Nonintellective variables and nontraditional college students: A domain-based investigation of academic achievement.
Key words: nontraditional students, nonintellective variables, academic achievement, grade point average (GPA), motivational factors, personality
Institutions of higher education have seen dramatic increases in the proportion of nontraditional students over the past few decades (Newbold, Mehta, & Forbus, 2010; Tilley, 2014). Prior research suggests this demographic shift has broad manifestations inasmuch as nearly three quarters of college students have at least some nontraditional characteristics (Horn, 1996; Wlodkowski, 2003). Nontraditional students are becoming increasingly visible at institutions of higher education and this trend does not appear to be slowing (Bye, Pushkar, & Conway, 2007).
These demographic shifts have led to research efforts aimed at a more thorough understanding of nontraditional students, the obstacles they encounter, and factors related to their academic performance. According to Tilley (2014) and Bennet, Evans, and Reidle (2007), for research purposes nontraditional students are primarily distinguished from traditional students by age (most commonly, greater than 25 years old). Research into non-traditional student academic performance has explored many factors relevant to achievement, including time management, stress, coping strategies (Forbus, Newbold, & Mehta, 2010), motivation, interest in academic material, positive affect (Bye et al., 2007), role conflict due to work and family obligations (Richards, 2008), access to financial resources (Hart, 2003), and academic anxiety (Rico, Beal, & Davis, 2010). Giancola, Giancola, and Borchert (2009) and Tones, Fraser, Edler, and White (2009) noted that nontraditional students typically experience greater levels of stress than traditional students, likely related to greater nonacademic demands (e.g., financial concerns, family and work obligations).
Much of this prior research involves investigating the relevance of findings generated in studies of traditional students to nontraditional student samples. Correlational strength and predictive power of personality variables with nontraditional student grade point average (GPA; commonly used as an indicator of academic achievement) have been studied repeatedly and revealed trends that differentiate nontraditional students from their younger counterparts (Tilley, 2014). However, given the consistent intercorrelations between many of these variables (Richardson, Abraham, & Bond, 2012), determining the relevance of broad categories of variables to nontraditional student GPA in addition to personality variables may be more useful for programs aimed at adult learner academic success.
Richardson et al. (2012), in a meta-analysis of research on academic performance, contend that there are two broad categories of variables investigated in prior research: intellective and nonintellective. Intellective variables are more commonly studied and can be understood as representing intelligence and cognitive aptitude (e.g., IQ, SAT/ACT scores, high school GPA, A-level points; Richardson et al., 2012). Despite the widespread reliance on these types of variables in applied settings, they often have only low-to-moderate correlations with GPA (Frey & Detterman, 2004; Richardson et al., 2012). Further, given that many of the obstacles to academic success presented to nontraditional students are broader-ranging than those that general intelligence pertains to, intellective variables may not be as relevant to achievement as nonintellective variables.
Research into nonintellective variables and GPA has grown significantly in the last two decades (Richardson et al., 2012). Unlike intellective variables, nonintellective variables are more context-specific and through meta-cognitive processes can be more easily adjusted (e.g., Wolters, 1998). Richardson et al. (2012) found 42 distinct nonintellective constructs that correlate with GPA among university-level students. These variables grouped into five research domains: personality traits, motivational factors, self-regulatory learning strategies, students' approach to learning, and psychosocial contextual influences. Of these domains, psychosocial contextual influences were found to have the weakest correlations while motivational factors had the strongest correlations. Performance self-efficacy, grade goal, effort regulation, and academic self-efficacy were the most highly correlated personality variables (Richardson et al., 2012).
The aforementioned studies on nonintellective variables have generally focused on traditional-aged college students. The present study was conducted to investigate plausible nonintellective correlates of academic achievement (operationalized as GPA) among nontraditional college students using the research domains suggested by Richardson et al. (2012) as a conceptual framework. Specific research questions arising from this literature review were: a) Which research domains are highly related to GPA among nontraditional students? b) Which personality variables are highly correlated with nontraditional student GPA? and c) Are the domains and variables that are related to traditional student GPA similarly related to nontraditional student GPA?
Of the 42 nonintellective variables and five research domains investigated by Richardson et al. (2012), six variables and two research domains (personality traits and motivational factors) were investigated in this study. These variables were further separated into 16 component variables (e.g., academic motivation was measured as extrinsic motivation, intrinsic motivation, and amotivation). The following sections are an overview of the variables included in this study.
