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Minghui Wang; Meagan Sundstrom; Karen Nylund-Gibson; Marsha Ing – Physical Review Physics Education Research, 2025
Clustering methods are often used in physics education research (PER) to identify subgroups of individuals within a population who share similar response patterns or characteristics. Among these, k-means (or k-modes, for categorical data) is one of the most commonly used clustering methods in PER. This algorithm, however, is distance-based rather…
Descriptors: Physics, Science Education, Educational Research, Multivariate Analysis
Bosman, Lisa; Soto, Esteban; Varela, Thaís Ferraz; Wollega, Ebisa – Teaching Statistics: An International Journal for Teachers, 2023
Statistical knowledge is required for students in a range of disciplines. However, there are limited educator resources that exist for applying statistics to solve real-world problems. This investigation provides one approach to teaching statistics using entrepreneurial-minded learning (as a way to connect real-world applications and value…
Descriptors: Statistics Education, Introductory Courses, Problem Solving, Entrepreneurship
Kenneth Tyler Wilcox; Ross Jacobucci; Zhiyong Zhang; Brooke A. Ammerman – Grantee Submission, 2023
Text is a burgeoning data source for psychological researchers, but little methodological research has focused on adapting popular modeling approaches for text to the context of psychological research. One popular measurement model for text, topic modeling, uses a latent mixture model to represent topics underlying a body of documents. Recently,…
Descriptors: Bayesian Statistics, Content Analysis, Undergraduate Students, Self Destructive Behavior
Sami Mejri; Steven Borawski – International Journal on E-Learning, 2023
This article will address predictors of success for online students. A survey questionnaire was used to gather data concerning online students' social and educational readiness levels at a four-year private university in the Midwestern United States. Of the 4,050 potential participants, 250 (6.23%) responded to the survey. Stepwise regression…
Descriptors: Academic Persistence, Success, Online Courses, Readiness
Changpetch, Pannapa; Reid, Moya – Journal of Education for Business, 2021
Based on a statistical analysis, undergraduate business students are shown to prefer classification tree over six other standard data mining techniques. Data were collected over a 4-year period from students taking a data mining course offered at a business university in the US. The principal reason given by students for this preference is that…
Descriptors: Data Analysis, Models, Statistical Analysis, Undergraduate Students
Bousnguar, Hassan; Najdi, Lotfi; Battou, Amal – Education and Information Technologies, 2022
Forecasting the enrollments of new students in bachelor's systems became an urgent desire in the majority of higher education institutions. It represents an important stage in the process of making strategic decisions for new course's accreditation and optimization of resources. To gain a deep view of the educational forecasting context, the most…
Descriptors: Higher Education, Undergraduate Students, Enrollment Management, Strategic Planning
Bornn, Luke; Mortensen, Jacob; Ahrensmeier, Daria – Canadian Journal for the Scholarship of Teaching and Learning, 2022
This paper presents a novel design for an upper-level undergraduate statistics course structured around data rather than methods. The course is designed around curated datasets to reflect real-world data science practice and engages students in experiential and peer learning using the data science competition platform Kaggle. Peer learning is…
Descriptors: Undergraduate Study, Cooperative Learning, Peer Influence, Undergraduate Students
Khosravi, Hassan; Cooper, Kendra M. L. – Journal of Learning Analytics, 2018
Educational environments continue to evolve rapidly to address the needs of diverse, growing student populations while embracing advances in pedagogy and technology. In this changing landscape, ensuring consistency among the assessments for different offerings of a course (within or across terms), providing meaningful feedback about student…
Descriptors: Graphs, Academic Achievement, Student Evaluation, Models
Aiken, John M.; Henderson, Rachel; Caballero, Marcos D. – Physical Review Physics Education Research, 2019
Physics education research (PER) has used quantitative modeling techniques to explore learning, affect, and other aspects of physics education. However, these studies have rarely examined the predictive output of the models, instead focusing on the inferences or causal relationships observed in various data sets. This research introduces a modern…
Descriptors: Physics, Bachelors Degrees, College Science, Student Records
Witteveen, Dirk; Attewell, Paul – Research in Higher Education, 2017
Higher education in America is characterized by widespread access to college but low rates of completion, especially among undergraduates at less selective institutions. We analyze longitudinal transcript data to examine processes leading to graduation, using Hidden Markov modeling. We identify several latent states that are associated with…
Descriptors: Markov Processes, Higher Education, Longitudinal Studies, Statistical Analysis
Zhao, Jingjing; Li, Tong; Elliott, Mark A.; Rueckl, Jay G. – Scientific Studies of Reading, 2018
This article reports two experiments in which the artificial orthography paradigm was used to investigate the mechanisms underlying learning to read. In each experiment, participants were taught the meanings and pronunications of words written in an unfamiliar orthography, and the statistical structure of the mapping between written and spoken…
Descriptors: Cooperative Learning, Reading Instruction, Orthographic Symbols, Pronunciation
Ye, Chaoxiong; Hu, Zhonghua; Li, Hong; Ristaniemi, Tapani; Liu, Qiang; Liu, Taosheng – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2017
Two broad theories of visual working memory (VWM) storage have emerged from current research, a discrete slot-based theory and a continuous resource theory. However, neither the discrete slot-based theory or continuous resource theory clearly stipulates how the mental commodity for VWM (discrete slot or continuous resource) is allocated.…
Descriptors: Models, Resource Allocation, Short Term Memory, Undergraduate Students
Vogel, Tobias; Carr, Evan W.; Davis, Tyler; Winkielman, Piotr – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2018
Stimuli that capture the central tendency of presented exemplars are often preferred--a phenomenon also known as the classic beauty-in-averageness effect. However, recent studies have shown that this effect can reverse under certain conditions. We propose that a key variable for such ugliness-in-averageness effects is the category structure of the…
Descriptors: Interpersonal Attraction, Preferences, Stimuli, Experiments
Warren, Scott J.; Robinson, Heather Ann – American Journal of Distance Education, 2018
With increased online course offerings at most higher education institutions, it is important to have a way to determine whether the courses continue to have value to students or outlived their usefulness. Courses are the central means of knowledge delivery for each institution and may be viewed through the lens of the product life-cycle, which is…
Descriptors: Online Courses, Learner Engagement, Developmental Stages, Models
Starns, Jeffrey J.; Ma, Qiuli – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2018
The two-high-threshold (2HT) model of recognition memory assumes that people make memory errors because they fail to retrieve information from memory and make a guess, whereas the continuous unequal-variance (UV) model and the low-threshold (LT) model assume that people make memory errors because they retrieve misleading information from memory.…
Descriptors: Guessing (Tests), Recognition (Psychology), Memory, Tests

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