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Christian Röver; David Rindskopf; Tim Friede – Research Synthesis Methods, 2024
The trace plot is seldom used in meta-analysis, yet it is a very informative plot. In this article, we define and illustrate what the trace plot is, and discuss why it is important. The Bayesian version of the plot combines the posterior density of [tau], the between-study standard deviation, and the shrunken estimates of the study effects as a…
Descriptors: Graphs, Meta Analysis, Bayesian Statistics, Visualization
Lafuente, Deborah; Cohen, Brenda; Fiorini, Guillermo; Garci´a, Agusti´n Alejo; Bringas, Mauro; Morzan, Ezequiel; Onna, Diego – Journal of Chemical Education, 2021
Machine learning, a subdomain of artificial intelligence, is a widespread technology that is molding how chemists interact with data. Therefore, it is a relevant skill to incorporate into the toolbox of any chemistry student. This work presents a workshop that introduces machine learning for chemistry students based on a set of Python notebooks…
Descriptors: Undergraduate Students, Chemistry, Electronic Learning, Artificial Intelligence
Ammar, Salwa; Kim, Min Jung; Masoumi, Amir H.; Tomoiaga, Alin – Decision Sciences Journal of Innovative Education, 2023
Over the past few years, academics have undertaken initiatives to bridge the gap between theory and practice in the ever-growing field of business analytics, including implementing real-life student projects in all shapes and forms. Every year since 2015, Manhattan College has invited student teams from across North America and elsewhere in the…
Descriptors: Business, Data Analysis, Business Administration Education, Intercollegiate Cooperation
Anderson, Joe S.; Williams, Susan K. – Decision Sciences Journal of Innovative Education, 2019
In this project, students asked and attempted to answer questions about themselves by collecting and analyzing data. With the prevalence of big data and business analytics, managers have data and quantitative information available more immediately than ever. However, managers need to understand how to use this information. In this project,…
Descriptors: Data Collection, Data Analysis, Student Projects, Experiential Learning
Gritsenko, Andrey – ProQuest LLC, 2017
Extreme Learning Machine (ELM) is a training algorithm for Single-Layer Feed-forward Neural Network (SLFN). The difference in theory of ELM from other training algorithms is in the existence of explicitly-given solution due to the immutability of initialed weights. In practice, ELMs achieve performance similar to that of other state-of-the-art…
Descriptors: Artificial Intelligence, Visualization, Regression (Statistics), Probability
Gordon, Sheldon P.; Gordon, Florence S. – International Journal for Technology in Mathematics Education, 2018
This article illustrates ways that dynamic software using some sophisticated techniques in Excel can be used to demonstrate fundamental ideas related to regression and correlation analysis to increase student understanding of the concepts and methods in elementary statistics courses and in courses at the college algebra/precalculus level that…
Descriptors: Visualization, Regression (Statistics), Correlation, Computer Software
McGee, Monnie – Journal of Statistics Education, 2019
In several sporting events, the winner is chosen on the basis of a subjective score. These sports include gymnastics, ice skating, and diving. Unlike for other subjectively judged sports, diving competitions consist of multiple rounds in quick succession on the same apparatus. These multiple rounds lead to an extra layer of complexity in the data,…
Descriptors: Data Use, Visualization, Interrater Reliability, Introductory Courses
Rudziewicz, Michael; Bossé, Michael J.; Marland, Eric S.; Rhoads, Gregory S. – International Journal for Mathematics Teaching and Learning, 2017
Humans possess a remarkable ability to recognise both simple patterns such as shapes and handwriting and very complex patterns such as faces and landscapes. To investigate one small aspect of human pattern recognition, in this study participants position lines of "best fit" to two-dimensional scatter plots of data. The study investigates…
Descriptors: Visualization, Pattern Recognition, Graphs, Data
Siebrase, Benjamin – ProQuest LLC, 2018
Multilayer perceptron neural networks, Gaussian naive Bayes, and logistic regression classifiers were compared when used to make early predictions regarding one-year college student persistence. Two iterations of each model were built, utilizing a grid search process within 10-fold cross-validation in order to tune model parameters for optimal…
Descriptors: Classification, College Students, Academic Persistence, Bayesian Statistics
Zhang, Xiao; Räsänen, Pekka; Koponen, Tuire; Aunola, Kaisa; Lerkkanen, Marja-Kristiina; Nurmi, Jari-Erik – Developmental Psychology, 2017
The longitudinal relations of domain-general and numerical skills at ages 6-7 years to 3 cognitive domains of arithmetic learning, namely knowing (written computation), applying (arithmetic word problems), and reasoning (arithmetic reasoning) at age 11, were examined for a representative sample of 378 Finnish children. The results showed that…
Descriptors: Arithmetic, Mathematics Instruction, Mathematical Logic, Short Term Memory
Showalter, Daniel A.; Mullet, Luke B. – Mid-Western Educational Researcher, 2017
Selection bias is a persistent, and often hidden, problem in educational research. It is the primary obstacle standing in between increasingly available large education datasets and the ability to make valid causal inferences to inform policymaking, research, and practice (Stuart, 2010). This article provides an accessible discussion on the…
Descriptors: Educational Research, Selection Criteria, Selection Tools, Bias
Schwibbe, Anja; Kothe, Christian; Hampe, Wolfgang; Konradt, Udo – Advances in Health Sciences Education, 2016
Sixty years of research have not added up to a concordant evaluation of the influence of spatial and manual abilities on dental skill acquisition. We used Ackerman's theory of ability determinants of skill acquisition to explain the influence of spatial visualization and manual dexterity on the task performance of dental students in two…
Descriptors: Dental Schools, Spatial Ability, Psychomotor Skills, Skill Development
Akar, Sacide Guzin Mazman; Altun, Arif – Contemporary Educational Technology, 2017
The purpose of this study is to investigate and conceptualize the ranks of importance of social cognitive variables on university students' computer programming performances. Spatial ability, working memory, self-efficacy, gender, prior knowledge and the universities students attend were taken as variables to be analyzed. The study has been…
Descriptors: Individual Differences, Learning Processes, Programming, Self Efficacy
Mix, Kelly S.; Levine, Susan C.; Cheng, Yi-Ling; Young, Chris; Hambrick, D. Zachary; Ping, Raedy – Grantee Submission, 2016
The relations among various spatial and mathematics skills were assessed in a cross-sectional study of 854 children from kindergarten, third, and sixth grades (i.e., 5 to 13 years of age). Children completed a battery of spatial mathematics tests and their scores were submitted to exploratory factor analyses both within and across domains. In the…
Descriptors: Spatial Ability, Mathematics Skills, Kindergarten, Grade 3