Need for Cognition (Personality Trait)
Cohen (1957) suggested that the need for cognition construct was predictive of how an individual approaches information and problems. Cacioppo, Petty, and Kao (1984) describe need for cognition as "an individual's tendency to engage in and enjoy effortful cognitive endeavors" (p. 306). Prior research has shown that need for cognition has small but positive correlations with GPA (Richardson et al., 2012). Need for cognition was included in the present study primarily due to a dearth of research as it relates to nontraditional student GPA.
Academic Procrastination (Personality Trait)
Procrastination refers to a tendency to delay a specific but necessary activity (Solomon & Rothblum, 1984). Procrastination has been studied extensively as it relates to a variety of tasks and settings, including academic work (Steel, 2007). Broadly, this research has consistently shown that academic procrastination is negatively correlated with GPA (Richardson et al., 2014; Steel, 2007). However, as with need for cognition, academic procrastination is an understudied phenomenon among nontraditional students.
Grit (Personality Trait)
Grit (Duckworth, Peterson, Matthews, & Kelly, 2007) is a trait-level personality construct that relates to long-term perseverance of difficult goals. Grit has been shown to correlate with various forms of academic achievement, including GPA, among high-achieving students (Duckworth et al., 2007; Duckworth & Quinn, 2009). Research has demonstrated relationships between grit and GPA among military academy students (Duckworth et al., 2009), ethnically diverse undergraduate students (Wolters & Hussain, 2015) and Black men at predominantly White institutions (Strayhorn, 2014)). Though it was not included in the meta-analysis by Richardson et al. (2012), grit is considered a personality trait (Duckworth et al., 2007) and was included in the present study to better understand its relation specifically with undergraduate nontraditional student GPA.
Academic Locus of Control (Motivational Factor)
Locus of control refers to a set of attributional tendencies in contexts where performance is a key factor (Rotter, 1966), and is often described in terms of perceived control over specific events and outcomes (e.g., academic locus of control). An individual with an external locus of control tends to describe causal factors of task performance in terms of external factors such as luck or the activity of other people. Providing explanations for success or lack thereof by means of internal factors such as effort and skill is a hallmark of an individual with an internal locus of control (Rotter, 1966). Richardson et al. (2012) found an internal academic locus of control was positively correlated with GPA, while external academic locus of control was not associated with GPA. Though well-studied among traditional student samples (for a meta-analysis, see Twenge, Zhang, & Im, 2004), little is known about academic locus of control and achievement specifically among nontraditional students.
Academic Motivation (Motivational Factor)
Intrinsic and extrinsic motivation can be understood as opposed sources of motivation (Vallerand et al., 1992). Academic intrinsic motivation is rooted in personal interest and pleasure derived from an activity (e.g., appreciation of course material or of challenging academic work). Academic extrinsic motivation for task performance is regulated by external factors such as parental expectations of academic performance or the desire for higher-paying career opportunities. Deci and Ryan (1985) and Vallerand and Bissonnette (1992) have suggested a third type, amotivation, which results from an inability to perceive causal relationships between activities and outcomes.
As with academic locus of control, Richardson et al. (2012) found that academic intrinsic motivation was a small positive correlate of GPA, though extrinsic motivation was not associated with GPA. Results on the relationship between amotivation and nontraditional undergraduate student GPA have not been published to date.
Academic Self-efficacy (Motivational Factor)
Self-efficacy captures the self-belief of an individual to perform necessary tasks required to accomplish goals and create desired outcomes (Bandura, 1977; 1982). Zimmerman (2000) suggested that self-efficacy is domain-specific and has predictive validity in academic settings. Richardson et al. (2012) found that performance self-efficacy was the strongest predictor of GPA among the variables included in their meta-analysis. Academic self-efficacy was demonstrated to be a moderate correlate of GPA. Like the other variables investigated in the present study, academic self-efficacy has not been studied extensively among nontraditional students.
The Current Study
The six variables investigated in the present study were expected to individually correlate with and predict GPA among both traditional and nontraditional students. It was hypothesized that variables within the motivational factors domain (locus of control, motivation, and self-efficacy) would be more strongly related to nontraditional student GPA. Personality variables hypothesized to correlate strongly with nontraditional student GPA include self-efficacy and motivation. It was suspected that need for cognition may correlate moderately with GPA. Further, it was also hypothesized that there would be significant differences in correlates and predictors of GPA by student type.
