Education

This dataset contains simulated records for 3,000 students, generated for the purpose of evaluating fairness in predicted grading models. The dataset includes decile rankings based on historical performance, predicted grades, and demographic attributes such as socioeconomic status, school type, gender, and ethnicity. The data was created using controlled randomization techniques and includes noise to reflect real-world prediction uncertainty. While entirely synthetic, the dataset is designed to mimic key structural patterns relevant to algorithmic fairness and educational inequality.

This dataset contains simulated records for 3,000 students, generated for the purpose of evaluating fairness in predicted grading models. The dataset includes decile rankings based on historical performance, predicted grades, and demographic attributes such as socioeconomic status, school type, gender, and ethnicity. The data was created using controlled randomization techniques and includes noise to reflect real-world prediction uncertainty. While entirely synthetic, the dataset is designed to mimic key structural patterns relevant to algorithmic fairness and educational inequality.

BitRecover OST to PST Converter is a trustworthy and powerful software tool specifically created to export offline OST files to importable PST format. This sophisticated application allows users to recover and move emails, contacts, calendars, and tasks from inaccessible or broken OST files to Microsoft Outlook while maintaining the original data structure and integrity. With its easy-to-use interface and powerful capabilities, the software is the perfect solution for users and organizations who want to recover access to their precious email data.

El dataset desarrollado integra múltiples dimensiones relevantes para el análisis académico y profesional de los estudiantes.

The Software Engineering Skillset Dataset focuses on three critical areas: Soft Skills, Life Skills, and Technical Skills. These components are essential for assessing and predicting the software engineering competencies of computer science students. Data for the dataset was collected through a carefully designed questionnaire, targeting students enrolled in software engineering courses at higher educational institutions across Kerala, India. Over two thousand students participated in the survey, which used a dedicated assessment quiz with fifteen questions to evaluate each skill category.

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.

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.

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.

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:

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.