Effect of Embedded Careers Education in Science Lessons on Students’ Interest, Awareness, and Aspirations

In the last decades, secondary school students have indicated a low interest in science and a lack of awareness of authentic science careers that may impede their aspiration to work in science-related fields in future. To raise students’ aspirations, several studies used context-based approaches, but few integrated career aspects into the school curricula. Accordingly, this study aimed to promote lower secondary school students’ interest in and awareness of science careers by introducing science career-related scenarios reflected in a real-world context as embedded careers education in science lessons. In this study, we explored the effect of the interventions on students’ interest, awareness, and aspirations towards science careers using Estonian, Finnish, and German datasets. According to the results, the students participating in the project indicated a higher interest in science, aspiration towards science careers, and awareness of future careers than those who did not experience the embedded science career interventions. Also, the results showed that when students got more information about science careers at school, their interest in science was more easily transferred to their aspirations in science studies and careers. Thus, this result emphasizes the importance of fostering awareness of science careers at lower secondary schools in order to inspire young learners to engage in science studies and works in future.

Introducing Statistical Inference: Design of a Theoretically and Empirically Based Learning Trajectory

This paper comprises the results of a design study that aims at developing a theoretically and empirically based learning trajectory on statistical inference for 9th-grade students. Based on theories of informal statistical inference, an 8-step learning trajectory was designed. The trajectory consisted of two similar four step sequences: (1) experimenting with a physical black box, (2) visualizing distributions, (3) examining sampling distributions using simulation software, and (4) interpreting sampling distributions to make inferences in real -life contexts. Sequence I included only categorical data and Sequence II regarded numerical data. The learning trajectory was implemented in an intervention among 267 students. To examine the effects of the trajectory on students’ understanding of statistical inference, we analyzed their posttest results after the intervention. To investigate how the stepwise trajectory fostered the learning process, students’ worksheets during each learning step were analyzed. The posttest results showed that students who followed the learning trajectory scored significantly higher on statistical inference and on concepts related to each step than students of a comparison group (n = 217) who followed the regular curriculum. Worksheet analysis demonstrated that the 8-step trajectory was beneficial to students’ learning processes. We conclude that ideas of repeated sampling with a black box and statistical modeling seem fruitful for introducing statistical inference. Both ideas invite more advanced follow-up activities, such as hypothesis testing and comparing groups. This suggests that statistics curricula with a descriptive focus can be transformed to a more inferential focus, to anticipate on subsequent steps in students’ statistics education.