A sample of 139 undergraduate students (M age = 29.43, SD = 11.58) was recruited from a small, rural Southeastern college. In keeping with the most commonly used method in current literature (Tilley, 2014), 25 years of age was used to place nontraditional and traditional students into separate samples. Seventy-two participants were identified as nontraditional students (M age = 37.42, SD = 11.04). Sixty-seven participants were identified as traditional students (Mage = 20.76, SD= 1.78).
Data was initially collected from 171 participants, but responses from 32 participants were removed due to incomplete surveys. Other exclusion criteria included being under 18 years of age (i.e., dual enrollment students) and/or a first-semester freshman with no GPA earned at the college level. Some participants received extra credit for their participation.
Personality trait measures. Need for cognition was measured using the 18-item Need for Cognition Scale (NCS; Cacioppo et al., 1984). Cronbach's alpha for these items in the present sample was 0.88. The Procrastination Assessment Scale for Students (PASS; Solomon et al., 1984) was used to measure academic procrastination and includes to subscales measuring frequency of ([alpha] = 0.87) and reasons for ([alpha] = 0.88) procrastination. The original 12-item Grit Scale (Duckworth et al., 2007; [alpha] = 0.79) was used to measure this personality trait.
Motivational factor measures. We used the Revised Academic Locus of Control Scale (Curtis & Trice, 2013) to measure academic internal and external locus of control. We classified participants as internal locus of control if their score was below 10.50 (the median for the current sample) and external locus of control if their score was greater than or equal to 10.50. To measure academic intrinsic and extrinsic motivation and amotivation, we used the 28-item Academic Motivation Scale (Vallerand et al., 1992). The three subscales of this measure--academic intrinsic motivation ([alpha] = 0.95), academic extrinsic motivation ([alpha] = 0.91), and amotivation ([alpha] = 0.89)--demonstrated high reliability in the current sample. Zajacova, Lynch, and Espenshade (2005) constructed the Academic Self-Efficacy Scale with relevant items taken from the Academic Milestones Scale (Lent, Brown, & Larkin, 1986) and the College Self-efficacy Inventory (Solberg, O'Brien, Villareal, Kennel, and Davis, 1993). The Academic Self-efficacy Scale ([alpha] = 0.95) was chosen for use in the present study because its authors removed items irrelevant to the experiences of nontraditional students (e.g., on-campus housing; Zajacova et al., 2005).
Eligible participants were recruited via a link that was posted on the campus newsfeed within the learning management system. After giving informed consent, participants were prompted to complete an online survey on the Qualtrics data collection platform. In total, each participant was asked to respond to 216 items. Participants were given a debriefing statement following completion of the survey.
Data analysis was performed in two broad steps. The primary analysis utilized multiple linear regression to establish predictors of academic achievement and was performed with all personality and motivational variables as predictors of one outcome variable, GPA (referred to as the original model below). After removing nonsignificant predictors, we reentered the remaining variables into a revised model. This revised model was then used to ascertain predictive power for nontraditional and traditional students independently.
Secondary analyses were performed using independent samples t-tests to compare differences between by student type (traditional versus nontraditional). Pearson correlation coefficients were used to demonstrate correlations among continuous variables.
Original model. Sixteen predictor variables and one outcome variable, GPA, were used in one model that included both nontraditional and traditional students (dummy-coded). This overall model was a good fit, F(18, 119) = 2.16, p = 0.008, [R.sup.2]= 0.25. However, only one of the predictor variables demonstrated both meaningful predictive power and a statistically significant relationship with GPA--amotivation, [beta] = -22, p = 0.04. Because student type was not a significant predictor of GPA, revised models were run separately by student type so that a contrast of predictors could be made.
Multicollinearity assumptions were also violated in the original model. To correct for this violation, we removed all locus of control scales and the reasons for procrastination subscale. These were removed due to weak predictive power and were not statistically significant.
Revised model. After reviewing the predictive power and significance of the original model, we included need for cognition, grit, academic motivation, academic self-efficacy, including component subscales for each measure in a revised model. When applied to the nontraditional student sample, this model was not significant, F(11, 59) = 1.58, p = 0.13, [R.sup.2]= 0.23. Amotivation was a significant individual predictor of GPA, [beta] = -0.28, p = 0.04. Though conceptually related to amotivation, intrinsic motivation ([beta] = 0.17, p = 0.22) and extrinsic motivation ([beta] = -0.10, p = 0.48) were not significant individual predictors. Other results notable for a lack of significance as individual predictors were demonstrated by grit ([beta] = -0.18, p = 0.13), confidence in self-efficacy ([beta] = 0.14, p = 0.39), and need for cognition ([beta] = -0.21, p = 0.10).
