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John Sailer – National Association of Scholars, 2023
American higher education has recently embraced a new tool for advancing its commitment to "diversity, equity, and inclusion" (DEI). Many colleges and universities are now engaged in "DEI cluster hiring," a practice which ultimately embodies higher education's turn toward political and social activism. Unfortunately, it has…
Descriptors: Diversity, Equal Education, Inclusion, Personnel Selection
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Cowan, Nelson; Elliott, Emily M. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2023
We used the timing of serial recall in several situations to reveal important aspects of recall groupings that participants construct and the reasons those groupings occur. We examined the timing of responses in the recall of digit strings within two published experiments. Cowan, Saults, Elliott, and Moreno (2002) examined memory for nine-item…
Descriptors: Serial Ordering, Recall (Psychology), Reaction Time, Short Term Memory
Singelmann, Lauren Nichole – ProQuest LLC, 2022
To meet the national and international call for creative and innovative engineers, many engineering departments and classrooms are striving to create more authentic learning spaces where students are actively engaging with design and innovation activities. For example, one model for teaching innovation is Innovation-Based Learning (IBL) where…
Descriptors: Engineering Education, Design, Educational Innovation, Models
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Tulsi A. Radhoe; Joost A. Agelink van Rentergem; Carolien Torenvliet; Annabeth P. Groenman; Wikke J. van der Putten; Hilde M. Geurts – Journal of Autism and Developmental Disorders, 2024
Autism is heterogeneous, which complicates providing tailored support and future prospects. We aim to identify subgroups in autistic adults with average to high intelligence, to clarify if certain subgroups might need support. We included 14 questionnaire variables related to aging and/or autism (e.g., demographic, psychological, and lifestyle).…
Descriptors: Adults, Autism Spectrum Disorders, Population Groups, Intelligence
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Barker, Joshua O.; Rohde, Jacob A. – Health Education & Behavior, 2019
E-cigarette use in the United States has significantly grown in recent years. Widespread diffusion of e-cigarette content across social media communities may be contributing to this growth. In this study, we (1) explored topics related to e-cigarettes and vaping on Reddit and (2) examined the extent to which these topics clustered across distinct…
Descriptors: Smoking, Social Media, Information Dissemination, Cluster Grouping
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Syrmpas, Ioannis; Papaioannou, Athanasios; Digelidis, Nikolaos; Erturan, Gokce; Byra, Mark – Journal of Teaching in Physical Education, 2021
Purpose: This study aimed to test the invariance of perceptions of the Spectrum teaching styles across Turkish and Greek preservice physical education teachers and to examine whether the styles could be classified into two clusters through self-determination theory. Greek (n = 298) and Turkish (n = 300) preservice teachers participated. Method:…
Descriptors: Teaching Styles, Questionnaires, Preservice Teachers, Physical Education Teachers
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Jennifer C. LaFleur – AERA Online Paper Repository, 2024
Drawing on interviews with parents of public-school students in who joined learning pods for the 2020-21 school year, this paper argues that the COVID-19 pandemic may have had a narrowing effect on the worlds of children in ways that increase their socio-spatial isolation along vectors of race and class. Interviews with parents who started…
Descriptors: COVID-19, Pandemics, Small Group Instruction, Parent Participation
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Yang, Xi; Zhou, Guojing; Taub, Michelle; Azevedo, Roger; Chi, Min – International Educational Data Mining Society, 2020
In the learning sciences, heterogeneity among students usually leads to different learning strategies or patterns and may require different types of instructional interventions. Therefore, it is important to investigate student subtyping, which is to group students into subtypes based on their learning patterns. Subtyping from complex student…
Descriptors: Grouping (Instructional Purposes), Learning Strategies, Artificial Intelligence, Learning Analytics
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Jina Kim – Society for Research on Educational Effectiveness, 2024
Background: Despite historical efforts (Ellwood & Kane, 2000; Daun-Barnett, 2013; Perna, 2006; Kane, 1999), persisting inequities in higher education access underscore the need for innovative research approaches. Previous studies utilizing geospatial approaches often overlook critical dimensions like college readiness and financial aid while…
Descriptors: Access to Education, Equal Education, Higher Education, High School Students
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Nabizadeh, Amir Hossein; Goncalves, Daniel; Gama, Sandra; Jorge, Joaquim – IEEE Transactions on Learning Technologies, 2022
The main challenge in higher education is student retention. While many methods have been proposed to overcome this challenge, early and continuous feedback can be very effective. In this article, we propose a method for predicting student final grades in a course using only their performance data in the current semester. It assists students in…
Descriptors: College Students, Prediction, Grades (Scholastic), Game Based Learning
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Onoue, Akira; Shimada, Atsushi; Minematsu, Tsubasa; Taniguchi, Rin-Ichiro – International Association for Development of the Information Society, 2019
This study aimed to cluster learners based on the structures of the knowledge maps they created. Learners drew their own knowledge maps to reflect their learning activities. Our system collected individual knowledge maps from many learners and clustered them to generate an integrated version of the knowledge maps of each cluster. We applied the…
Descriptors: Concept Mapping, Learning Processes, Cluster Grouping, Graphs
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Emily Gardiner; Anton R. Miller; Lucyna M. Lach – Journal of Developmental and Physical Disabilities, 2021
Background: Given the significant behavioral heterogeneity characterizing children with neurodevelopmental disorders and disabilities (NDD/D), the current study examined whether cluster analysis could classify a diverse sample into more homogeneous subgroups. Aims: We first utilized cluster analysis to identify subgroups of children demonstrating…
Descriptors: Intellectual Disability, Children, Neurodevelopmental Disorders, Cluster Grouping
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Quy, Tai Le; Roy, Arjun; Friege, Gunnar; Ntoutsi, Eirini – International Educational Data Mining Society, 2021
Traditionally, clustering algorithms focus on partitioning the data into groups of similar instances. The similarity objective, however, is not sufficient in applications where a "fair-representation" of the groups in terms of protected attributes like gender or race, is required for each cluster. Moreover, in many applications, to make…
Descriptors: Cluster Grouping, Artificial Intelligence, Mathematics, Computer Uses in Education
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Hall, Matthew L.; De Anda, Stephanie – Journal of Speech, Language, and Hearing Research, 2021
Purpose: The purposes of this study were (a) to introduce "language access profiles" as a viable alternative construct to "communication mode" for describing experience with language input during early childhood for deaf and hard-of-hearing (DHH) children; (b) to describe the development of a new tool for measuring DHH…
Descriptors: Deafness, Hearing Impairments, Children, Profiles
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Goodson, Aaron – Journal of Negro Education, 2020
Follow-up studies of clustering by academic major, the dynamic of 25% or more of the student-athletes on a roster pursuing the same academic major, indicate that it still occurs in revenue-generating sports. Research on clustering is absent in member institutions of different NCAA divisions, institutions with unique missions, specifically…
Descriptors: Majors (Students), Black Colleges, African American Students, Student Athletes
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