Abstract: This research article investigates the potential of using blockchain technology and machine learning algorithms to enhance the understanding of causal grading in educational settings. Traditional grading systems often lack transparency and fail to capture the complexity and nuances involved in the process of assessing student performance. By leveraging blockchain’s decentralized and immutable nature, combined with the power of machine learning algorithms, we propose a novel approach that can provide a comprehensive understanding of the causal relationships between various assessment factors and final grades.
Our study aims to explore the following research questions: How can blockchain technology be utilized to enhance the transparency and accuracy of grading systems? How can machine learning algorithms be leveraged to identify and understand the complex causal relationships between assessment factors and final grades? Through an interdisciplinary approach, we aim to bridge the gap between artificial intelligence and educational research, offering innovative solutions to improve the current grading practices.
To achieve our research goals, we will employ a mixed-methods research design. We will collect both quantitative and qualitative data from a diverse sample of educational institutions, including K-12 schools and higher education institutions. The quantitative data will include students’ assessment scores, demographic information, and other relevant variables, while the qualitative data will be collected through interviews and focus groups with educators and administrators.
Additionally, we will develop a machine learning model that uses the collected data to uncover hidden patterns and causal relationships between different assessment factors and final grades. This model will be trained on the blockchain platform to ensure data security and transparency throughout the grading process. The results of our study will provide insights into the potential of blockchain and machine learning in revolutionizing grading practices and enable educators to make informed decisions based on a deeper understanding of the nuances involved in student assessment.
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