AVERAGE IN COLLEGE STUDENTS by Keli Fine A Senior Honors Thesis Submitted to the Faculty of The University of Utah In Partial Fulfillment of the Requirements for the Honors Degree in Bachelor of Science In Department of Psychology Approved: Sam Goldstein Thesis Faculty Supervisor Lisa G. Aspinwall, PhD Chair, Department of Psychology Jeanine K. Stefanucci, PhD Honors Faculty Advisor Sylvia D. Torti, PhD Dean, Honors College December 2014 Copyright 2014 All Rights Reserved
ii ABSTRACT Research has demonstrated a positive correlation between Executive Functioning (EF) and Grade Point Average (GPA; Duckworth, Tsukayama, & May, 2010; Latzman, Elkovitch, Young, & Clark, 2010; Knouse, Feldman, & Blevins, 2014). However, previous studies have failed to give a comprehensive view of all aspects of EF, instead focusing on at most three or four specific components and their relationship to GPA. In the current study, the Comprehensive Executive Function Inventory (CEFI) was used to measure nine different aspects of EF. Students GPA s along with demographic information and number of hours spent studying were gathered using self-report. Statistical analyses were conducted evaluating the relationship between EF and reported GPA. Working memory and hours spent studying were significantly correlated with GPA; eight other subscales of EF as well as overall EF scores did not prove to be significantly correlated with GPA. These results give a fuller picture of the relationship between EF and GPA, showing which aspects of executive functioning are most strongly connected to academic success. Keywords: executive functioning, grade point average, college students, success.
iii TABLE OF CONTENTS ABSTRACT ii INTRODUCTION 1 METHODS 4 RESULTS 6 DISCUSSION 8 CONCLUSION 12 REFERENCES 13
INTRODUCTION EF is a broadly defined construct that refers to mental control processes that enable physical, cognitive, and emotional self-control (Corbett, Hendren, Rocke, & Ozonoff, 2009, p. 210) EF facilitates the behaviors required for achieving a set goal, allowing us to do what we set out to do (or not). These processes are primarily carried out in the frontal lobe of the brain, which does not reach full maturation until a person is in his or her midtwenties. Studying the relationship between executive functioning and achievement in school-aged individuals, therefore, presents a unique opportunity to look at how deficits in EF due to lack of maturation may affect achievement. The standardization of school achievement in the form of GPA also makes this population a prime subject of interest for EF research. Previous research has demonstrated that the amount of time spent studying is not a good predictor of academic success. Schuman, Walsh, Olson, and Etheridge (1985) found that class attendance was a better predictor of both individual course grades and overall GPA than was the number of hours spent studying. This finding that was replicated by Plant and colleagues (2005), who also found that the quality of the time spent studying (i.e., how distracting the study environment was) was a better predictor of GPA than number of hours alone. This suggests that it is the way we study rather than how much we study that affects our success; however, because EF governs the way in which we seek to achieve our goals, the findings of Plant et al. could mean that executive functioning is the key to academic success rather than total time spent studying. Despite the indications that EF predicts academic success, previous research has only focused on a small number of somewhat random constructs in isolation that are
2 associated with EF. Duckworth, Tsukayama, and May (2010) looked at self-control derived from self, teacher, and parent reports, finding that increases in self-control did predict increases in GPA. Duckworth was not investigating EF, so while his results contribute to the general knowledge about EF in the sense that EF most certainly includes self-control, Duckworth s results only give us a very broad, non-specific measure of EF. Latzman and colleagues (2010) looked at the relationship between EF and specific academic subjects using the Delis-Kaplan Executive Functioning Systems, which divides EF into three constructs: conceptual flexibility, the ability to engage in flexible thinking and behavior ; monitoring, actively monitoring and evaluating information in working memory ; and inhibition, the ability to deliberately inhibit a dominant or automatic response (Latzman, Elkovitch, Young, & Clark, 2010, p. 456). Their findings showed that higher conceptual flexibility was associated with better scores in reading and science, monitoring was related to social studies and reading scores, and inhibition was related to math and science scores. Knouse and colleagues (2014) measured EF in the most extensive fashion to date using the Barkley Deficits in Executive Functioning Scale (BDEFS), which consists of five subscales: self-motivation, the ability to maintain effort; management of time, how prone an individual is to procrastination; organization, a measure of the individual s information processing abilities; self restraint, or impulsivity; and finally regulation of emotions, an individual s ability to recover emotionally after exposure to a stressor (Knouse, Feldman, & Blevins, 2014). Knouse and her colleagues found that self-motivation was the strongest predictor of GPA, with management of time, organization, and self-restraint also having positive relationships with GPA. Surprisingly,
