This study examined the effects of an eight-week morphological intervention on early literacy outcomes among first-grade Arabic-speaking students in northern Israel. A quasi-experimental pretest-post test design was implemented with 140 Arabic-speaking first graders, drawn from both typically developing and struggling reader populations. Students were assigned to experimental and control conditions and assessed on six measures: morphological pattern awareness, root awareness, syllable segmentation, phonemic segmentation, single-word reading, and reading comprehension. Across all six outcome measures, the experimental group showed substantially greater gains than controls, a pattern that held for both reader groups, though the intervention effects ran consistently larger among those who had entered the study already struggling. The strongest improvements were recorded in single-word reading and morphological pattern awareness. Notably, gains extended to lexical items instantiating trained patterns that had not appeared in instruction, indicating transfer beyond what item-specific learning alone would predict. The findings make a case that morpho-orthographic pattern knowledge is both teachable and consequential at the very onset of literacy acquisition, with effects that reach word-level decoding and early comprehension alike. The results indicate clear instructional implications, explicit pattern-based morphological teaching appears well-suited to early Arabic literacy curricula, and particularly so for learners whose reading development is already at risk.
Aligned with United Nations (UN) Sustainable Development Goal 13 (SDG-13: Climate Action), this study examined levels of climate change awareness, beliefs, and pro-environmental behavior among high school students in Israel. The sample consisted of 360 students, including 139 Environmental Science (ES) majors and 221 students from other science disciplines. Data were analyzed using descriptive statistics, MANOVA, hierarchical regression, mediation analysis, and Pearson correlations. Results indicated moderate awareness of climate change across majors, however no significant differences in knowledge, beliefs, or behavior were found between ES and other majors. Students' beliefs in their ability to mitigate climate change were generally strong and served as the primary predictor of pro-environmental behavior. In contrast, awareness exerted a positive, but weaker, influence. Gender differences were significant, with females scoring higher across all dimensions. Correlation analysis further revealed a strong positive relationship between beliefs and behavior as well as a moderate association between awareness and behavior. These findings suggest that affective and motivational factors, particularly students' sense of efficacy, appear to play a central role in translating knowledge into sustainable action. The study suggests that strengthening the belief-action link in climate education may be a productive focus for future research and curriculum development.
Developing higher-order thinking skills in general, and question-asking skills in particular, is considered a cornerstone for fostering critical and cognitive thinking skills. Question-asking is a key component in student engagement toward understanding complex scientific concepts. This study investigates two innovative dimensions in chemistry education: the integration of nanoscience and nanotechnology aspects into chemistry instruction, and its impact on development of question-asking skills among 11th-grade high school students. Using a quasi-experimental design, 120 students were divided equally into experimental and control groups. Both groups read a simplified scientific text on redox reactions; the control group’s text addressed redox reactions only, while the experimental group’s text had nanoscience aspects embedded within the same redox context. Students’ questions were evaluated by five judges using a validated four-level questioning hierarchy, ranging from factual to complex integrative knowledge. Results revealed significant differences in both the number and cognitive level of questions generated by the two groups. Students exposed to the nano-integrated text demonstrated markedly higher question-asking skills. In conclusion, integrating nanoscience into chemistry instruction was shown to be a powerful gateway to developing higher-order thinking, enabling students to ask more cognitively advanced questions, deepen their scientific understanding, and engage more meaningfully with scientific content.
Empirical evidence suggests that composite instructional designs implementing a problem-solving phase prior to the instruction phase (PS-I) are more effective than the reverse sequence (I-PS) in promoting conceptual knowledge and transfer. This is particularly true when the instruction phase builds on typical erroneous student solutions from the problem-solving phase, which makes the nature of the instruction phase crucial. Despite claims regarding the high effectiveness of the instruction phase, there is scant literature on this topic. Few studies have explored performance gains when students work individually during the instruction phase to compare erroneous examples—well-crafted, typical erroneous student solutions from the problem-solving phase—with worked examples consisting of step-by-step descriptions of the canonical solution, to detect and explain the errors in the erroneous examples. The current study examined whether (1) scaffolding these troubleshooting activities, (2) requiring students to compare their solutions from the problem-solving phase with worked examples to detect and explain their errors, or (3) scaffolding the self-diagnosis activities in (2) would differentially enhance the efficacy of the instruction phase. Twelve 8th-grade classes (261 students) completed a pretest/intervention/immediate posttest/delayed posttest on simple electric circuits. The intervention consisted of four PS-I sessions targeting different yet-to-be-learned accepted ideas. In the instruction phase of each session, students in each class were randomly assigned to four conditions in a 2 × 2 design, keeping the problem-solving phase identical across conditions. The findings indicated that the troubleshooting activities without scaffolding led to better learning outcomes. The instructional implications and directions for future research are discussed.
The increasing demand for intelligent, automated prototyping in digital manufacturing calls for frameworks that unite generative creativity with functional feasibility. The current diffusion-based prototypes do not support manufacturability-aware optimisation and computation-efficient design optimization. Proposed framework combines U-Net based DDPM with NSGA-II to achieve multi-objective optimization of quality, diversity and manufacturability. Traditional deep learning models like GANs and VAEs generate diverse geometries but lack optimization, while evolutionary algorithms (EAs) excel in refinement but fall short on creative synthesis. To overcome this, the present study proposes a novel hybrid framework integrating a Generative Diffusion Network (GDN) with NSGA-II, forming a closed-loop co-evolutionary design system. Implemented using Python, the model is trained on the SIP-17 industrial parts dataset, enhancing the generation process through a feedback loop that fine-tunes the generator with elite EA-selected designs. The proposed method achieves a 24% improvement in image fidelity (FID ↓ 0.196), a 12% increase in diversity (LPIPS ↑ 0.47), and a 28% boost in manufacturability score (↑ 0.87) compared to baseline models. Experimental results show better stability of the optimization and feasibility for industrial use than the current diffusion-based design approaches. This hybrid approach presents a scalable, high-performance design pipeline that advances AI-driven industrial prototyping in terms of quality, creativity, and practicality.
