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Abstract This meta-analysis explores the impact of informal science education experiences (such as after-school programs, enrichment activities, etc.) on students' attitudes towards, and interest in, STEM disciplines (Science, Technology, Engineering, and Mathematics). The research addresses two primary questions: (1) What is the overall effect size of informal science learning experiences on students' attitudes towards and interest in STEM? (2) How do various moderating factors (e.g., types of informal learning experience, student grade level, academic subjects, etc.) impact student attitudes and interests in STEM? The studies included in this analysis were conducted within the United States in K-12 educational settings, over a span of thirty years (1992–2022). The findings indicate a positive association between informal science education programs and student interest in STEM. Moreover, the variability in these effects is contingent upon several moderating factors, including the nature of the informal science program, student grade level, STEM subjects, publication type, and publication year. Summarized effects of informal science education on STEM interest are delineated, and the implications for research, pedagogy, and practice are discussed. 

Abstract This study examined the dimensionality and effectiveness of the five categories Likert Scale of the framework for observing and categorizing instructional strategies (FOCIS), a survey that measures students' preference for learning activities in science instructions, developed by Tai et al. in 2012. The data included 6546 students from 3rd to 12th grade including 4 school districts. The results show that the FOCIS survey has 7 dimensions measuring students’ preferences. This study only tests the effectiveness of the Competing dimension. Compared to the Partial Credit Model (PCM) model and Rasch model, condensing down the categories to dichotomous items fits the data better. The AIC and BIC decreased, and the infit outfit improved on the Rasch model. 

The increasing use of machine learning and Large Language Models (LLMs) opens up opportunities to use these artificially intelligent algorithms in novel ways. This article proposes a methodology using LLMs to support traditional deductive coding in qualitative research. We began our analysis with three different sample texts taken from existing interviews. Next, we created a codebook and inputted the sample text and codebook into an LLM. We asked the LLM to determine if the codes were present in a sample text provided and requested evidence to support the coding. The sample texts were inputted 160 times to record changes between iterations of the LLM response. Each iteration was analogous to a new coder deductively analyzing the text with the codebook information. In our results, we present the outputs for these recursive analyses, along with a comparison of the LLM coding to evaluations made by human coders using traditional coding methods. We argue that LLM analysis can aid qualitative researchers by deductively coding transcripts, providing a systematic and reliable platform for code identification, and offering a means of avoiding analysis misalignment. Implications of using LLM in research praxis are discussed, along with current limitations. 

Abstract Background Capturing measures of students’ attitudes toward science has long been a focus within the field of science education. The resulting interest has led to the development of many instruments over the years. There is considerable disagreement about how attitudes should be measured, and especially whether students’ attitudes toward science can or should be measured unidimensionally, or whether separate attitude dimensions or subscales should be considered. When it is agreed upon that the attitudes toward science construct should be measured along separate subscales, there is no consensus about which subscales should be used. Methods A streamlined version of the modified Attitudes Towards Science Inventory (mATSI), a widely used science measurement instrument, was validated for a more diverse sample as compared to the original study (Weinburgh and Steele in Journal of Women and Minorities in Science and Engineering 6:87–94, 2000). The analytical approach used factor analyses and longitudinal measurement invariance. The study used a sample of 2016 self-reported responses from 6 and 7th grade students. The factor analysis elucidated the factor structure of students’ attitudes toward science, and some modifications were made in accordance with the results. Measurement invariance analysis was used to confirm the stability of the measure. Results Our results support that the subscales, anxiety toward science and value and enjoyment of science , are two factors and stable over time. Conclusions Our results suggest that our proposed modified factor structure for students’ attitudes toward science is reliable, valid, and appropriate for use in longitudinal studies. This study and its resulting streamlined mATSI survey could be of value to those interested in studying student engagement and measuring middle-school students' attitudes toward science. 

STEM education programs are often formulated with a "hands-on activities" focus across a wide array of topics from robotics to rockets to ecology. Traditionally, the impact of these programs is based on surveys of youth on program-specific experiences or the youths’ interest and impressions of science in general. In this manuscript, we offer a new approach to analyzing science programming design and youth participant impact. The conceptual framework discussed here concentrates on the organization and analysis of common learning activities and instructional strategies. We establish instrument validity and reliability through an analysis of validity threats and pilot study results. We conclude by using this instrument in an example analysis of a STEM education program.