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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. 

Artificial intelligence and recent advances in deep learning architectures, including transformer networks and large language models, change the way people think and act to solve problems. Software engineering, as an increasingly complex process to design, develop, test, deploy, and maintain large-scale software systems for solving real-world challenges, is profoundly affected by many revolutionary artificial intelligence tools in general and machine learning in particular. In this roadmap for artificial intelligence in software engineering, we highlight the recent deep impact of artificial intelligence on software engineering by discussing successful stories of applications of artificial intelligence to classic and new software development challenges. We identify the new challenges that the software engineering community has to address in the coming years to successfully apply artificial intelligence in software engineering, and we share our research roadmap toward the effective use of artificial intelligence in the software engineering profession, while still protecting fundamental human values. We spotlight three main areas that challenge the research in software engineering: the use of generative artificial intelligence and large language models for engineering large software systems, the need of large and unbiased datasets and benchmarks for training and evaluating deep learning and large language models for software engineering, and the need of a new code of digital ethics to apply artificial intelligence in software engineering. 

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.