To elucidate differences between nontraditional and traditional students, we applied the same model to the traditional student group. Among the traditional student group, the model was a good fit, F(11, 55) = 2.50, p = 0.001, [R.sup.2]= 0.33, with greater predictive power of GPA than the original model. Two variables were significant individual predictors in the expected positive direction: grit ([beta] = 0.32, p = 0.01), and confidence in academic self-efficacy ([beta] = 0.38, p = 0.02). Procrastination due to fear of failure ([beta] = 0.36, p = 0.047) was also a significant individual predictor, though in an unexpected direction. Amotivation ([beta] = -0.31, p = 0.06) was negatively predictive of GPA, but it was only marginally significant.
Secondary analyses of participant data were performed using Pearson correlation coefficients and independent samples t-tests. For the Pearson correlations, the overall sample was divided by the conventional 25-year boundary and treated as two distinct samples in which various correlations occurred. Data were treated as dummy-coded categorical variables for the t-tests. The variables used in these analyses were the same as those used in the original model described in the Primary Analysis section.
Pearson correlation coefficients. Nontraditional student GPA negatively correlated with need for cognition, r(71) = -0.26, p = 0.03, amotivation, r(71) = -0.29, p = 0.01, and confidence in self-efficacy, r(71) = 0.25, p = 0.04. There was a marginal trend for a negative correlation between grit and GPA, r(71) = -0.20, p = 0.09. Among traditional students, extrinsic motivation, r(69) = 0.27, p = 0.03, and confidence in self-efficacy, r(69) = 0.35, p = 0.004, were each found to positively correlate with GPA.
Independent samples t-tests. GPA did not differ significantly by student type, t(136) = 1.71, p = 0.09. Mean GPA for nontraditional students was 3.26 (SD = 0.47). For traditional students, mean GPA was 3.11 (SD = 0.58).
Measured differences between two aspects of procrastination--reasons for procrastination and procrastination due to fear of failure--were significant. Traditional students reported more procrastination due to fear of failure (M= 52.43, SD = 12.17) than nontraditional students (M = 49.32, SD = 11.04), t(136) = 2.95, p = 0.004. Scores for reasons for procrastination were also significantly higher for traditional students (M = 64.30, SD = 16.20) than for nontraditional students (M= 56.34, SD = 15.47), t(136) = 3.143, p = 0.002. A non-significant trend for procrastination due to task aversion was found (traditional M = 2.72, SD = 0.83; nontraditional M= 2.44; SD = 0.84), t(136) = 1.932, p = 0.055).
Nontraditional students (M = 5.11, SD = 0.17) reported significantly higher intrinsic motivation than traditional students (M = 4.58, SD = 0.19), t(136) = -2.17, p = 0.03. Amotivation was significantly higher among traditional students (M = 2.02, SD = 0.17) than among nontraditional students (M = 1.48, SD = 0.12), t(136) = 2.727, p = 0.007.
Traditional students reported greater recognition of stressors interfering with self-efficacy (M = 6.43, SD = 0.20) than nontraditional students (M= 5.44, SD = 0.24), t(136) = 3.131, p = 0.002. Traditional students also scored higher on need for cognition (M = 46.90, SD = 1.40) than nontraditional students (M= 42.28, SD = 1.38), t(136) = 2.36, p = 0.02.
Personality variables included in this investigation demonstrated greater predictive power and stronger correlations with traditional student GPA than with nontraditional student GPA. Specific personality variables most closely related to nontraditional student GPA were amotivation (correlate and inverse predictor in a nonsignificant model), need for cognition (inversely correlated), and self-efficacy (positively correlated). These three variables demonstrated relationships with GPA, but were not predictive. Compared to traditional students, nontraditional students reported less procrastination. Nontraditional students also reported higher levels of amotivation and intrinsic motivation. These results lend credence to the notion that factors related to academic achievement among nontraditional and traditional students are different, which corroborates prior findings (Tilley, 2014).
