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Yiran Chen – Research in Higher Education, 2025
The "k"-means clustering method, while widely embraced in college student typology research, is often misunderstood and misapplied. Many researchers regard "k"-means as a near-universal solution for uncovering homogeneous student groups, believing its success hinges primarily on the selection of an appropriate "k."…
Descriptors: College Students, Classification, Educational Research, Research Methodology
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David Shuang Song; Anthony Lising Antonio; Pearl Lo – International Studies in Sociology of Education, 2025
In a longitudinal interview-based study of racial-minority students of low-income or working-class origin at an elite private university in the United States, we examine how class and race co-determine students' friendship-making patterns. We advance previous research in college students' friendship-making by applying a dual lens of…
Descriptors: College Students, Private Colleges, Social Class, Race
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Salehudin, Imam; Alpert, Frank – Education & Training, 2022
Purpose: This study analyzed segment differences of student preference for video use in lecture classes and university use of video lecture classes. The authors then conducted novel gap analyses to identify gaps between student segments' preferences for videos versus their level of exposure to in-class videos. Multivariate analysis of variance…
Descriptors: Preferences, Video Technology, Class Activities, College Students
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Ji Won You – Studies in Higher Education, 2024
Team project-based learning has become increasingly common in higher education. This study aimed to characterise and understand students' team learning experiences in team project-based learning by considering various aspects, such as individual qualities, teamwork, task, and instructor support. K-means clustering analysis was performed using…
Descriptors: Cooperative Learning, Profiles, Outcomes of Education, Student Projects
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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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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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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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Misun Seo – Journal of Pan-Pacific Association of Applied Linguistics, 2021
This study examined Korean learners' production of intervocalic English biconsonantal clusters consisting of /p/, /t/, /k/, /m/, /n/, or /[eng] / followed by /l/ or /[voiced alveolar approximant]/. The results of the production experiment showed several factors influencing Korean learners' production. First, Korean learners' production was…
Descriptors: Second Language Learning, English (Second Language), Phonemes, Korean
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Çebi, Ayça; Güyer, Tolga – Education and Information Technologies, 2020
In this study, students' interactions with different learning activities are examined and the relation among learning performance with different interaction patterns, learning performance, self-regulated learning (SRL) strategies and motivation is presented. Learning materials including different kinds of activities are prepared and presented to…
Descriptors: Interaction, Behavior Patterns, Learning Analytics, Electronic Learning
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Howlin, Colm P.; Dziuban, Charles D. – International Educational Data Mining Society, 2019
Clustering of educational data allows similar students to be grouped, in either crisp or fuzzy sets, based on their similarities. Standard approaches are well suited to identifying common student behaviors; however, by design, they put much less emphasis on less common behaviors or outliers. The approach presented in this paper employs fuzzing…
Descriptors: Data Collection, Student Behavior, Learning Strategies, Feedback (Response)
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Li, Tiffany Wenting; Paquette, Luc – International Educational Data Mining Society, 2020
Spatial visualization skills are essential and fundamental to studying STEM subjects, and sketching is an effective way to practice those skills. One significant challenge of supporting practice using sketching questions is the vast number of possible mistakes, making it time-consuming for instructors to provide customized and actionable feedback…
Descriptors: Error Patterns, Cluster Grouping, Visualization, Spatial Ability
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Balint, Trevor A.; Teodorescu, Raluca; Colvin, Kimberly; Choi, Youn-Jeng; Pritchard, David – Physics Teacher, 2017
In this paper we examine how different types of participants in a physics Massive Open Online Course (MOOC) tend to use the existing course resources. We use data from the 2013 offering of the Massive Open Online Course 8.MReVx designed by the RELATE (REsearch in Learning Assessing and Tutoring Effectively) Group at the Massachusetts Institute of…
Descriptors: Physics, Online Courses, Instructional Materials, Educational Resources
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Hall, Jessica; McGregor, Karla K.; Oleson, Jacob – Journal of Speech, Language, and Hearing Research, 2017
Purpose: The purpose of this study is to determine whether deficits in executive function and lexical-semantic memory compromise the linguistic performance of young adults with specific learning disabilities (LD) enrolled in postsecondary studies. Method: One hundred eighty-five students with LD (n = 53) or normal language development (ND, n =…
Descriptors: Executive Function, Semantics, Memory, Young Adults
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Lancieri, Luigi – International Association for Development of the Information Society, 2017
Several studies point out the link between sociability and academic results. In this paper, we highlight a phenomenon of asymmetry in the perception of friendship. This occurs when a student think he has more or less friends than he really has. We present an experimental method that allows us to analyze this question in relation with the academic…
Descriptors: Friendship, Interpersonal Relationship, Academic Achievement, Social Networks
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Shimada, Atsushi; Mouri, Kousuke; Taniguchi, Yuta; Ogata, Hiroaki; Taniguchi, Rin-ichiro; Konomi, Shin'ichi – International Educational Data Mining Society, 2019
In this paper, we focus on optimizing the assignment of students to courses. The target courses are conducted by different teachers using the same syllabus, course design, and lecture materials. More than 1,300 students are mechanically assigned to one of ten courses taught by different teachers. Therefore, mismatches often occur between students'…
Descriptors: Student Placement, Learning Activities, Learning Analytics, Cognitive Style
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