Education
A novel ultra-low-voltage (ULV) Dual-EdgeTriggered (DET) flip-flop based on the True-Single-PhaseClocking (TSPC) scheme is presented in this paper. Unlike Single-Edge-Triggering (SET), Dual-Edge-Triggering has the advantage of operating at the half-clock rate of the SET clock. We exploit the TSPC principle to achieve the best energy-efficient figures by reducing the overall clock load (only to 8 transistors) and register power while providing fully static, contention-free functionality to satisfy ULV operation.
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This dataset is crawled from educational websites, focusing on the subject of high school physics, covering multiple-choice, single-choice and fill-in-the-blank questions, totaling tens of thousands of high-quality Chinese test questions. Each question is equipped with detailed text descriptions and intuitive image descriptions, and stored in json format to ensure easy parsing and efficient utilization of the data. Each question contains key information such as label, question stem, question type, answer, knowledge point and image path.
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The data provided the raw coding data for systematic review and meta-analysis of AR in higher education. Regarding the coding data for systematic review, the coding scheme comprises five parts: basic information, disciplines, technology features, instructional design, and research results. Regarding the coding data for meta-analysis, we code the effect size and several moderating variables of the selected experimental research studies.
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To download this dataset without purchasing an IEEE Dataport subscription, please visit: https://zenodo.org/records/13896353
Please cite the following paper when using this dataset:
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MOOCCube is an open data repository for natural language processing, knowledge graphs, data mining and other researchers who are interested in massive open online courses(MOOCs). It contains 706 MOOC courses, 38,181 videos, 114,563 concepts, and 199,199 real MOOC users. This data source also contains a large-scale Concept Graph and related academic papers as additional resources for further utilization.
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To download the dataset without purchasing an IEEE Dataport subscription, please visit: https://zenodo.org/records/13738598
Please cite the following paper when using this dataset:
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This dataset abstract presents findings from a comprehensive survey conducted to investigate the impact of social media on high school students in Bangladesh. With the pervasive influence of social media platforms in contemporary society, particularly among the younger demographic, understanding its effects on adolescent behavior, mental health, academic performance, and social interactions is of paramount importance.
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This study investigates whether the ingredients listed on restaurant menus can provide insights into a city's socioeconomic status. Using data from an online food delivery system, the study compares menu items with local education rates and rental prices. A machine learning model is developed to predict menu prices based on ingredients and socioeconomic factors. An efficiency metric is proposed to cluster restaurants to address autocorrelation, comparing ingredient averages to socioeconomic indicators.
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Contributions: This study offers valuable insights to MOOC designers about user priorities associated with web accessibility principles for designing web content that provides higher levels of user experience, motivating course completions. Background: MOOCs improve access to quality education. Despite policy support and more involvement by leading educational institutions worldwide, poor course completion rates undermine the objectives for MOOC diffusion.
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