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Abdennabi Lakrim; Mohamed Chergui; Bouazza El Wahbi – Journal of Education and Learning (EduLearn), 2025
This study is a contribution to efforts to promote practices for dealing with the difficulties encountered by learners in probabilistic modeling situations. We attempt to elucidate as precisely as possible the types of difficulties that secondary school students face in the process of modeling with probability tools. By referring to a large and…
Descriptors: Probability, Difficulty Level, Correlation, Classification
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Aydogdu, Seyhmus – Journal of Educational Computing Research, 2021
Student modeling is one of the most important processes in adaptive systems. Although learning is individual, a model can be created based on patterns in student behavior. Since a student model can be created for more than one student, the use of machine learning techniques in student modeling is increasing. Artificial neural networks (ANNs),…
Descriptors: Mathematical Models, Artificial Intelligence, Bayesian Statistics, Learning Processes
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Jiang, Shiyan; Nocera, Amato; Tatar, Cansu; Yoder, Michael Miller; Chao, Jie; Wiedemann, Kenia; Finzer, William; Rosé, Carolyn P. – British Journal of Educational Technology, 2022
To date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K-12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created,…
Descriptors: High School Students, Data, Artificial Intelligence, Mathematical Models
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Lonneke Boels; Enrique Garcia Moreno-Esteva; Arthur Bakker; Paul Drijvers – International Journal of Artificial Intelligence in Education, 2024
As a first step toward automatic feedback based on students' strategies for solving histogram tasks we investigated how strategy recognition can be automated based on students' gazes. A previous study showed how students' task-specific strategies can be inferred from their gazes. The research question addressed in the present article is how data…
Descriptors: Eye Movements, Learning Strategies, Problem Solving, Automation
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Ural, Alattin – Journal of Educational Issues, 2020
The purpose of this research is to classify the mathematical modelling problems produced by pre-service mathematics teachers in terms of the number of variables and to determine the mathematical modelling skills and mathematical skills used in solving the problems in each class. The current study is a qualitative research and the data was analyzed…
Descriptors: Classification, Mathematical Models, Mathematics Teachers, Preservice Teachers
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E. Taranto; G. Colajanni; A. Gobbi; M. Picchi; A. Raffaele – International Journal of Mathematical Education in Science and Technology, 2024
Operations Research (OR) is a branch of applied mathematics that deals with optimization problems arising from different real contexts. The solving process of its problems is based on the construction and resolution of mathematical models, showing the possible connections between mathematics and the real world. Nevertheless, OR is not typically…
Descriptors: Problem Solving, Cooperative Learning, Information Technology, Mathematics Instruction
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Pérez Martínez, Helen Mariel; Cuevas-Vallejo, Carlos A.; Islas Ortiz, Erasmo; Orozco-Santiago, José – North American Chapter of the International Group for the Psychology of Mathematics Education, 2022
In this paper, we present the development of an investigation on the promotion of covariational reasoning in high school students (14-15 years old) in Mexico. The study consists of designing and applying a sequence of didactic activities that simulate a real situation virtually. The activities are organized through a Hypothetical Learning…
Descriptors: Thinking Skills, Mathematics Instruction, High School Students, Learning Trajectories
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Zieffler, Andrew; Justice, Nicola; delMas, Robert; Huberty, Michael D. – Journal of Statistics and Data Science Education, 2021
Statistical modeling continues to gain prominence in the secondary curriculum, and recent recommendations to emphasize data science and computational thinking may soon position algorithmic models into the school curriculum. Many teachers' preparation for and experiences teaching statistical modeling have focused on probabilistic models.…
Descriptors: Mathematical Models, Thinking Skills, Teaching Methods, Statistics Education
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Ryo, Masahiro; Jeschke, Jonathan M.; Rillig, Matthias C.; Heger, Tina – Research Synthesis Methods, 2020
Research synthesis on simple yet general hypotheses and ideas is challenging in scientific disciplines studying highly context-dependent systems such as medical, social, and biological sciences. This study shows that machine learning, equation-free statistical modeling of artificial intelligence, is a promising synthesis tool for discovering novel…
Descriptors: Artificial Intelligence, Case Studies, Biology, Research Reports
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Matayoshi, Jeffrey; Uzun, Hasan; Cosyn, Eric – International Educational Data Mining Society, 2022
Knowledge space theory (KST) is a mathematical framework for modeling and assessing student knowledge. While KST has successfully served as the foundation of several learning systems, recent advancements in machine learning provide an opportunity to improve on purely KST-based approaches to assessing student knowledge. As such, in this work we…
Descriptors: Knowledge Level, Mathematical Models, Learning Experience, Comparative Analysis
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Raj, Gaurav; Mahajan, Manish; Singh, Dheerendra – International Journal of Web-Based Learning and Teaching Technologies, 2020
In secure web application development, the role of web services will not continue if it is not trustworthy. Retaining customers with applications is one of the major challenges if the services are not reliable and trustworthy. This article proposes a trust evaluation and decision model where the authors have defined indirect attribute, trust,…
Descriptors: Trust (Psychology), Models, Decision Making, Computer Software
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Gruver, Nate; Malik, Ali; Capoor, Brahm; Piech, Chris; Stevens, Mitchell L.; Paepcke, Andreas – International Educational Data Mining Society, 2019
Understanding large-scale patterns in student course enrollment is a problem of great interest to university administrators and educational researchers. Yet important decisions are often made without a good quantitative framework of the process underlying student choices. We propose a probabilistic approach to modelling course enrollment…
Descriptors: Models, Course Selection (Students), Enrollment, Decision Making
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Lamprianou, Iasonas – Educational and Psychological Measurement, 2018
It is common practice for assessment programs to organize qualifying sessions during which the raters (often known as "markers" or "judges") demonstrate their consistency before operational rating commences. Because of the high-stakes nature of many rating activities, the research community tends to continuously explore new…
Descriptors: Social Networks, Network Analysis, Comparative Analysis, Innovation
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Lebedev, Arseniy; Krupa, Tatsiana; Rezakov, Maksim – International Journal of Environmental and Science Education, 2016
According to the survey of experts, the association of non-cognitive skills in groups according to their classification as object of evaluation leads to the fact that one group includes very different skills - both in volume and in the way of they identify and assess. So, the purpose of the research is the development of mathematical model of…
Descriptors: Mathematical Models, Mathematics Skills, Metacognition, Classification
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dos Santos, Regina Antunes Teixeira; Gerling, Cristina Capparelli; de Bortoli, Álvaro Luiz – Music Education Research, 2017
A short piano piece was prepared by undergraduate and graduate piano students (N = 15) without guidance from their piano teachers. Temporal parameters were analyzed in several time frames, from a phrase-to-phrase level to an interonset interval. At a macro level, local tempo was shown to be a crucial parameter that divided the students into two…
Descriptors: Undergraduate Students, Musical Instruments, Music Education, Graduate Students
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