Referring back to the five research domains suggested by Richardson et al. (2012), these results indicate that personality traits and motivational factors may be less relevant for nontraditional students than traditional students. Future research related to nontraditional student achievement might focus on more thorough investigation of the three research domains not investigated in the present study: self-regulatory learning strategies, psychosocial contextual influences, and students' approach to learning. Though variables in the psychosocial contextual influences domain (e.g., academic integration, social support) have not been highly correlated with overall student GPA (Richardson et al., 2012), these types of variables are possibly more relevant to nontraditional students (Goncalves & Trunk, 2014). Nontraditional students have reported significant feelings of isolation, administrative inflexibility to nontraditional student circumstances, and an absence of relevant student organizations as barriers to achievement (Goncalves et al., 2014). Such factors may be less relevant for traditional students, who, for various reasons (e.g., on-campus housing, consistent access to student organizations), may be better integrated into the social system of academic institutions.
The research domain most closely related to nontraditional student GPA was motivational factors. Amotivation has not been studied previously with respect to nontraditional student GPA and presents opportunities for further research due to a seemingly strong inverse relationship. Intrinsic motivation was significantly higher among nontraditional students, a finding that has been reported previously (Bye et al., 2007). Extrinsic motivation was found to be neither significantly different between nontraditional and traditional students nor related to GPA in either group, consistent with Richardson et al. (2014).
The relative importance of motivational factors for nontraditional students can be understood in light of the greater number and different types of obstacles presented to this population during their academic careers (Goncalves et al., 2014; Tilley, 2014).
However, motivation was not a correlate or predictor of nontraditional student GPA. Intrinsic motivation seemed to relate to nontraditional student status, but not with academic achievement. One interpretation of this finding is that the decision to enroll as a nontraditional student, of the broader population of adults older than 25 years, is made by people who are highly intrinsically motivated and can overcome difficulties with role conflict and stressors (e.g., Tilley, 2014). Future research efforts could focus on how role conflict (overlooked in the present study) is related to intrinsic motivation and academic achievement among nontraditional students.
Results from the current study suggest that nontraditional students procrastinate less often than their traditional counterparts. This finding corroborates results from previous studies (Balkis, 2013; Prohaska, Morrill, Atiles, & Perez, 2000). Perhaps most relevant to the nontraditional student population is the relation of procrastination to role conflict (Senecal, Julien, & Guay, 2003). As nontraditional students experience more role conflict and nonacademic obligations than traditional students (Goncalves et al., 2014; Tilley, 2014), and role conflict encourages the prioritization of one role of many competing obligations (Steel, 2007), then it follows that procrastination would more closely relate to nontraditional student GPA than traditional student GPA. Results of the current study are consistent with this notion, but further empirical research is needed to test these speculations.
Need for cognition, though well-research among the broader student population, has not been thoroughly investigated as a possible GPA predictor among nontraditional students. Results from the present study indicate that there is a slight inverse relationship between nontraditional student GPA and age (the primary indicator of nontraditional status in the present study). Sports (1994) suggested that need for cognition decreases with age due to, among other factors, general cognitive decline, though there are plausible additional reasons. Considering need for cognition in its original context as one of many needs (Maslow, 1943) in relation to the nontraditional experience suggests that the variable is less relevant to their academic experience. As Tilley (2014) suggests, nontraditional students are typically most concerned with the financial and social benefits of higher education.
Results of the current study underline previous findings about the strong relationship between academic self-efficacy and student GPA, as well as nontraditional student GPA in particular (Cerino, 2014; Richardson et al., 2012; Zajacova et al., 2005; Bong, 2001; Lent et al., 1986). The importance of academic self-efficacy seems one significant point of similarity between nontraditional and traditional student achievement. Because self-efficacy is a modifiable factor, this highlights a relevant avenue for intervention for students with low achievement.
An important limitation of this study was sample bias in the direction of students with high GPAs (nontraditional M=3.26; traditional M=3.11). This bias presents an obstacle for determining predictors of lower GPA and academic achievement. Prior studies of nonintellective variables and academic achievement had similar sample bias (e.g., samples of National Spelling Bee contestants in Duckworth et al., 2007). The method used for recruiting participants in the present study--self-selection and some students receivingextra credit for academic courses--provides a plausible explanation for why many high-achievers and few low-achievers participated (Harrison, Meister, & LeFevre, 2011). Future studies should ensure a broader range of GPA.
