Exploring Learning Patterns: A Review of Clustering in Data-Driven Pedagogy

Educational institutes amass and retain extensive amounts of data including records of student attendance, test scores, exam results, and performance statistics. Extracting insights from this data can provide valuable information to educators and policymakers. The rapid expansion of educational data underscores the need for sophisticated algorithms to process such vast quantities of information. This challenge led to the emergence of the field of educational data mining (EDM). Clustering is a popular approach within EDM that can find hidden patterns in data. Numerous studies in EDM have concentrated on applying diverse clustering algorithms to educational attributes. This paper presents a comprehensive literature review focusing on 43 papers spanning between 2013 to 2023 on the use of clustering algorithms and their effectiveness within the realm of EDM. The review indicates that K-means clustering has been utilized extensively in the reviewed literature with 29 of the 43 reviewed papers using K-means clustering in their analysis. It was also uncovered that cluster-based analysis majorly focuses on analyzing student performance in a course or in a degree program closely followed by clustering students based on class of learners. Insights are deduced from the reviewed literature highlighting the focus of current research and potential directions for the future

Exploring Learning Patterns: A Review of Clustering in Data-Driven Pedagogy

Educational institutes amass and retain extensive amounts of data including records of student attendance, test scores, exam results, and performance statistics. Extracting insights from this data can provide valuable information to educators and policymakers. The rapid expansion of educational data underscores the need for sophisticated algorithms to process such vast quantities of information. This challenge led to the emergence of the field of educational data mining (EDM). Clustering is a popular approach within EDM that can find hidden patterns in data. Numerous studies in EDM have concentrated on applying diverse clustering algorithms to educational attributes. This paper presents a comprehensive literature review focusing on 43 papers spanning between 2013 to 2023 on the use of clustering algorithms and their effectiveness within the realm of EDM. The review indicates that K-means clustering has been utilized extensively in the reviewed literature with 29 of the 43 reviewed papers using K-means clustering in their analysis. It was also uncovered that cluster-based analysis majorly focuses on analyzing student performance in a course or in a degree program closely followed by clustering students based on class of learners. Insights are deduced from the reviewed literature highlighting the focus of current research and potential directions for the future

Advanced Vehicle Safety: A Prototype Circuit for Accident Prevention and Emergency Response

In contemporary society, the pursuit of robust accident prevention, detection, and reporting systems is paramount, particularly within the context of vehicular safety. This study introduces a meticulously designed prototype circuit, adaptable for deployment across diverse vehicle types. This intelligent circuitry endeavors to mitigate accidents and safeguard human lives by employing a multifaceted approach.The system's primary functions encompass the verification of seat belt usage and the assessment of alcohol consumption by the driver. Additionally, it incorporates a vibration sensor to detect accidents promptly. Complementing these features, the integration of GPS and GSM modules facilitates the rapid notification of emergency services, ensuring prompt assistance in the event of an accident.The core of this system is an Arduino microcontroller, orchestrating the interconnected components to process data and trigger actions based on predefined conditions. The circuit's performance has been rigorously tested, initially through simulation in Proteus software, and subsequently via real-world hardware implementation. Comparative analysis of software and hardware results lends insights into the system's functionality and reliability.The overarching objective of this study is to curtail accidents arising from intoxicated driving, unforeseen driver fatigue, and road obstructions. In instances of accidents, the electronic apparatus employed has the capacity to dispatch spontaneous and precise distress signals to law enforcement and medical personnel, thereby expediting casualty recovery and potentially saving lives. This research advances the fusion of technology and safety measures to augment road safety comprehensively.[6] Upadhyay, V., Gupta, S., Chaturvedi, S. and Singh, D. [2020], ‘Integrated accident prevention detectionand response system (iapdrs)’,International journal of engineering and advanced technology9(3), 2086–2089.[7] Vitkar, S. P., Banare, A. and Nadar, J. [2022], ‘Conceptual framework for accident detection and pre-vention’,Journal of Pharmaceutical Negative Resultspp. 7449–7455.[8] Wu, C., Zhang, P., Zhang, Z., Zheng, W., Xu, B., Wang, W., Yu, Z., Dai, X., Zhang, B. and Zang, K. [2023],‘Slip partitioning and crustal deformation patterns in the tianshan orogenic belt derived from gpsmeasurements and their tectonic implications’,Earth-Science Reviewsp. 104362.[9] Xu, X., Hu, X., Zhao, Y., Lü, X. and Aapaoja, A. [2023], ‘Urban short-term traffic speed prediction withcomplicated information fusion on accidents’,Expert Systems with Applicationsp. 119887.