This study aims to analyze students’ creative thinking skills in answering the problem-solving questions. This study employs qualitative design, involving 110 fifth graders in Malang Municipality and Regency as the subjects. The obtained data were analyzed using the descriptive-explorative approach. The findings reveal that the high-achievers in Mathematics showed good skills in the aspects of fluency and flexibility, but were still struggling in the novelty aspect.  The average-achievers showed good skills in flexibility aspects but were lacking in the fluency and novelty aspects. They showed an understanding of Mathematics problems but found it difficult to decide the solving strategies, and thus their answers were lacking in structure and less systematic. When solving a problem, the calculation made seemed rushing, was less careful, and frequented with trial and error strategy. The low-achievers showed difficulties in understanding the problems. Their answers were not systematic, not well-structured, and not detailed. This indicates that the low-achievers had not shown creative thinking skills in fluency, flexibility, and novelty aspects.

We investigated the effects of just-in-time guidance at various stages of inquiry learning by novice learners. Thirteen participants, randomly assigned to an intervention (n = 8) or control (n = 5) group, were observed as they learned about DC electric circuits using a web-based simulation. Just-in-time instructional prompts to observe, predict, explain, systematically test, collect evidence, and generate rules were strongly associated with diagnosing and correcting misconceptions, and constructing correct scientific concepts. Students’ repeated use of predictions, systematic testing, and evidence-coordinated reasoning often led to formulating new principles, generalizing from observed patterns, verifying comprehension, and experiencing “Aha!” moments. Just-in-time prompts helped learners manage embedded cognitive challenges in inquiry tasks, achieve a comprehensive understanding of the model represented in the simulation, and show significantly higher knowledge gain. Just-in-time prompts also promoted rejection of incorrect models of inquiry and construction of robust scientific mental models. The results suggest ways of customizing guidance to promote scientific learning within simulation environments.

Learning in high schools has used a lot of smartphone assistance to make it easier for students to understand the material explained by the teacher. However, with the many uses of smartphones in learning, of course, it must provide positive benefits to the ability of students, especially the science literacy ability. In this study, the objectives to be achieved are to examine how much the smartphone's usability in physics learning, examine students' science literacy and examine the effect of the smartphone’s usability on students' science literacy in physics learning. The method used in this study is a quantitative method with a research design used is a one-shot case study. The data on smartphone usability is obtained using a questionnaire, and science literacy of students is obtained through on physics science literacy test. The results of this study indicate that the smartphones usability and students' science literacy in physics learning are respectively in the high and medium categories with a respective percentage of 57.20% and 36.36%. The students' science literacy is influenced by the smartphone's usability in physics learning by 34.30%. These results indicate that smartphone usability by most students is very high, but has not been able to contribute optimally to students' science literacy. Therefore, special treatment is needed in utilizing the use of smartphones in physics learning so that students' science literacy can be optimized.

Analytical thinking is crucial for developing problem-solving, decision-making, and higher-order thinking skills. Many researchers have consistently developed learning management models to enhance students' analytical thinking, resulting in extensive knowledge but lacking clear systematic summaries. This study aims to: (a) explore the effect sizes and research characteristics influencing students' analytical thinking, and (b) compare the effect sizes of learning management models after adjusting for propensity score matching. In exploring 131 graduate research papers published between 2002 and 2021, the research utilized forms for recording research characteristics and questionnaires for assessing research quality for data collection. Effect sizes were calculated using Glass's method, while data analysis employed random effects, fixed effects, and regression meta-analysis methods. The findings indicate that (a) research on learning management models significantly impacts students' analytical thinking at a high level (d̅ = 1.428). Seven research characteristics, including year of publication, field of research, level, duration per plan, learning management process, measurement and evaluation, and research quality, statistically influence students' analytical thinking, and (b) after propensity score matching, learning through techniques such as KWL, KWL-plus, Six Thinking Hats, 4MAT, and Mind Mapping had the highest influence on students' analytical thinking. Recommendations for developing students' analytical thinking involve creating a learning management process that fosters understanding, systematic practical training, expanding thinking through collaborative exchanges, and assessments using learning materials and tests to stimulate increased analytical thinking.