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Direct linkVandana Onker; Krishna Kumar Singh; Hemraj Shobharam Lamkuche; Sunil Kumar; Vijay Shankar Sharma; Chiranji Lal Chowdhary; Vijay Kumar – Education and Information Technologies, 2025
Predicting academic performance in Educational Data Mining has been a significant research area. This involves utilizing machine learning techniques to analyze data from educational settings. Predicting student academic performance is a complex task due to the influence of multiple factors. This research uses supervised machine-learning approaches…
Descriptors: Foreign Countries, Artificial Intelligence, Academic Achievement, Grades (Scholastic)
Download full textVincentas Lamanauskas, Editor – International Baltic Symposium on Science and Technology Education, 2025
These proceedings contain papers of the 6th International Baltic Symposium on Science and Technology Education (BalticSTE2025) held in Šiauliai, Lithuania, June 16-19, 2025. This collection of BalticSTE25 articles presents 13 scientific articles and an introductory article on the topic of natural science and technology education. The articles…
Descriptors: Science Education, Technology Education, STEM Education, Curriculum Development
Komalavalli, K.; Hemalatha, R.; Dhanalakshmi, S. – Shanlax International Journal of Education, 2020
In the current scenario, Smart Phones are one of the Artificial Intelligence devices regularly used by the students. The study aimed to know the utility of the SmartPhones in their day to day life, most useful applications and the valid security features of the Smart Phones and its impact among the Students at Higher Education level. Primary data…
Descriptors: Artificial Intelligence, Telecommunications, Handheld Devices, College Students
