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The dataset comprises career literature and information about course programs in Uganda which can be used to provide career guidance for individuals. It was created through web scraping of official websites of public universities and from data from the Onet databases. It can be used by career advisors and mentors, university intake officials, AI researchers whose field is education and job market prediction, and also supports research in NLP and LLM fine-tuning.
Learning to read is a fundamental skill for academic success and a key tool for enabling individuals to fully participate in society. However, approximately 10% of children face difficulties in acquiring this skill due to dyslexia, a neurodevelopmental disorder that affects reading and writing acquisition. Dyslexia is often underdiagnosed, and affected children are typically identified only after experiencing academic failure, despite the fact that the condition is unrelated to general intelligence.
This paper demonstrates how digital signal processing (DSP) and artificial intelligence (AI) together form a unifying platform across diverse applications and educational contexts. Through case studies in biomedicine, rehabilitation, sport activities, and renewable energy, students are encouraged to collect their own datasets, explore the mathematical foundations of signal processing, and validate results through practical implementations.
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.
¢This study delves into the connections between green ELT, DEIB, virtual reality, mediation, life skills, and task-based teaching, learning, and assessment in the context of sustainable and inclusive education. The study emphasizes the significance of incorporating ecological concepts into language instruction, advocating for diversity, fairness, and inclusivity in learning environments, and using virtual reality technology to augment language acquisition.
In primary education in China, mathematics, science, and Chinese are commonly considered as the core subjects. This emphasis is primarily due to their significance in providing a strong foundation for students' overall academic development in their whole life. Mathematics cultivates logical thinking, problem-solving skills, and numerical proficiency, which are essential in various disciplines. Science education fosters scientific literacy, critical thinking, and an understanding of the natural world.
The dataset file contains all the relevant data for this paper, including original text data, labels, and statistical information, which is utilized for training, testing, and validation of the proposed models or arguments. Additionally, there is a question bank file that comprises all test questions, filtered test data, and annotated result data after testing. This data is used to evaluate the performance of the models or methods proposed in the paper.
