Personality, Intelligence, and Academic Achievement: Charting their Developmental Interplay

Abstract

Although intelligence and personality traits have long been recognized as key predictors of students’ academic achievement, little is known about their longitudinal and reciprocal associations. Here, we charted the developmental interplay of intelligence, personality (Big Five) and academic achievement in 3,880 German secondary school students, who were assessed four times between the ages 11 and 14 years (i.e., in grade 5, 6, 7, and 8). We fitted random-intercept cross-lagged panel models (RI-CLPs) to investigate reciprocal within-person associations between (a) academic achievement and intelligence, (b) academic achievement and personality, as well as (c) intelligence and personality. The results revealed negative within-person associations between Conscientiousness and Extraversion assessed at the first wave of measurement and intelligence assessed at the second wave. None of the reciprocal personality-achievement associations attained statistical significance. Academic achievement and intelligence showed reciprocal within-person relations, with the strongest coefficients found for achievement longitudinally predicting intelligence. Our work contributes to developmental theorizing on interrelations between personality, intelligence, and academic achievement, as well as to within-person conceptualizations in personality research.

A psychological network analysis to specify predictions of fraction subtopics on algebra subtopics in an intelligent tutoring system

“…However, due to the anonymization of the data, we could not investigate the role of individual di erences with respect to learner characteristics, as no participant-specific information was available. Therefore, future research may expand on our study by considering individual learner characteristics such as motivation, personality, cognitive abilities, prior academic achievement, socioeconomic status, or math anxiety (e.g., Bardach et al, 2023;Meyer et al, 2023) and their e ects on learning trajectories within ITSs (Hilz, Guill, Rolo , Aldrup, & Köller, 2023;Hilz, Guill, Rolo , Sommerho , & Aldrup, 2023). In addition, it would be illuminating to combine the process learning data used in our study with students' self-reported learning behavior and emotional dynamics during learning, as well as other behavioral assessments (e.g., emotion recognition systems, eye-tracking, heart rate variability, EEG data) to dig even deeper into the details of mathematics learning with ITSs.…”