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Adrianne L. Jenner; Pamela M. Burrage – International Journal of Mathematical Education in Science and Technology, 2024
Mathematics provides us with tools to capture and explain phenomena in everyday biology, even at the nanoscale. The most regularly applied technique to biology is differential equations. In this article, we seek to present how differential equation models of biological phenomena, particularly the flow through ion channels, can be used to motivate…
Descriptors: Cytology, Mathematical Models, Prediction, Equations (Mathematics)
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Zong, Zheng; Schunn, Christian D. – International Journal of Computer-Supported Collaborative Learning, 2023
Online peer feedback has proven to be practically useful for instructors and to be useful for learning, especially for the feedback provider. Because students can vary widely in skill level, some research has explored matching reviewer and author by performance level. However, past research on the impacts of reviewer matching has found little…
Descriptors: Computer Mediated Communication, Feedback (Response), Peer Evaluation, Biology
Scott Anthony Gigante – ProQuest LLC, 2021
In recent years, modern technologies have enabled the collection of exponentially larger quantities of data in the biomedical domain and elsewhere. In particular, the advent of single-cell genomics has allowed for the collection of datasets containing hundreds of thousands of cells measured in tens of thousands of dimensions. This rapid expansion…
Descriptors: Visualization, Data, Algorithms, Artificial Intelligence
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Line Have Musaeus; Deborah Tatar; Peter Musaeus – Journal of Biological Education, 2024
Computational modelling is widely used in biological science. Therefore, biology students need to learn computational modelling. However, there is a lack of evidence about how to teach computational modelling in biology and what the effects are on student learning. The purpose of this intervention-control study was to investigate how knowledge in…
Descriptors: Computation, Models, High School Students, Biology
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Ariely, Moriah; Nazaretsky, Tanya; Alexandron, Giora – International Journal of Artificial Intelligence in Education, 2023
Machine learning algorithms that automatically score scientific explanations can be used to measure students' conceptual understanding, identify gaps in their reasoning, and provide them with timely and individualized feedback. This paper presents the results of a study that uses Hebrew NLP to automatically score student explanations in Biology…
Descriptors: Artificial Intelligence, Algorithms, Natural Language Processing, Hebrew
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Sung, Rou-Jia; Swarat, Su L.; Lo, Stanley M. – Journal of Biological Education, 2022
Exams constitute the predominant form of summative assessment in undergraduate biology education, with the assumption that exam performance should reflect student conceptual understanding. Previous work highlights multiple examples in which students can answer exam problems correctly without the corresponding conceptual understanding. This…
Descriptors: Biology, Problem Solving, Undergraduate Students, Scientific Concepts
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Herman, Gabor T.; Vitanyi, Paul M. B. – American Mathematical Monthly, 1976
Growth functions involved in mathematical models for biological development are discussed using the algebra of polynomials and matrices. (SD)
Descriptors: Algebra, Algorithms, Biology, College Mathematics
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Stencel, John E. – American Biology Teacher, 1991
A real world sample of actual data that students can use to see the application of the Hardy-Weinberg law to a real population is provided. The directions for using a six-step algorithmic procedure to determine Hardy-Weinberg percentages on the data given are described. (KR)
Descriptors: Algorithms, Biology, Genetics, Problem Solving
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Thomson, Norman; Stewart, James – Journal of Biological Education, 1985
Explains an algorithm which details procedures for solving a broad class of genetics problems common to pre-college biology. Several flow charts (developed from the algorithm) are given with sample questions and suggestions for student use. Conclusions are based on the authors' research (which includes student interviews and textbook analyses).…
Descriptors: Algorithms, Biology, Genetics, Learning Strategies
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Stencel, John E. – Journal of College Science Teaching, 1992
Explains how a simple three-step algorithm can aid college students in solving synapse transmission problems. Reports that all of the students did not completely understand the algorithm. However, many learn a simple working model of synaptic transmission and understand why an impulse will pass across a synapse quantitatively. Students also see…
Descriptors: Algorithms, Anatomy, Biology, College Science
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Stewart, Jim; Dale, Michael – Science Education, 1989
Investigates high school students' understanding of the physical relationship of chromosomes and genes as expressed in their conceptual models and in their ability to manipulate the models to explain solutions to dihybrid cross problems. Describes three typical models and three students' reasoning processes. Discusses four implications. (YP)
Descriptors: Algorithms, Biology, Concept Formation, Fundamental Concepts
Cipra, Barry – What's Happening in the Mathematical Sciences, 1993
This document consists of the first two volumes of a new annual serial devoted to surveying some of the important developments in the mathematical sciences in the previous year or so. Mathematics is constantly growing and changing, reaching out to other areas of science and helping to solve some of the major problems facing society. Volumes 1 and…
Descriptors: Algorithms, Biology, Classification, Coding
Zuckerman, June T. – 1992
Various researchers have associated meaningful problem solving with methods guided directly by a conceptual knowledge base. By contast, a meaningless solving course, or sequence of operations, is essentially independent of the solver's conceptual understanding of the problem under consideration. This paper is the first to document a meaningless,…
Descriptors: Algorithms, Biology, Cognitive Processes, Conceptual Tempo