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Eyüp Yurt – International Society for Technology, Education, and Science, 2024
This study addresses the opportunities presented by AI applications in education and the ethical issues brought about by this technology. AI in education holds excellent potential in personalized learning, automated assessment and feedback, and monitoring and analyzing student performance. However, using these technologies also raises ethical…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Ethics
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Joshua Bumanlag; Adrian Angelo Abelarde – International Society for Technology, Education, and Science, 2024
This paper presents an improved Distributed Genetic Algorithm (DGA) that surpasses the classic genetic algorithm (GA) in terms of both efficiency and effectiveness when it comes to optimizing faculty scheduling systems. The proposed Differential Evolution Genetic Algorithm (DGA) combines DE with chaotic mapping and asynchronous communication to…
Descriptors: Algorithms, Faculty Workload, School Schedules, Computer Uses in Education
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Parian Haghighat; Denisa Gandara; Lulu Kang; Hadis Anahideh – Grantee Submission, 2024
Predictive analytics is widely used in various domains, including education, to inform decision-making and improve outcomes. However, many predictive models are proprietary and inaccessible for evaluation or modification by researchers and practitioners, limiting their accountability and ethical design. Moreover, predictive models are often opaque…
Descriptors: Prediction, Learning Analytics, Multivariate Analysis, Regression (Statistics)
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Oscar Clivio; Avi Feller; Chris Holmes – Grantee Submission, 2024
Reweighting a distribution to minimize a distance to a target distribution is a powerful and flexible strategy for estimating a wide range of causal effects, but can be challenging in practice because optimal weights typically depend on knowledge of the underlying data generating process. In this paper, we focus on design-based weights, which do…
Descriptors: Evaluation Methods, Causal Models, Error of Measurement, Guidelines
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Moran P. Lee; Abubakir Siedahmed; Neil T. Heffernan – Grantee Submission, 2024
Contextual multi-armed bandits have previously been used to personalize student support messages given to learners by supplying a model with relevant context about the user, problem, and available student supports. In this work, we propose using careful feature selection with relevant domain knowledge to improve the quality of student support…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Reinforcement
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Sami Baral; Eamon Worden; Wen-Chiang Lim; Zhuang Luo; Christopher Santorelli; Ashish Gurung; Neil Heffernan – Grantee Submission, 2024
The effectiveness of feedback in enhancing learning outcomes is well documented within Educational Data Mining (EDM). Various prior research have explored methodologies to enhance the effectiveness of feedback to students in various ways. Recent developments in Large Language Models (LLMs) have extended their utility in enhancing automated…
Descriptors: Automation, Scoring, Computer Assisted Testing, Natural Language Processing
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Amjad Almusaed; Asaad Almssad; Ammar K. Albaaj – International Society for Technology, Education, and Science, 2024
The impact of artificial intelligence (AI) on education and lifelong learning is a topic of significant importance as AI continues to change numerous sectors. This paper aims to critically examine AI's profound effect in these domains. The present research explores the ethical dilemmas and pedagogical approaches relevant to incorporating…
Descriptors: Ethics, Lifelong Learning, Artificial Intelligence, Computer Software