Implicit theories about intelligence and growth (personal best) goals: Exploring reciprocal relationships

Abstract

Background. There has been increasing interest in growth approaches to students' academic development, including value-added models, modelling of academic trajectories, growth motivation orientations, growth mindsets, and growth goals.

Improving the perception of intelligence: a short intervention for Secondary School Students.

“…It is also remarkable the increase in the fixed mindset in the control group. Other longitudinal studies that have analyzed mindsets or goal orientations have not observed such high variability (Martin, 2015, Martin andLiem, 2010), and the period between evaluations of these studies has been greater than ours. Therefore, it is possible that this variability, which was not significant, is due simply to chance, even so, we consider that it would be interesting to carry out longitudinal studies to know the path of the mindsets.…”

Creating a critical mass eliminates the effects of stereotype threat on women's mathematical performance

“…Such environmental cues may relay a message to women that their ability to succeed in mathematics is limited by group membership, namely their gender (Dweck, , ; Good et al ., ). Indeed, this is an important consideration in view of research indicating that a fixed‐ability mindset may have a deleterious and long‐term effect on educational outcomes (Verniers & Martinot, ; c.f., also Martin, ). Although single‐sex classroom initiatives may have a positive effect by alleviating women's apprehensions about confirming gender‐related stereotypes in the eyes of outgroup members (Picho & Stephens, ; Titze, Jansen, & Heil, ), they may also have a paradoxical effect on mindset.…”

Growth trajectories of mathematics achievement: Longitudinal tracking of student academic progress

“…Whereas previous studies tended to investigate growth in terms of average change across time for groups or cohorts, sustained intra‐individual growth deserves special attention from an educational perspective (Anderman et al ., ; Martin, , ; Martin & Liem, ; Murayama et al ., ). Recent developments in statistical methods, including hierarchical linear modelling (HLM), latent growth modelling (LGM), and multidimensional scaling applied to longitudinal profile analysis (LPAMS), facilitated studies of intra‐individual, as well as interindividual, growth (Anderman et al ., ; Grimm & Ram, ; Muthén & Khoo, ; Shin, , ; Shin et al ., ).…”