processing speed

The current study examined the effectiveness of the educational Lego “More Math” on the mathematical progress, spatial understanding, working memory, and processing speed of students in female first grade of elementary school. female first-grade students. The research employed a quasi-experimental design incorporating a pre-test and post-test framework, with the inclusion of a control group. The population of this study consisted of female first-grade students studying in the schools of Tehran District 14 in the academic year 2022-2023. Among the available schools in District 14, Mihrab Girl’s Elementary School was selected as the sample. Then, 30 students were selected by convenience sampling and randomly assigned to the experimental (15 students) and control (15 students) groups. The data were collected by the Wechsler Intelligence Scale for Children, Fifth Edition (WISC-V), which consisted of ten sub-tests comprising five sub-scales for fluency, language comprehension, visual-spatial reasoning, processing speed, and working memory, and the teacher-made math test. The Lego More to Math educational protocol consisted of weekly training sessions over the course of 15 weeks, with each session lasting for 60 minutes. The data were analyzed using univariate and multivariate analyses of covariance by SPSS26.The analysis revealed significant differences between groups for spatial perception (F = 1.48, p = 0.00, η² = 0.36), working memory (F = 12.13, p = 0.00, η² = 0.32), processing speed (F = 18.16, p = 0.00, η² = 0.42), and math progress (F = 5.78, p = 0.00, η² = 0.29, test power = 0.9), with non-significant pre-test results (p = 0.88) and significant group differences (F = 11.36, p = 0.00). The study demonstrated that the Lego "More to Math" educational program significantly enhanced first graders' mathematical progress, spatial understanding, working memory, and processing speed. These findings suggested that incorporating structured, play-based learning tools like Lego into early education effectively boosted cognitive and mathematical skills in young students

  Background: Quantum computing has brought in all new paradigm for computational processing providing unparallel ability for data analysis. Considering worldwide data production is expected to exceed 180 trillion zettabytes by 2025 the utilization of the conventional computing framework hampers the real-time processing of data. People consider quantum computing, which uses principles of quantum mechanics to solve problems 100 and 1,000 times faster than classical computing.   Objective: The article looks at quantum computing and its relevance to real time data analytics to determine its relevance, hence its impact, by the year 2025. It is worthwhile to emphasize the comparison of quantum algorithms with traditional approaches to dealing with extensive, data-centered workloads in various fields.   Methods: A comparison was made on quantum versus classical computing algorithms based on criteria such as, the flow rate, precision, and flexibility. Data sets provided by the finance stream, including real-time stock analysis, supply chain and logistics, genomic sequencing from the healthcare domain were used. Over 10 million simulation experiments were performed to gain trends and insights into the operational problems for quantum simulation.   Results: The study establishes differences in the efficiencies of these two approaches, with quantum algorithms speeding up particular tasks as much as a hundred times higher than classical algorithms and almost 15% of the error rate being decreased if quantum error correction modes were used. In scalability tests it was shown that quantum systems could process data sets larger than 10 terabytes with little slowdown, compared to a classical system, which reduced efficiency by as much as 30%. However, in present day quantum hardware, processing the capability is limited and problems arise with regards the error correction protocol.   Conclusion: Quantum computing, on the other hand, has an unconventional prospect of real-time data analytics to operate at high efficiency and big scale on data-bound concerns. However, much progress is required in the way of bettering coherence times and reducing exacting error rates, crucial advances for total realization of quantum potentialities by 2025