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Jedediyah Williams – Mathematics Teacher: Learning and Teaching PK-12, 2024
Email filters classify new messages as either spam or not spam based on word frequency, syntax, and metadata. A "classifier" is an algorithm that maps input data into categories based on distinguishing characteristics, or "features." Features can be raw data or attributes derived from that data. "Feature engineering"…
Descriptors: Classification, Engineering, Numbers, Algorithms
Joemari Olea; Kevin Carl Santos – Journal of Educational and Behavioral Statistics, 2024
Although the generalized deterministic inputs, noisy "and" gate model (G-DINA; de la Torre, 2011) is a general cognitive diagnosis model (CDM), it does not account for the heterogeneity that is rooted from the existing latent groups in the population of examinees. To address this, this study proposes the mixture G-DINA model, a CDM that…
Descriptors: Cognitive Measurement, Models, Algorithms, Simulation
Axel Langner; Lea Sophie Hain; Nicole Graulich – Journal of Chemical Education, 2025
Often, eye-tracking researchers define areas of interest (AOIs) to analyze eye-tracking data. Although AOIs can be defined with systematic methods, researchers in organic chemistry education eye-tracking research often define them manually, as the semantic composition of the stimulus must be considered. Still, defining appropriate AOIs during data…
Descriptors: Organic Chemistry, Science Education, Eye Movements, Educational Research
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)
Jinsook Lee; Yann Hicke; Renzhe Yu; Christopher Brooks; René F. Kizilcec – British Journal of Educational Technology, 2024
Large language models (LLMs) are increasingly adopted in educational contexts to provide personalized support to students and teachers. The unprecedented capacity of LLM-based applications to understand and generate natural language can potentially improve instructional effectiveness and learning outcomes, but the integration of LLMs in education…
Descriptors: Artificial Intelligence, Technology Uses in Education, Equal Education, Algorithms
Jean-Paul Fox – Journal of Educational and Behavioral Statistics, 2025
Popular item response theory (IRT) models are considered complex, mainly due to the inclusion of a random factor variable (latent variable). The random factor variable represents the incidental parameter problem since the number of parameters increases when including data of new persons. Therefore, IRT models require a specific estimation method…
Descriptors: Sample Size, Item Response Theory, Accuracy, Bayesian Statistics
Yin Kiong Hoh – American Biology Teacher, 2025
Artificial intelligence (AI) encompasses the science and engineering behind creating intelligent machines capable of tasks that typically rely on human intelligence, such as learning, reasoning, decision-making, and problem-solving. By analyzing vast amounts of data, identifying patterns, and making predictions that were once impossible, AI has…
Descriptors: Artificial Intelligence, Biological Sciences, Computer Software, Algorithms
Jeffrey Ehme – PRIMUS, 2024
The Miller-Rabin test is a useful probabilistic method for finding large primes. In this paper, we explain the method in detail and give three variations on this test. These variations were originally developed as student projects to supplement a course in error correcting codes and cryptography.
Descriptors: Probability, Numbers, Coding, Algorithms
Kylie L. Anglin – Annenberg Institute for School Reform at Brown University, 2025
Since 2018, institutions of higher education have been aware of the "enrollment cliff" which refers to expected declines in future enrollment. This paper attempts to describe how prepared institutions in Ohio are for this future by looking at trends leading up to the anticipated decline. Using IPEDS data from 2012-2022, we analyze trends…
Descriptors: Validity, Artificial Intelligence, Models, Best Practices
Andrew Kwok-Fai Lui; Sin-Chun Ng; Stella Wing-Nga Cheung – Interactive Learning Environments, 2024
The technology of automated short answer grading (ASAG) can efficiently process answers according to human-prepared grading examples. Computer-assisted acquisition of grading examples uses a computer algorithm to sample real student responses for potentially good examples. The process is critical for optimizing the grading accuracy of machine…
Descriptors: Grading, Computer Uses in Education, Educational Technology, Artificial Intelligence
Juraj Hromkovic; Regula Lacher – Informatics in Education, 2025
The design of algorithms is one of the hardest topics of high school computer science. This is mainly due to the universality of algorithms as solution methods that guarantee the calculation of a correct solution for all potentially infinitely many instances of an algorithmic problem. The goal of this paper is to present a comprehensible and…
Descriptors: Algorithms, Computer Science Education, High School Students, Teaching Methods
Harold Doran; Testsuhiro Yamada; Ted Diaz; Emre Gonulates; Vanessa Culver – Journal of Educational Measurement, 2025
Computer adaptive testing (CAT) is an increasingly common mode of test administration offering improved test security, better measurement precision, and the potential for shorter testing experiences. This article presents a new item selection algorithm based on a generalized objective function to support multiple types of testing conditions and…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Algorithms
Samuel B. Allan; Peter K. Dunn; Robert G. McDougall – International Journal of Mathematical Education in Science and Technology, 2024
In this note we demonstrate two instances where matrix multiplication can be easily verified. In the first setting, the matrix product appears as matrix element concatenation, and in the second, the product coincides with matrix addition. General proofs for some results are provided with a more complete description for 2×2 matrices. Suggested for…
Descriptors: Mathematics Instruction, Teaching Methods, Multiplication, Addition
Donna Poade; Russell M. Crawford – Brock Education: A Journal of Educational Research and Practice, 2024
The emergence of artificial intelligence (AI) in academia has prompted various debates on the uses, threats, and limitations of tools that can create text for numerous academic purposes. Critics argue that these advancements may provide opportunities for cheating and plagiarism and even replace the art of writing entirely. To reclaim the…
Descriptors: Academic Language, Artificial Intelligence, Algorithms, Personal Autonomy
Thin-Yin Leong; Nang-Laik Ma – INFORMS Transactions on Education, 2024
This paper develops a spreadsheet simulation methodology for teaching simulation and performance analysis of priority queues with multiple servers, without resorting to macros, add-ins, or array formula. The approach is made possible by a "single overtaking" simplifying assumption under which any lower-priority customer may be passed in…
Descriptors: Spreadsheets, Simulation, Teaching Methods, Computer Science Education
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