This study investigated the combined predictive validity of intelligence and personality factors on multiple measures of academic achievement. Students in a college of higher education in the Netherlands (N0137) completed a survey that measured intelligence, the Big Five personality traits, motivation, and four specific personality traits. Student performance was measured with grade point average (GPA) and time to graduation, as well as with five specific performance measures: regular exams, skills training, team projects, internships, and a written thesis. Results show that 33% of the variance in GPA and 30% of the variance in time to graduation can be explained by combining intelligence, personality, and motivational predictors. Conscientiousness is the best predictor across a broad spectrum of academic achievement measures and explains five times as much variance in GPA as does intelligence. The practical implications are that institutes of higher education should collect personality data on students at the outset and then help students accordingly. Highly conscientious students who are organized and internally motivated might potentially be offered more challenging honours programs with corresponding special commendations on their diplomas, whereas students who score low on conscientious would receive more structure through student study groups, frequent deadlines, shorter assignments, group assignments, clearly defined learning goals, and less second chances for passing examinations.Keywords Academic success . Higher education . Intelligence . Motivation . Personality traits . Predictive validity Harris (1940) andCattell (1965) long ago noted the importance of personality factors for predicting academic achievement. Harris (1940) discussed the importance of persistence, in the guise of effort, and concluded that the essential factors for scholastic achievement were: Eur J Psychol Educ (2012) (a) ability, otherwise known as intelligence or scholastic aptitude, (b) effort, also known as drive or degree of motivation, and (c) personal, economic, social, and academic circumstances. Years later, Cattell (1965) suggested that, for university students who had already been selected on intelligence, personality and motivation would be just as important for predicting academic achievement. Recent research has shown that personality accounts for variance in academic achievement over and above intelligence (Bratko et al. 2006;Gilles and Bailleux 2001;Noftle and Robins 2007;Poropat 2009), and that personality may have even more predictive power than intelligence at the post-secondary levels of education (Conard 2006;Di Fabio and Busoni 2007;Furnham and Chamorro-Premuzic 2004;Furnham et al. 2003;Petrides et al. 2005).O 'Conner and Paunonen (2007) recently reviewed the studies concerning how well intelligence and personality factors predicted academic achievement at the post-secondary level and reiterated the earlier recommendations of Harris and Cattell. They offered a template for subsequent research and ca...
Background: The corona pandemic has forced higher education (HE) institutes to transition to online learning, with subsequent implications for student wellbeing.Aims: This study explored influences on student wellbeing throughout the first wave of the corona crisis in the Netherlands by testing serial mediation models of the relationships between perceived academic stress, depression, resilience, and HE support.Methods: The Covid-19 International Student Wellbeing Study (C19 ISWS) was used, with a total sample of 2,480 higher education students studying at InHolland Universities of Applied Sciences in the Netherlands. Student subgroups were created, so that students with low and high perceived academic stress could be assessed, in addition to depressed and non-depressed students. Predictive model fit was tested using Macro PROCESS.Results: A significant serial mediation model for the total student sample was revealed, including protective mediating effects of resilience and HE support on the positive direct effect of perceived academic stress on depression. At subgroup level, significant (partial) predictive effects of resilience on depression scores were noted. A partial serial effect between resilience and HE support was found for students with low perceived stress levels, whereas a parallel partial mediation model was present among highly academically stressed students. Regarding non-depressed students, a full parallel mediation model was found, whereas the model for depressed students inadequately explained the data.Conclusions: Overall, resilience and HE support mediate the predictive effect of academic stress on depressive symptoms among students. In addition, substantial differences in model fit arise when inspecting the students on a subgroup level. These findings contribute to the gap in knowledge regarding student wellbeing during the Covid-19 pandemic in the Netherlands, in addition to providing novel insights on student subgroup dynamics. While Covid-19 restrictions continue to demand online learning, student wellbeing may be enhanced overall by targeting resilience and increasing awareness and availability of HE support services. The current study also highlights the need for differential approaches when examining wellbeing for specific student groups.
BackgroundThe COVID-19 pandemic has forced higher education (HE) to shift to emergency remote teaching (ERT), subsequently influencing academic belonging and social integration, as well as challenging students' engagement with their studies.AimsThis study investigated influences on student engagement during ERT, based on student resilience. Serial mediation analyses were used to test the predictive effects between resilience, academic belonging, social integration, and engagement.MethodsThe Student Well-being Monitor (SWM 2021) was completed by 1332 HE students studying at Inholland University of Applied Sciences in the Netherlands. Predictive models were compared among students with low, normal, or high resilience using SPSS extension Macro PROCESS.ResultsA significant serial mediation model was found among all HE students, including positive mediating effects of academic belonging and social integration. More so, independent partial predictive effects of academic belonging and social integration on engagement were also present. Assessment of student resilience profiles revealed substantial differences between predictive models. For low resilience students, serial mediation was present and included the largest partial predictive effect from social integration compared to other groups. For highly resilient students, mediation via academic belonging was found, including the strongest partial and indirect effects compared to other groups.ConclusionsOverall, academic belonging and social integration positively mediate the effect of resilience on engagement in addition to demonstrating independent positive predictive effects. Inspection of resilience profiles reveals substantial model fit differences, suggesting use of different engagement strategies between student groups. Findings contribute to understanding of HE student engagement during ERT in the Netherlands and provide novel insight on the mechanisms between resilience and engagement. While ERT continues to be required, engagement may be enhanced by stimulating academic belonging for all students generally, but low resilience students could be best served by additionally targeting social integration and resilience.
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