As mentioned in prior literature (Tilley, 2014), the standard method for separating nontraditional and traditional students is by age (usually 25 years). Various researchers (Thunborg, Bron, & Edstrom, 2013; Tilley, 2014; Goncalves et al., 2014) have suggested that subtleties beyond age characterize the nontraditional student experience: poorer academic integration, family and employment obligations, greater stress levels, and financial burdens. Future research into nontraditional student academic achievement should include refinement of the definition of "nontraditional" for a more nuanced understanding of this diverse and growing population. This may allow for a better comparison among the similarities and differences in academic achievement based on traditional/nontraditional student type so that student services and programs geared toward student achievement can better meet the needs of the entire student body.
Balkis, M (2013). Academic procrastination, life satisfaction and academic achievement: The mediation role of rational beliefs about studying. Journal of Cognitive and Behavioral Psychotherapies, 13(1), 57-74.
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychology Review, 84(2), 191-215.
Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122-147.
Bennett, S., Evans, T., & Riedle, J. (2007). Comparing academic motivation and accomplishments among traditional, nontraditional, and distance education college students. Psi Chi Journal of Undergraduate Research, 12(4), 154-161.
Bong, M. (2001). Role of self-efficacy and task-value in predicting college students' course performance and future enrollment intentions. Contemporary Educational Psychology, 26(4), 553-570.
Bye, D., Pushkar, D., & Conway, M. (2007). Motivation, interest, and positive affect in traditional and nontraditional undergraduate students. Adult Education Quarterly, 57(2), 141-158.
Cerino, E. S. (2014). Relationships between academic motivation, self-efficacy, and academic procrastination. Psi Chi Journal of Psychological Research, 19(4), 156-163.
Cohen, A. R. (1957). Need for cognition and order of communication as determinants of opinion change. In C. I. Hovland (Ed.) The order of presentation and persuasion. New Haven, CT: Yale University Press.
Curtis, N. A., & Trice, A. D. (2013). A revision of the academic locus of control scale for college students. Perceptual & Motor Skills, 116(3), 817-829. doi: 10.2466/08.03.PMS.116.3.817-829
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York, NY: Plenum Press.
Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. Personality Processes and Individual Differences, 92(6), 107-1101. doi: 10.1037/0022-35184.108.40.2067
Duckworth, A. L., & Quinn, P. D. (2009). Development and validation of the short grit scale (Grit-S). Journal of Personality Assessment, 91(2), 1087-1101. doi: 10.1080/00223890802634290
Forbus, P., Newbold, J. J., & Mehta, S. S. (2011). A study of non-traditional and traditional students in terms of time management behaviors, stress factors, and coping strategies. Academy of Educational Leadership Journal, 15(1), 109-125.
Frey, M. C., & Detterman, D. K. (2004). Scholastic assessment or g? The relationship between the SAT and general cognitive ability. Psychological Science, 15, 373-378. doi: 10.1111/j.0956-7976.2004.00687.x
Giancola, J. K., Giancola, M. J., & Borchert, D. (2009). Dealing with the stress of college: A model for adult students. Adult Education Quarterly, 59(3), 246-263. doi: http://dx.doi.org/10.1177/0741713609331479
Goncalves, S. A., & Trunk, D. (2014). Obstacles to success for the nontraditional student in higher education. Psi Chi Journal of Psychological Research, 19(4), 164-172.
Harrison, M. A., Meister, D. G., & LeFevre, A. J. (2011). Which students complete extra-credit work? College Student Journal, 45, 550-555.
Hart, N. K. (2003). Best practices in providing nontraditional students with both academic and financial support. New Directions for Higher Education, 121(4), 99-106. http://dx.doi.org/10.1002/he.104
Horn, L. (1996). Nontraditional undergraduates: Trends in enrollment from 1986 to 1992 and persistence and attainment among 1989 beginning postsecondary students. Washington, DC: U.S. Government Printing Office.
Lent, R. W., Brown, S. D., & Larkin, K. C. (1986). Self-efficacy in the prediction of academic performance and perceived career options. Journal of Counseling Psychology, 33(3), 265-269. doi: http://dx.doi.org/10.1037/0022-0220.127.116.115
Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50, 370-396
Prohaska, V., Morrill, P., Atiles, I., & Perez, A. (2000). Academic procrastination by nontraditional students. Journal of Social Behavior and Personality, 15, 125-134.
