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Alexey L. Voskov – International Journal of Mathematical Education in Science and Technology, 2024
QR decomposition is widely used for solving the least squares problem. However, existing materials about it may be too abstract for non-mathematicians, especially STEM students, and/or require serious background in linear algebra. The paper describes theoretical background and examples of GNU Octave compatible MATLAB scripts that give relatively…
Descriptors: Mathematics, Algorithms, Data Science, Mathematical Concepts
Zhang, Maoxin; Andersson, Björn – Educational Assessment, 2023
Process data from educational assessments enhance the understanding of how students answer cognitive items. However, effectively making use of these data is challenging. We propose an approach to identify solution patterns from operation sequences and response times by generating networks from process data and defining network features that…
Descriptors: Problem Solving, Network Analysis, Cognitive Processes, Mathematics
Catherine Barbara Acitelli – ProQuest LLC, 2022
The field of Cryptography is rapidly evolving, and the development of quantum computers is on the rise. Lattice-based cryptosystems are promising candidates for quantum resistance, and it is the responsibility of institutions to prepare students for related careers. Undergraduate students in mathematics and mathematics-adjacent fields can -- and…
Descriptors: Undergraduate Study, Curriculum Design, Technology, Coding
Julia Tomanova; Martin Vozar; Dasa Munkova – International Journal of Education in Mathematics, Science and Technology, 2024
The study focuses on the identification of relationships and/or rules between computational thinking (CT) concepts among the undergraduate students of Applied Informatics due to their attitudes towards mathematics. We analyze three CT concepts -- decomposition, pattern recognition, and algorithmic thinking. We assume that students who have a…
Descriptors: Computation, Thinking Skills, Student Attitudes, Undergraduate Students
Tamara Broderick; Andrew Gelman; Rachael Meager; Anna L. Smith; Tian Zheng – Grantee Submission, 2022
Probabilistic machine learning increasingly informs critical decisions in medicine, economics, politics, and beyond. To aid the development of trust in these decisions, we develop a taxonomy delineating where trust in an analysis can break down: (1) in the translation of real-world goals to goals on a particular set of training data, (2) in the…
Descriptors: Taxonomy, Trust (Psychology), Algorithms, Probability
Feng, Mingyu, Ed.; Käser, Tanja, Ed.; Talukdar, Partha, Ed. – International Educational Data Mining Society, 2023
The Indian Institute of Science is proud to host the fully in-person sixteenth iteration of the International Conference on Educational Data Mining (EDM) during July 11-14, 2023. EDM is the annual flagship conference of the International Educational Data Mining Society. The theme of this year's conference is "Educational data mining for…
Descriptors: Information Retrieval, Data Analysis, Computer Assisted Testing, Cheating
Landauer, Thomas K., Ed.; McNamara, Danielle S., Ed.; Dennis, Simon, Ed.; Kintsch, Walter, Ed. – Routledge, Taylor & Francis Group, 2007
"The Handbook of Latent Semantic Analysis" is the authoritative reference for the theory behind Latent Semantic Analysis (LSA), a burgeoning mathematical method used to analyze how words make meaning, with the desired outcome to program machines to understand human commands via natural language rather than strict programming protocols.…
Descriptors: Semantics, Natural Language Processing, Philosophy, Artificial Intelligence