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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
Hui Shi; Yihang Zhou; Vanessa P. Dennen; Jaesung Hur – Education and Information Technologies, 2024
The imbalance in student-teacher ratio and the diversity of student population pose challenges to MOOC's quality of instructor support. An understanding of student profiles, such as who they are and how they behave, is critical to improving personalized support of MOOC learning environments. While past studies have explored different types of…
Descriptors: MOOCs, Behavior Patterns, Student Behavior, Cluster Grouping
Scheidt, Matthew; Godwin, Allison; Berger, Edward; Chen, John; Self, Brian P.; Widmann, James M.; Gates, Ann Q. – Journal of Engineering Education, 2021
Background: Noncognitive and affective (NCA) factors (e.g., belonging, engineering identity, motivation, mindset, personality, etc.) are important to undergraduate student success. However, few studies have considered how these factors coexist and act in concert. Purpose/Hypothesis: We hypothesize that students cluster into several distinct…
Descriptors: Engineering Education, Undergraduate Students, Student Characteristics, Differences
Arora, Skand; Goel, Manav; Sabitha, A. Sai; Mehrotra, Deepti – American Journal of Distance Education, 2017
The open nature of Massive Open Online Courses (MOOCs) attracts a large number of learners with different backgrounds, skills, motivations, and goals. This has brought a need to understand such heterogeneity in populations of MOOC learners. Categorizing these learners based upon their interaction with the course can help address this need and…
Descriptors: Online Courses, Heterogeneous Grouping, Learner Engagement, Student Characteristics
Barata, Gabriel; Gama, Sandra; Jorge, Joaquim; Gonçalves, Daniel – IEEE Transactions on Learning Technologies, 2016
State of the art research shows that gamified learning can be used to engage students and help them perform better. However, most studies use a one-size-fits-all approach to gamification, where individual differences and needs are ignored. In a previous study, we identified four types of students attending a gamified college course, characterized…
Descriptors: Prediction, Performance, Profiles, Games
Cantwell, Robert H.; Bourke, Sid F.; Scevak, Jill J.; Holbrook, Allyson P.; Budd, Janene – Studies in Higher Education, 2017
A national cohort of doctoral students (n = 1390) completed a suite of metacognitive questionnaires indicating management of affective, intellectual and contingency demands in learning. Responses to the questionnaires were analysed for evidence of individual differences in reported metacognitive behaviours. Three patterns of metacognitive response…
Descriptors: Individual Differences, Doctoral Programs, Graduate Students, Metacognition
Morret, Tanya H.; Machado, Crystal H. – AASA Journal of Scholarship & Practice, 2017
Given the wide range of ability (academic, linguistic and cultural) in classrooms differentiated instruction is often difficult to manage. District and building level leadership can play an important role by providing the vision and support needed to implement Whole School Cluster Grouping (WSCG), the innovative scheduling approach described in…
Descriptors: Elementary School Students, Cluster Grouping, Individualized Instruction, Scheduling
Pond, Jarrad W. T.; Chini, Jacquelyn J. – Physical Review Physics Education Research, 2017
In this study, we explore the strategic self-regulatory and motivational characteristics of students in studio-mode physics courses at three universities with varying student populations and varying levels of success in their studio-mode courses. We survey students using questions compiled from several existing questionnaires designed to measure…
Descriptors: Algebra, Physics, Profiles, Student Characteristics
Saarela, Mirka; Kärkkäinen, Tommi – International Educational Data Mining Society, 2015
Certain stereotypes can be associated with people from different countries. For example, the Italians are expected to be emotional, the Germans functional, and the Chinese hard-working. In this study, we cluster all 15-year-old students representing the 68 different nations and territories that participated in the latest Programme for…
Descriptors: Weighted Scores, Stereotypes, Standardized Tests, Student Characteristics
Vanwynsberghe, Griet; Vanlaar, Gudrun; Van Damme, Jan; De Fraine, Bieke – School Effectiveness and School Improvement, 2017
Although the importance of primary schools in the long term is of interest in educational effectiveness research, few studies have examined the long-term effects of schools over the past decades. In the present study, long-term effects of primary schools on the educational positions of students 2 and 4 years after starting secondary education are…
Descriptors: Secondary Education, School Effectiveness, Elementary Secondary Education, Followup Studies
Costa, Carolina; Alvelos, Helena; Teixeira, Leonor – Technology, Pedagogy and Education, 2016
This study analyses and compares the use of Web 2.0 tools by students in both learning and leisure contexts. Data were collected based on a questionnaire applied to 234 students from the University of Aveiro (Portugal) and the results were analysed by using descriptive analysis, paired samples t-tests, cluster analyses and Kruskal-Wallis tests.…
Descriptors: Foreign Countries, Web 2.0 Technologies, College Students, Questionnaires
Barata, Gabriel; Gama, Sandra; Jorge, Joaquim; Gonçalves, Daniel – International Journal of Game-Based Learning, 2014
Gamification of education is a recent trend, and early experiments showed promising results. Students seem not only to perform better, but also to participate more and to feel more engaged with gamified learning. However, little is known regarding how different students are affected by gamification and how their learning experience may vary. In…
Descriptors: Educational Games, Learning Experience, College Students, Learning Strategies
Ellis, Robert A. – Active Learning in Higher Education, 2016
There is variation in the university student experience of learning. Prior research has shown that factors that shape this include student characteristics, the learning context, student perceptions of that context and approaches to learning and their learning outcomes. In blended contexts, there is a need to identify variables which can explain…
Descriptors: Student Experience, Educational Environment, Inquiry, Higher Education
Lauen, Douglas Lee; Fuller, Bruce; Dauter, Luke – American Journal of Education, 2015
The debate over charter school effectiveness relies largely on neoclassical logic: individual parents or students express demand for a widening array of school types and then experience variable levels of organizational quality. We argue that market-like behavior is nested in segments of local organizational fields with different types of charter…
Descriptors: Charter Schools, School Effectiveness, Achievement Gains, Academic Achievement
Papi, Mostafa; Teimouri, Yasser – Language Learning, 2014
The study aimed to identify different second language (L2) learner motivational types drawing on the framework of the L2 motivational self system. A total of 1,278 secondary school students learning English in Iran completed a questionnaire survey. Cluster analysis yielded five different groups based on the strength of different variables within…
Descriptors: Multivariate Analysis, Second Language Learning, Second Language Instruction, Motivation