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Amy Adair – ProQuest LLC, 2024
Developing models, using mathematics, and constructing explanations are three practices essential for science inquiry learning according to education reform efforts, such as the Next Generation Science Standards (NGSS Lead States, 2013). However, students struggle with these intersecting practices, especially when developing and interpreting…
Descriptors: Artificial Intelligence, Evaluation Methods, Scaffolding (Teaching Technique), Mathematics
Li, ZhaoBin; Yee, Luna; Sauerberg, Nathaniel; Sakson, Irene; Williams, Joseph Jay; Rafferty, Anna N. – International Educational Data Mining Society, 2020
Digital educational technologies offer the potential to customize students' experiences and learn what works for which students, enhancing the technology as more students interact with it. We consider whether and when attempting to discover how to personalize has a cost, such as if the adaptation to personal information can delay the adoption of…
Descriptors: Educational Technology, Technology Uses in Education, Student Needs, Student Characteristics
Crupi, Vincenzo; Nelson, Jonathan D.; Meder, Björn; Cevolani, Gustavo; Tentori, Katya – Cognitive Science, 2018
Searching for information is critical in many situations. In medicine, for instance, careful choice of a diagnostic test can help narrow down the range of plausible diseases that the patient might have. In a probabilistic framework, test selection is often modeled by assuming that people's goal is to reduce uncertainty about possible states of the…
Descriptors: Information Theory, Cognitive Processes, Information Seeking, Probability
Goksu, Idris; Islam Bolat, Yusuf – Review of Education, 2021
In this meta-analysis, the aim is to determine the overall effect of the ARCS (attention, relevance, confidence, satisfaction) model of motivation on students' academic achievement, motivation, attention, relevance, confidence and satisfaction. Additionally, the effect of the model is analysed according to the learning environment in which the…
Descriptors: Models, Student Motivation, Academic Achievement, Attention
Gendreau, Audrey – ProQuest LLC, 2014
Efficient self-organizing virtual clusterheads that supervise data collection based on their wireless connectivity, risk, and overhead costs, are an important element of Wireless Sensor Networks (WSNs). This function is especially critical during deployment when system resources are allocated to a subsequent application. In the presented research,…
Descriptors: Computer Simulation, Computer Networks, Costs, Risk
Clement, Benjamin; Roy, Didier; Oudeyer, Pierre-Yves; Lopes, Manuel – Journal of Educational Data Mining, 2015
We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by students, taking into account the limited time and motivational resources. At a given point in time, the system proposes to the students the activity which makes them progress faster. We introduce two…
Descriptors: Learning Activities, Intelligent Tutoring Systems, Models, Teaching Methods
Street, Garrett M.; Laubach, Timothy A. – American Biology Teacher, 2013
We provide a 5E structured-inquiry lesson so that students can learn more of the mathematics behind the logistic model of population biology. By using models and mathematics, students understand how population dynamics can be influenced by relatively simple changes in the environment.
Descriptors: Biology, Population Growth, Science Instruction, Computer Simulation
Li, Nan; Cohen, William W.; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2013
The order of problems presented to students is an important variable that affects learning effectiveness. Previous studies have shown that solving problems in a blocked order, in which all problems of one type are completed before the student is switched to the next problem type, results in less effective performance than does solving the problems…
Descriptors: Teaching Methods, Teacher Effectiveness, Problem Solving, Problem Based Learning
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
Fiedler, Klaus; Kareev, Yaakov – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2006
Adaptive decision making requires that contingencies between decision options and their relative assets be assessed accurately and quickly. The present research addresses the challenging notion that contingencies may be more visible from small than from large samples of observations. An algorithmic account for such a seemingly paradoxical effect…
Descriptors: Sampling, Decision Making, Computer Simulation, Models

Baker, Bruce D.; Richards, Craig E. – Journal of Education Finance, 2002
Discusses the use of dynamic systems modeling in the field of school finance policy. Provides detailed description of the use of computer-based systems-modeling simulations to New Jersey school finance equity problems. (Contains 37 references.) (PKP)
Descriptors: Computer Simulation, Educational Finance, Elementary Secondary Education, Financial Policy
Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use

Gruska, Jozef – Education and Computing, 1993
Describes shortcomings of computer science/engineering education and explains a new focus on informatics. Highlights include simulation, visualization, algorithmization, design of information processing models, parallel computing, a history of informatics, informatics versus physics and mathematics, and implications for education. (51 references)…
Descriptors: Algorithms, Computer Science Education, Computer Simulation, Higher Education
Barnes, Tiffany, Ed.; Chi, Min, Ed.; Feng, Mingyu, Ed. – International Educational Data Mining Society, 2016
The 9th International Conference on Educational Data Mining (EDM 2016) is held under the auspices of the International Educational Data Mining Society at the Sheraton Raleigh Hotel, in downtown Raleigh, North Carolina, in the USA. The conference, held June 29-July 2, 2016, follows the eight previous editions (Madrid 2015, London 2014, Memphis…
Descriptors: Data Analysis, Evidence Based Practice, Inquiry, Science Instruction

Journal of Computers in Mathematics and Science Teaching, 1989
Reviews seven papers presented by the Technical Committee for Education of the International Federation for Information Processing. Topics consider: undergraduate physics, high school optics, a mathematics lab with LOGO, simulations and modeling for secondary education, computer assisted instruction with biology, and mathematics of tomorrow. (MVL)
Descriptors: Cognitive Processes, College Science, Computer Assisted Instruction, Computer Simulation
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