3 regulation of emotions was found to have a negative relationship with GPA, a finding that Knouse was unable to explain. While these studies provide convincing evidence that EF may be a predictor of GPA, we do not have a full picture of the individual aspects of EF that contribute to higher GPAs from the disparate measures used. Duckworth s concept of self-control might encompass Knouse s self-restraint, management of time, organization, regulation of emotions, and motivation, or it might only encompass some of these factors but not others. Similarly, Latzman s inhibition might be related to Knouse s regulation of emotions and Duckworth s self-control, and monitoring might be related to management of time and organization, but conceptual flexibility seems to be distinct from any of the measures Knouse or Duckworth use. As stated earlier, Knouse was unable to explain the negative correlation between regulation of emotions and GPA, leaving another aspect of the EF and GPA relationship unknown. Therefore, in order to fully understand the relationship between EF and academic success, a tool that takes a comprehensive approach to measuring EF is required. The current study aims to do this using the Comprehensive Executive Function Inventory (CEFI; Goldstein & Naglieri, 2013), which gives both an overall EF score as well as breaking EF down into nine subscales. These subscales cover the constructs measured in previous studies but also break them down into more specific, distinct abilities. In doing so, we hope to gain a full understanding of the components of EF involved in academic success. METHOD Participants
4 The current study gathered information from 123 University of Utah students (82 F, 41 M) who had completed at least 12 credit hours at the University and were between the ages of 18 and 32. 80% of participants identified as Caucasian or white, 7% identified as having Latino or Hispanic, 6.5% identified as Hispanic and Caucasian, less than 1% identified as African American, American Indian, or Native Hawaiian respectively, and 4% identified as Asian. 7% of participants reported speaking a language other than English as their first language. Three participants reported receiving special academic accommodations, including testing accommodations for ADHD, enrollment in the trio program i, and the use of Braille and screen readers, none of which should affect the results of the current study. The average overall GPA of participants was 3.31. Students on average reported spending 14 hours per week studying. The study was posted on the University s online psychology research participant pool. Students were given.5 credit hours for completing the surveys. Participants were informed before beginning the study that their participation was voluntary, data would be kept anonymous with no identifying markers, and they were free to withdraw at any time. Written consent was not required as the study posed minimal risk to the participant. Measures The study itself consisted of two self-report surveys. The first collected general demographic information as well as GPA and study habits. Students were asked about their age, gender, race/ethnicity, first language, whether they received any academic special accommodations, the number of credit hours they had completed at the University, cumulative and major GPAs, and the estimated number of hours per week they spent studying.
5 Comprehensive Executive Function Inventory (CEFI). After completing the GPA survey, students were asked to complete the Comprehensive Executive Function Inventory (CEFI; Goldstein & Naglieri, 2013). The CEFI consists of 100 questions asking participants to think about their own thoughts and behavior during the previous four weeks: during the past four weeks, how often did you stay calm when handling small problems? During the past four weeks, how often did you find it hard to control your emotions? During the past four weeks, how often did you keep goals in mind when making decisions? For each question, participants may choose never (N), rarely (R), sometimes (S), often (O), very often (V), or always (A). The CEFI can be divided into 9 subscales, with 12 items measuring attention, defined as being able to keep one s attention focused on a particular task without getting distracted; 9 items measure emotion regulation, or the ability to manage emotions as well as responding with the appropriate level of emotion; 7 items measuring flexibility, the ability to adapt one s behavior to the situation and solve problems; 10 items measuring inhibitory control, or the ability to inhibit impulsive behavior; 10 items measuring initiation, a reflection of how well an individual is able to start tasks and self-motivate; 10 items measuring organization, including organization of tasks, thoughts, behavior, and time; 11 items measuring planning, defined as the ability to come up with strategies for solving problems and accomplishing tasks; 10 items measuring self-monitoring, or the ability to assess and adapt one s behavior in order to accomplish a task; and finally, 11 items measuring working memory, or the ability to keep in mind the information necessary to accomplish a task. A score of 100 on the CEFI constitutes an average score with higher numbers indicating better executive functioning (completion of intended
6 tasks) and lower scores indicating weaknesses in executive functioning. Scores are also calculated for each of the 9 subscales of the CEFI. The CEFI has proved to have both good reliability and validity. The Full Scale scores for CEFI were found to have a 0.97 Cronbach s alpha among normative samples for reliability. In addition, the CEFI correlates well with other accepted measures of EF, suggesting high validity. Table 1 Correlations between GPA and EF scores RESULTS R M SD Full Scale.11 106 13 Attention.08 102 14 Emotional Regulation.09 103 14 Flexibility -.0025 103 15 Inhibitory Control.10 108 12 Initiation.11 107 14 Organization.07 106 14 Planning.07 105 14 Self Monitoring.07 107 13 Working Memory.17* 104 13 Hours spent studying.50* 14 8
