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Babak Mahjour; Andrew McGrath; Andrew Outlaw; Ruheng Zhao; Charles Zhang; Tim Cernak – Journal of Chemical Education, 2023
Data science is becoming a mainstay in research. Despite this, very few STEM graduates matriculate with basic formal training in programming. The current lesson plan was developed to introduce undergraduates studying chemistry or biology to chemoinformatics and data science in medicinal chemistry. The objective of this lesson plan is to introduce…
Descriptors: Undergraduate Students, Chemistry, Biology, Information Science
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Kamali N. Sripathi; Rosa A. Moscarella; Matthew Steele; Rachel Yoho; Hyesun You; Luanna B. Prevost; Mark Urban-Lurain; John Merrill; Kevin C. Haudek – Journal of Mixed Methods Research, 2024
Assessing student knowledge based on their writing using traditional qualitative methods is time-consuming. To improve speed and consistency of text analysis, we present our mixed methods development of a machine learning predictive model to analyze student writing. Our approach involves two stages: first an exploratory sequential design, and…
Descriptors: Artificial Intelligence, Mixed Methods Research, Student Writing Models, Biology
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Pablo Antonio Archila; Brigithe Tatiana Ortiz; Anne-Marie Truscott de Mejía; Jorge Molina – Information and Learning Sciences, 2024
Purpose: In November 2022, the commercial company, OpenAI, launched ChatGPT. Since then, university students have rapidly become regular users of this artificial intelligence (AI) platform. One reason for this is the powerful capability of this generative AI tool to produce textual content, which in many cases, is almost indistinguishable from…
Descriptors: Foreign Countries, Bilingual Education, English, Spanish
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Bertolini, Roberto; Finch, Stephen J.; Nehm, Ross H. – Journal of Science Education and Technology, 2021
High levels of attrition characterize undergraduate science courses in the USA. Predictive analytics research seeks to build models that identify at-risk students and suggest interventions that enhance student success. This study examines whether incorporating a novel assessment type (concept inventories [CI]) and using machine learning (ML)…
Descriptors: Evaluation Methods, Scores, Artificial Intelligence, Grade Prediction
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Terence G. Favero – Advances in Physiology Education, 2024
The advent of artificial intelligence (AI)-powered language models presents new opportunities and challenges in education. By teaching students how to craft prompts that elicit insightful responses, faculty can scaffold activities where AI acts as a supplemental resource to amplify critical thinking and support student learning. Ongoing dialogue…
Descriptors: Artificial Intelligence, Teaching Methods, Physiology, Technology Uses in Education
Gregory J. Crowther; Usha Sankar; Leena S. Knight; Deborah L. Myers; Kevin T. Patton; Lekelia D. Jenkins; Thomas A. Knight – Journal of Microbiology & Biology Education, 2023
The biology education literature includes compelling assertions that unfamiliar problems are especially useful for revealing students' true understanding of biology. However, there is only limited evidence that such novel problems have different cognitive requirements than more familiar problems. Here, we sought additional evidence by using…
Descriptors: Science Instruction, Artificial Intelligence, Scoring, Molecular Structure
International Association for Development of the Information Society, 2012
The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…
Descriptors: Academic Achievement, Academic Persistence, Academic Support Services, Access to Computers