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Global warming is a major concern, and tackling it via education is crucial. The purpose of this study is to investigate the impact of combining the Science, Technology, Engineering, and Mathematics (STEM) approach with the Project-based Learning (PjBL) model on students' learning outcomes and science process skills in global warming topics. The study, which used a one-group pretest-posttest design, collected data from 33 grade XI students via essay questions and observation sheets to assess learning outcomes and science process skills. Critical indicators of science process abilities were observing, classifying, interpreting, predicting, applying concepts, communicating, and forming conclusions. Students worked on a STEM-based project to build a flood-resistant house over the course of five meetings (including the pretest and post-test). The findings revealed considerable improvements: learning outcomes increased by 0.6 (medium category), while science process skills improved by 0.7 (high category). Observations revealed that pupils' scientific process skills were excellent. The Wilcoxon test revealed a significant difference between students' learning outcomes and science process abilities before and after the intervention, with a Sig (2-tailed) value of 0.000. The study indicated that combining the STEM method with the PjBL model improves students' learning outcomes and scientific process abilities. However, the small sample size implies that the results may not be generalizable. The consequence is that integrating the STEM method and the PjBL model provides an innovative option in physics education, improving learning outcomes and scientific process skills.

While visual literacy has been identified as a foundational skill in life science education, there are many challenges in teaching and assessing biomolecular visualization skills. Among these are the lack of consensus about what constitutes competence and limited understanding of student and instructor perceptions of visual literacy tasks. In this study, we administered a set of biomolecular visualization assessments, developed as part of the BioMolViz project, to both students and instructors at multiple institutions and compared their perceptions of task difficulty. We then analyzed our findings using a mixed-methods approach. Quantitative analysis was used to answer the following research questions: (1) Which assessment items exhibit statistically significant disparities or agreements in perceptions of difficulty between instructors and students? (2) Do these perceptions persist when controlling for race/ethnicity and gender? and (3) How does student perception of difficulty relate to performance? Qualitative analysis of open-ended comments was used to identify predominant themes related to visual problem solving. The results show that perceptions of difficulty significantly differ between students and instructors and that students’ performance is a significant predictor of their perception of difficulty. Overall, this study underscores the need to incorporate deliberate instruction in visualization into undergraduate life science curricula to improve student ability in this area. Accordingly, we offer recommendations to promote visual literacy skills in the classroom.

Aging is a natural process with varying effects. As we grow older, our bodies become more susceptible to aging-associated diseases. These diseases, individually or collectively, lead to the formation of distinct aging phenotypes. Identifying these aging phenotypes and understanding the complex interplay between coexistent diseases would facilitate more personalized patient management, a better prognosis, and a prolonged lifespan. Many studies distinguish between successful aging and frailty. However, this simple distinction fails to reflect the diversity of underlying causes. In this study, we sought to establish the underlying causes of frailty and determine the patterns in which these causes converge to form aging phenotypes. We conducted a comprehensive geriatric examination, cognitive assessment, and survival analysis of 2,688 long-living adults (median age = 92 years). The obtained data were clustered and used as input data for the Aging Phenotype Calculator, a multiclass classification model validated on an independent dataset of 96 older adults. The accuracy of the model was assessed using the receiver operating characteristic curve and the area under the curve. Additionally, we analyzed socioeconomic factors that could contribute to specific aging patterns. We identified five aging phenotypes: non-frailty, multimorbid frailty, metabolic frailty, cognitive frailty, and functional frailty. For each phenotype, we determined the underlying diseases and conditions and assessed the survival rate. Additionally, we provided management recommendations for each of the five phenotypes based on their distinct features and associated challenges. The identified aging phenotypes may facilitate better-informed decision-making. The Aging Phenotype Calculator (ROC AUC = 92%) may greatly assist geriatricians in patient management.