7 DF = 121 We calculated Pearson s R for each of the relationships between cumulative GPA and Full Scale CEFI score, GPA and each CEFI subscale score, and GPA and number of hours spent studying. Pearson s r gave insight into how strong the relationship was between the two variables of each correlation. Mean scores and standard deviations for each scale are given in Table 1. We had hypothesized that EF would be a better predictor of GPA than hours spent studying. However, our results did not support this hypothesis. Hours spent studying proved to be most strongly connected to GPA, with an R of.50; this finding was statistically significant at the.05 level. In regards to EF, working memory proved to be the best predictor of GPA and only statistically significant EF result with an R value of.17 and a p value of.03. Full Scale CEFI, Inhibitory Control, Emotion Regulation, and Initiation scores were next, with R values ranging between.09 and.11, though these results were not statistically significant. Organization, Planning, Attention, and Self Monitoring all were correlated with GPA at R values of.07 and.08, again not at a statistically significant level. Finally, Flexibility did not appear to predict GPA scores to any significant extent (R=-.0025). While the only results that were statistically significant were the influence of working memory and hours spent studying on GPA, previous findings have found statistically significant correlations between other EF constructs and academic achievement. DISCUSSION
8 Contrary to our hypotheses, our findings showed that working memory and hours spent studying were the only constructs that predicted academic success at a statistically significant level. Due to the fact that previous research suggests there are indeed significant correlations between EF constructs and academic achievement, repeating this experiment after taking measures to address methodological limitations discussed later might give us a much better understanding of exactly how executive functioning predicts GPA than previous research did. The relatively small number of factors used by other researchers each encompass several of the constructs in the CEFI. For example, the concept of self-control that Duckworth (2010) found to be correlated with GPA likely encompasses organization, planning, self-monitoring, and emotion regulation; motivation is likely measured by both initiation and planning. Conceptual flexibility can likely be accounted for by both flexibility and working memory. Breaking EF down into such distinct categories as we did in this study gives us a more precise measurement for looking at specifically where students are lacking and which skills are particularly critical for academic success. Our results did not show the same negative correlation between emotional regulation and GPA that Knouse, Feldman, and Blevins (2014) did; indeed, in this study emotional regulation was among the constructs most strongly correlated with GPA, suggesting that perhaps Knouse s findings are an example of a spurious correlation due to some underlying third variable. Knouse s studies were conducted at highly selective colleges; perhaps where the students had high motivation to do the work necessary to achieve their grades. High stress levels due to being at a selective school might result in lower levels of emotional regulation despite high achievement levels. The current study
9 looked at a state school where there is likely more school-life balance and less pressure to achieve, resulting in less stress and perhaps higher median levels of emotion regulation. These outcomes have implications for future research as well as the development of interventions. While hours spent studying was most strongly correlated with GPA, it could very well be the case that there is a point of diminishing returns after which additional hours spent studying contribute to minimal gains in GPA. Focusing on improving EF could help struggling students who are putting in the time but not getting the results. For instance, helping students improve their inhibition and attention skills might help them produce more results from the same amount of time by helping them stay focused and avoid distractions, in essence improving the quality of their study time as opposed to the quantity. Furthermore, in leveraging EF, students will be equipped to succeed in all areas of study, no matter the discipline, as opposed to subject-specific tutoring where students must choose which subject to invest their time in. For instance, interventions focused on helping students improve EF would be applicable to any and every subject they chose to study due to the fact that EF constructs are critical in any goal-directed behavior, such as achieving a certain grade, as opposed to having to choose a specific subject to invest their time in. As a result, future research should be directed towards developing and testing interventions that help address deficits in EF, especially in the areas of inhibitory control, emotion regulation, and initiation, as these were the strongest predictors of academic success. Future research might also look at whether the correlation between EF and academic success varies by major. Are there particular disciplines for which EF and/or particular aspects of EF are more critical than others? This might give insight into where