Richards, J. (2008). The benefits of an accelerated learning format in teacher education programs. Journal of Research in Innovative Teaching, 1(1), 73-81.
Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students' academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353-387. doi: 10.1037/a0026838
Rico, J. S., Beal, J., & Davies, T. (2010). Promising practices for faculty in accelerated nursing programs. Journal of Nursing Education, 49(3), 150-155. http://dx.doi/org/10.3928/01484834-20100115-01
Rotter, J. B. (1966) Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs, 80(1), 1-28.
Senecal, C., Julien, E., & Guay, F. (2003). Role conflict and academic procrastination: A self-determination perspective. European Journal of Social Psychology, 33, 135-45. doi: 10.1002/ejsp.144
Solberg, V. S., Hale, J. B., Villarreal, P., & Kavanaugh, J. (1993). Development of the college stress inventory for use with Hispanic populations: A confirmatory analytic approach. Hispanic Journal of Behavioral Sciences, 15(4), 490-497. doi: 10.1177/07399863930154004
Solomon, L. J., & Rothblum, E. D. (1984). Academic procrastination: Frequency and cognitive correlates. Journal of Counseling Psychology, 31, 503-509.
Spotts, H. (1994). Evidence of a relationship between need for cognition and chronological age: Implications for persuasion in consumer research. Advances in Consumer Research, 21(1), 238-243.
Steel, P. (2007). The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure. Psychology Bulletin, 133(1), 65-94. doi: 10.1037/0033-2909.133.1.65
Strayhorn, T. (2014). What role does grit play in the academic success of black male collegians at predominantly white institutions? Journal of African American Studies, 18(1), 1-10. doi: 10.100/s12111-012-9243-0
Thunborg, C., Bron, A. & Edstrom, E. (2013). Motives, commitment and student identity in higher education--Experiences of non-traditional students in Sweden. Studies in Education of Adults, 45(2), 177-193.
Tilley, B. P. (2014). What makes a student non-traditional? A comparison of students over and under age 25 in online, accelerated psychology courses. Psychology Learning and Teaching, 13(2), 95-106. http://dx.doi.org/10.2304/plat.2014.13.2.95
Tones, M., Fraser, J., Edler, R, & White, K. M. (2009). Supporting mature-aged students from a low socioeconomic background. Higher Education, 58(4), 505-529. http://dx.doi.org/10.1007/s10734-009-9208-y
Twenge, J. M., Zhang, L., & Im, C. (2004). It's beyond my control: A cross-temporal meta-analysis of increasing externality in locus of control, 1960-2002. Personality and Social Psychology Review, 8(3), 308-319.
Vallerand, R. J., & Bissonnette, R. (1992). Intrinsic, extrinsic and amotivational styles as predictors of behavior: A prospective study. Journal of Personality, 60(3), 599-620.
Vallerand, R. J., Pelletier, L. G., Blais, M. R., Briere, N. M., Senecal, C., & Vallieres, E. F. (1992). The academic motivation scale: A measure of intrinsic, extrinsic, and amotivation in higher education. Educational and Psychological Measurement, 52.
Wlodkowski, R. J. (2003). Accelerated learning in colleges and universities. New Directions for Adult and Continuing Education, 97, 5-15. http://dx.doi.org/101002/ace.84
Wolters, C. A. (1998). Self-regulated learning and college students' regulation of motivation. Journal of Educational Psychology, 90, 224-235. doi: 10.1037/0022-0618.104.22.168
Wolters, C. A., & Hussain, M. (2015). Investigating grit and its relations with college students' self-regulated learning and academic achievement. Metacognition and Learning, 10(3), 293-311. doi: http://dx.doi.org/10.1007/s11409-014-9128-9
Zajacova, A., Lynch, S. M., & Espenshade, T. J. (2005). Self-efficacy, stress, and academic success in college. Research in Higher Education, 46(6), 677-706. doi: 10.1007/s11162-004-4139-z
Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25(1), 82-91.
DAVID N. WARDEN
CHARLSIE A. MYERS
College of Coastal Georgia
|Printer friendly Cite/link Email Feedback|
|Author:||Warden, David N.; Myers, Charlsie A.|
|Publication:||College Student Journal|
|Date:||Sep 22, 2017|
|Previous Article:||Change of academic major: The influence of broad and narrow personality traits.|
|Next Article:||Negative automatic thoughts, emotional intelligence and demographical different variables affecting university students.|