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An, Weihua – Sociological Methods & Research, 2023
In this article, I present a new multivariate regression model for analyzing outcomes with network dependence. The model is capable to account for two types of outcome dependence including the mean dependence that allows the outcome to depend on selected features of a known dependence network and the error dependence that allows the outcome to be…
Descriptors: Multivariate Analysis, Regression (Statistics), Models, Correlation
Supply, Anne-Sophie; Wijns, Nore; Van Dooren, Wim; Onghena, Patrick – Educational Studies in Mathematics, 2023
The many studies with coin-tossing tasks in literature show that the concept of randomness is challenging for adults as well as children. Systematic errors observed in coin-tossing tasks are often related to the representativeness heuristic, which refers to a mental shortcut that is used to judge randomness by evaluating how well a set of random…
Descriptors: Pattern Recognition, Preschool Children, Prediction, Thinking Skills
Batool, Saba; Rashid, Junaid; Nisar, Muhammad Wasif; Kim, Jungeun; Kwon, Hyuk-Yoon; Hussain, Amir – Education and Information Technologies, 2023
Educational data mining is an emerging interdisciplinary research area involving both education and informatics. It has become an imperative research area due to many advantages that educational institutions can achieve. Along these lines, various data mining techniques have been used to improve learning outcomes by exploring large-scale data that…
Descriptors: Academic Achievement, Prediction, Data Use, Information Retrieval
Khor, Ean Teng – International Journal of Information and Learning Technology, 2022
Purpose: The purpose of the study is to build predictive models for early detection of low-performing students and examine the factors that influence massive open online courses students' performance. Design/methodology/approach: For the first step, the author performed exploratory data analysis to analyze the dataset. The process was then…
Descriptors: Prediction, Low Achievement, Algorithms, Artificial Intelligence
Shoaib, Muhammad; Sayed, Nasir; Amara, Nedra; Latif, Abdul; Azam, Sikandar; Muhammad, Sajjad – Education and Information Technologies, 2022
Technology and data analysis have evolved into a resource-rich tool for collecting, researching and comparing student achievement levels in the classroom. There are sufficient resources to discover student success through data analysis by routinely collecting extensive data on student behaviour and curriculum structure. Educational Data Mining…
Descriptors: Prediction, Artificial Intelligence, Student Behavior, Academic Achievement
Wijns, Nore; Verschaffel, Lieven; De Smedt, Bert; Torbeyns, Joke – Child Development, 2021
The present study aimed to analyze the direction of the associations between repeating patterning, growing patterning, and numerical ability. Participants were 410 children who were annually assessed on their repeating patterning, growing patterning, and numerical ability, at ages 4, 5, and 6 years (i.e., spring 2017, 2018, and 2019). A…
Descriptors: Pattern Recognition, Numeracy, Longitudinal Studies, Young Children
Borriello, Giulia A.; Grenell, Amanda; Vest, Nicholas A.; Moore, Kyler; Fyfe, Emily R. – Child Development, 2023
This study examined repeating and growing pattern knowledge and their associations with procedural and conceptual arithmetic knowledge in a sample of U.S. children (N = 185; M[subscript age] = 79.5 months; 55% female; 88% White) and adults (N = 93; M[subscript age] = 19.5 years; 62% female; 66% White) from 2019 to 2020. Three key findings emerged:…
Descriptors: Mathematics Instruction, Pattern Recognition, Prediction, Correlation
Lemay, David John; Doleck, Tenzin – Interactive Learning Environments, 2022
Predicting student performance in Massive Open Online Courses (MOOCs) is important to aid in retention efforts. Researchers have demonstrated that video watching features can be used to accurately predict student test performance on video quizzes employing neural networks to predict video test grades from viewing behavior including video searching…
Descriptors: MOOCs, Academic Achievement, Prediction, Student Behavior
Gontzis, Andreas F.; Kotsiantis, Sotiris; Panagiotakopoulos, Christos T.; Verykios, Vassilios S. – Interactive Learning Environments, 2022
Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students' retention services. Big Data mining and machine learning methods are applied to manipulate, analyze and predict students' failure,…
Descriptors: Student Attrition, Distance Education, At Risk Students, Achievement
Gkontzis, Andreas F.; Kotsiantis, Sotiris; Panagiotakopoulos, Christos T.; Verykios, Vassilios S. – Interactive Learning Environments, 2022
Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students' retention services. Big Data mining and machine learning methods are applied to manipulate, analyze, and predict students' failure,…
Descriptors: Student Attrition, Distance Education, At Risk Students, Achievement
Xu, Tonghui – Journal of Educators Online, 2023
The early detection of students' academic performance or final grades helps instructors prepare their online courses. In the Open University Learning Analytics Dataset, I found many online students clicked the course materials before the first day of class. This study aims to investigate how data mining models can use this student interaction data…
Descriptors: College Students, Online Courses, Academic Achievement, Data Analysis
Blum, Caleigh; Taylor, Amy – Science Activities: Projects and Curriculum Ideas in STEM Classrooms, 2022
Children are very curious about the world around them. You may find them peering at tadpoles in a pond, counting ants on a log, or wondering about the stars, the sun, and the moon. I have been asked many times: Is the moon really made of cheese? Do astronauts live on the moon? Why does the moon look different every time I look up? Introducing…
Descriptors: Grade 1, Elementary School Students, Astronomy, Science Instruction
Costa, Stella F.; Diniz, Michael M. – Education and Information Technologies, 2022
The large rates of students' failure is a very frequent problem in undergraduate courses, being even more evident in exact sciences. Pointing out the reasons of such problem is a paramount research topic, though not an easy task. An alternative is to use Educational Data Mining techniques (EDM), which enables one to convert data from educational…
Descriptors: Prediction, Undergraduate Students, Mathematics Education, Models
Cohausz, Lea; Tschalzev, Andrej; Bartelt, Christian; Stuckenschmidt, Heiner – International Educational Data Mining Society, 2023
Demographic features are commonly used in Educational Data Mining (EDM) research to predict at-risk students. Yet, the practice of using demographic features has to be considered extremely problematic due to the data's sensitive nature, but also because (historic and representation) biases likely exist in the training data, which leads to strong…
Descriptors: Information Retrieval, Data Processing, Pattern Recognition, Information Technology
Ruffman, Ted; Chen, Lisa; Lorimer, Ben; Vanier, Sarah; Edgar, Kate; Scarf, Damian; Taumoepeau, Mele – Developmental Science, 2023
There are two broad views of children's theory of mind. The mentalist view is that it emerges in infancy and is possibly innate. The minimalist view is that it emerges more gradually in childhood and is heavily dependent on learning. According to minimalism, children initially understand behaviors rather than mental states, and they are assisted…
Descriptors: Language Processing, Infants, Language Acquisition, Infant Behavior