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Houssam El Aouifi; Mohamed El Hajji; Youssef Es-Saady – Education and Information Technologies, 2024
Dropout refers to the phenomenon of students leaving school before completing their degree or program of study. Dropout is a major concern for educational institutions, as it affects not only the students themselves but also the institutions' reputation and funding. Dropout can occur for a variety of reasons, including academic, financial,…
Descriptors: At Risk Students, Potential Dropouts, Identification, Influences
Xuandong Zhao – ProQuest LLC, 2024
The rapid advancement of powerful Large Language Models (LLMs), such as ChatGPT and Llama, has revolutionized the world by bringing new creative possibilities and enhancing productivity. However, these advancements also pose significant challenges and risks, including the potential for misuse in the form of fake news, academic dishonesty,…
Descriptors: Computational Linguistics, Intellectual Property, Artificial Intelligence, Productivity
Semiu Salawu; Jo Lumsden; Yulan He – International Journal of Bullying Prevention, 2022
A negative consequence of the proliferation of social media is the increase in online abuse. Bullying, once restricted to the playground, has found a new home on social media. Online social networks on their part have intensified efforts to tackle online abuse, but unfortunately, such is the scale of the problem that many young people are still…
Descriptors: Computer Mediated Communication, Bullying, Computer Software, Computational Linguistics

Kim, Sehwan; Wurster, Leslie; Williams, Charles; Hepler, Nancy – Journal of Drug Education, 1998
Presents a model for resource allocation of state-prevention funds based on the relative need for prevention services measured in terms of composite risk-factor index scores computed for each county. Suggests that funding be based on the resource-allocation algorithms rather than the suggested amount of funding computed for each county.…
Descriptors: Algorithms, Prevention, Programs, Resource Allocation

Kim, Sehwan; Wurster, Leslie; Williams, Charles; Hepler, Nancy – Journal of Drug Education, 1998
Discusses the development of an algorithm for an equitable distribution of state prevention funds to its substate jurisdiction based on the need for prevention services. Suggests that the final decision on the level of funding must be based on the selection of the schedule for resource allocation rather than the suggested amount or level of…
Descriptors: Algorithms, Case Studies, Predictor Variables, Prevention

Kim, Sehwan; Wurster, Leslie; Williams, Charles; Hepler, Nancy – Journal of Drug Education, 1998
The first of three articles that develop a framework for county-based prevention-resource-allocation algorithms based on the aggravated need for substance-abuse-prevention services estimated at the county level. Algorithm development is based on (1) statewide student drug survey, and (2) a set of social indicators routinely published by agencies…
Descriptors: Adolescents, Algorithms, Case Studies, Children