"Where will I belong more?": The role of belonging comparisons between STEM fields in high school girls' STEM interest.

In trying to understand women's underrepresentation in STEM (Science, Technology, Engineering, Mathematics), most existing research focuses on one STEM-field or collapses across all STEM-fields. However, these fields differ vastly in female representation: women tend to be most strongly underrepresented in technological and computer science university majors and to a lesser extent in mathematics and chemistry, while they are less underrepresented in biological sciences. To understand this variability, we examine how girls in the process of making higher education choices compare different STEM-fields to each other. We draw upon dimensional comparison theory, which argues that educational motivation involves intra-individual comparisons of achievement across school subjects. However, previous research has shown that a focus on achievement in STEM is not enough, anticipated belonging in a STEM-field plays a pivotal role in interest in pursuing that field. Consistent with this, we examined participants' comparisons of anticipated belonging across STEM-fields. A sample of 343 high school girls in STEM-focused university tracks completed a survey on their anticipated belonging and interest in pursuing different STEM majors. Latent Profile Analysis resulted in 3 profiles, showing different belonging comparison patterns across STEM-fields. Examining these comparisons-both within and across profiles-showed how girls felt pushed away from certain STEM-fields and pulled toward others. The findings suggest that for interest in pursuing specific STEM-fields it is not just about the level of anticipated belonging within that STEM-field, but just as much about the level of anticipated belonging in comparison to another STEM-field. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11218-021-09663-6.

Profiling first-year students in STEM programs based on autonomous motivation and academic self-concept and relationship with academic achievement.

The low success rate of first-year college students in Science, Technology, Engineering, and Mathematics (STEM) programs has spurred many academic achievement studies in which explanatory factors are studied. In this study, we investigated from a person-oriented perspective whether different motivational and academic self-concept profiles could be discerned between male and female first-year college students in STEM and whether differences in early academic achievement were associated with these student groups. Data on autonomous motivation, academic self-concept, and early academic achievement of 1,400 first-year STEM college students were collected. Cluster analyses were used to distinguish motivational profiles based on the relative levels of autonomous motivation and academic self-concept for male and female students. Differences in early academic achievement of the various profiles were studied by means of ANCOVA. Four different motivational profiles were discerned based on the dimensions of autonomous motivation (A) and academic self-concept (S): students scoring high and respectively low on both dimensions (HA-HS or LA-LS), and students scoring high on one dimension and low on the other (HA-LS or LA-HS). Also gender differences were found in this study: male students with high levels of academic self-concept and autonomous motivation had higher academic achievement compared to male students with low levels on both motivational dimensions. For female students, motivational profiles were not associated with academic achievement. The findings partially confirm the internal and external validity of the motivational theories underpinning this study and extend the present insights on identifying subgroup(s) of at risk students in contemporary STEM programs at university level.

Adaptive feedback and student behaviour in computer-assisted instruction.

OBJECTIVES: Two packages of adaptive computer-assisted instruction (CAIs), both offering questions and subsequent feedback, were compared in terms of amount of feedback offered, learning efficiency and appreciation. Feedback was either barely more than knowledge of result ('minimal') or consisted of a complete additional learning path ('elaborate'). The CAIs differed in the way the type of feedback given was triggered. METHODS: A total of 97 Bachelor of Science students were stratified based on a pre-test before the experiment and were allocated randomly to two groups to receive either a 'programme-assessed' or a 'student-assessed' treatment. In the former, the feedback provided by the CAI (either elaborate or minimal) was completely determined by the objective correctness of the student's response. In the student-assessed treatment, elaborate feedback was provided only to students who stated explicitly that they did not know the answer. Afterwards, students completed a post-test and an appreciation questionnaire. RESULTS: Both CAIs resulted in a significant learning effect, but overall the effect was significantly higher in the programme-assessed treatment. Students using the student-assessed CAI hardly ever used the 'I don't know' option and therefore received mostly minimal feedback. There was no difference in students' appreciation between treatments. An interaction between the learner's prior knowledge and treatment was found: for learners with a high level of prior knowledge the programme-assessed treatment resulted in significantly higher learning effectiveness compared with the student-assessed treatment, whereas for learners with a low level of prior knowledge there was no significant difference. CONCLUSIONS: Our results demonstrate that learners achieved a significantly higher learning effectiveness in the programme-assessed treatment in which the received feedback was fully controlled by the correctness of the answer, compared with the student-assessed treatment. In the latter, students hardly ever admitted to not knowing the answer. Therefore, student-initiated use of the tool in a student-assessed CAI requires to be improved.