10 to direct efforts to intervene but also might be useful as a diagnostic tool for helping students at least know which majors might come naturally to them when trying to choose. In addition, these results also have implications for success in other areas of life outside academics. If success is not just a matter of working hard but also requires working well (engaging effectively in goal-directed behavior), corporations and organizations would do well to develop and look for employee development programs that target EF as a way of helping employees work toward their goals more effectively. The current research also has several limitations. First and foremost, only two of our findings were significant (the correlation between GPA and working memory and hours spent studying respectively), thus it cannot be concluded whether or not the rest of these factors truly play a role or not as was suggested by previous research. Self-report measures were used which creates the possibility of bias and even deception in the answers given by participants. We hoped that the anonymity of the surveys would limit this, as participants had no reason to lie or exaggerate when their answers were not traceable. Due to the nature of the study and the resources available, we were only able to ask participants about their own behavior and were therefore unable to obtain any thirdparty ratings of behavior that might have been enlightening in terms of getting a true picture of EF. Roommates, friends, and professors might have very different perceptions of subjects ability to engage in successful goal-directed behavior than those of the subjects themselves. A subject might think they rarely have trouble controlling their emotions while his or her roommate might think he or she is wildly over-reactive. Obtaining this kind of third-party input might have given us a more valid EF score.
11 We also were not able to control for confounding variables. Previous academic performance in the form of either high school GPA or SAT/ACT scores were not controlled for, and we did not ask participants about their expectations for their own success, both of which could influence college academic success. Previous successes may lead to expectations about achievement that could easily affect the amount of effort students put forth, influencing their achievement level and muddying the effects of EF. Furthermore, we were only able to assess GPA and EF retrospectively and were not able to use experimental manipulation, thus our results can only show correlation rather than causation. Future research might look at the effects of an EF-specific intervention on academic achievement (such as goal-setting exercises or other EF strategies) using a within-subjects design comparing students original GPA for one semester with that of their GPA for a semester after an EF intervention was administered. In this way, it might be possible to prove that increases in EF abilities cause increases in academic success rather than merely that they are correlated with it. CONCLUSIONS The goal of this study was to assess whether EF was correlated with GPA, and more specifically which aspects of EF best predicted GPA. We found that only one aspect of EF (working memory) predicted GPA at a statistically significant level and that this was still not as strong a predictor as hours spent studying. These results have implications for future research on developing interventions not only for college students but people in all walks of life seeking to improve their ability to set and achieve goals.
12 References Corbett, B. A., Constantine, L. J., Hendren, R., Rocke, D., & Ozonoff, S. (2009). Examining executive functioning in children with autism spectrum disorder, attention deficit hyperactivity disorder and typical development. Psychiatry Research, 166, 210-222. Retrieved March 12, 2016. Duckworth, A. L., Tsukayama, E., & May, H. (2010). Establishing Causality Using Longitudinal Hierarchical Linear Modeling: An Illustration Predicting Achievement From Self-Control. Social Psychological and Personality Science, 1(4), 311-317. Goldstein, S., Naglieri, J. A. (2013). Comprehensive Executive Function Inventory. North Tonawanda, NY: Multi-Health Systems Inc. Knouse, L. E., Feldman, G., & Blevins, E. J. (2014). Executive functioning difficulties as predictors of academic performance: Examining the role of grade goals. Learning and Individual Differences, 36, 19-26. Latzman, R. D., Elkovitch, N., Young, J., & Clark, L. A. (2010). The contribution of executive functioning to academic achievement among male adolescents. Journal of Clinical and Experimental Neuropsychology, 32(5), 455-462. Plant, E. A., Ericsson, K. A., Hill, L., & Asberg, K. (2005). Why study time does not predict grade point average across college students: Implications of deliberate practice for academic performance. Contemporary Educational Psychology, 30(1), 96-116. Schuman, H., Walsh, E., Olson, C., & Etheridge, B. (1985). Effort and reward: The assumption that college grades are affected by quantity of study. Social Forces, 63(4), 945-966.
13 Footnotes 1. The TRiO program consists of advising, instruction, tutoring, and informational workshops for students in need of assistance. As none of these interventions are EF specific, they should not affect the results of this study.
14 Name of Candidate: Keli Fine Birth date: September 21, 1991 Birth place: Address: London, United Kingdom 1161 Sunset Dunes Way Draper, UT, 84020