Although participation rates of women in science, technology, engineering, and mathematics (STEM) are continually improving, low rates are still an issue in many countries. While previous studies found positive effects of online mentoring for increasing girls’ interests in STEM, research concerning explanatory mechanisms is lacking. We found evidence that in a 1‐year online mentoring program for girls (age: M = 13.82 years, N = 998) in STEM, suitably implemented mentoring (operationalized via relationship quality in a program that systematically incorporates structural and organizational aspects of successful mentoring) was associated with positive changes in the learning environments of the mentees (as indicated by their increasing educational capital). These positive changes were associated with increases in the program‐related mentoring outcomes STEM activities and elective intentions in STEM. Finally, we found that suitably implemented online mentoring was indirectly related to an increase in these two mentoring outcomes via an increase in educational capital. These results indicate the importance of paying close attention to learning environments when planning interventions.

Online mentoring can be useful for supporting girls in science, technology, engineering, and mathematics (STEM). Yet, little is known about the differential effects of various online mentoring formats. We examine the general and relative effectiveness of three online mentoring formats, one‐on‐one mentoring, many‐to‐many group mentoring, and a hybrid form of the two. All three formats were implemented in different years in the Germany‐wide online‐only mentoring program, CyberMentor, whose platform enables communication and networking between up to 800 girls (in grades 5–13) and 800 women (STEM professionals) each year. We combined longitudinal mentee data for all first‐year participants (N = 4017 girls, M_age = 14.15 years) from 9 consecutive mentoring years to evaluate and compare the three mentoring formats. Overall, all formats effected comparable increases in mentees’ STEM activities and certainty about career plans. However, mentees’ communication behavior and networking behavior on the mentoring platform differed between the three formats. Mentees in the hybrid mentoring format showed the most extensive STEM‐related communication and networking on the platform. We also analyzed the explanatory contributions of STEM‐related communication and networking on interindividual differences in the developmental trajectories of mentees’ STEM activities, elective intentions in STEM, and certainty about career plans, for each format separately.

The article proposes a revised mental energy hypothesis for the emergence of the g factor. Charles E. Spearman interpreted the g factor as a kind of domain-general mental energy. Nowadays, it is known that the energy currency of organisms is the chemical energy transporter adenosine triphosphate (ATP). ATP is produced by complex metabolic processes (mainly by glucose oxidation), and most of it in the human brain is used for neural signaling. There are substantial individual differences in the metabolic properties of the brain, which lead to different levels of energy production. It is thereby proposed to place more emphasis on individual differences of metabolic functions in intelligence research. Neuroscientific findings suggest that increased brain metabolism and, therefore, higher energy-production levels facilitate better performance on different cognitive tasks. These findings are not in conflict with the refined neural efficiency hypothesis. In addition, building on Dennis Garlick’s proposal that neural plasticity is the core process underlying the development of the g factor, it is illustrated why ATP is crucial for neural plasticity. Taken together, the direct effects of level of energy production on cognitive performance and the relations with neural plasticity suggest an important role in the emergence of the g factor.

Intelligence is a well-supported predictor of school achievement, however, it refers only to the cognitive facet of learning determinants. The aim of this article is to contrast the predictive properties of two comprehensive concepts developed within the actiotope model of giftedness (AMG) with that of intelligence. These concepts are educational and learning capital (ELC) and the domain impact level of activities (DILA), which were contrasted with a nonverbal intelligence measure (Raven’s standard progressive matrices). We investigated N = 90 fourth graders from regular classes in a German urban area. Results showed that achievement in German language was better predicted by ELC and DILA than by intelligence, whereas mathematical achievement was predicted by intelligence and educational and learning capital to similar degrees. The AMG concepts also showed incremental predictive power over intelligence. These findings suggest that ELC and, with some limitation, DILA (a) are well suited for predictions of school achievement and (b) capture valuable different aspects of the learning system than intelligence measures. Implications for education and research are discussed.