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Mengjiao Yin; Hengshan Cao; Zuhong Yu; Xianyu Pan – International Journal of Web-Based Learning and Teaching Technologies, 2024
This study presents the Academic Investment Model (AIM) as a novel approach to predicting student academic performance by incorporating learning styles as a predictive feature. Utilizing data from 138 Marketing students across China, the research employs a combination of machine learning clustering methods and manual feature engineering through a…
Descriptors: Predictor Variables, Artificial Intelligence, Performance, Cluster Grouping
Ashley Haigler – ProQuest LLC, 2021
The results of an industry research survey showed, understanding Dissertation Research categories has not been the focused on many researchers and institutions. This research expands on machine learning methodologies using two similar datasets to answer these three questions: 1. Is there a way to track the trends of Pace University's Doctor of…
Descriptors: Artificial Intelligence, Content Analysis, Cluster Grouping, Classification
Theobald, Elli – CBE - Life Sciences Education, 2018
Discipline-based education researchers have a natural laboratory--classrooms, programs, colleges, and universities. Studies that administer treatments to multiple sections, in multiple years, or at multiple institutions are particularly compelling for two reasons: first, the sample sizes increase, and second, the implementation of the treatments…
Descriptors: Educational Research, Hierarchical Linear Modeling, Program Implementation, Predictor Variables
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
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
Rentroia-Bonito, Maria Alexandra; Gonçalves, Daniel; Jorge, Joaquim A. – International Journal of Mobile and Blended Learning, 2015
Technological advances during the last decade have provided huge possibilities to support e-learning. However, there are still concerns regarding Return-on-Investment (ROI) of e-learning, its sustainability within organizational boundaries and effectiveness across potential learner groups. Much previous research has concentrated on learners'…
Descriptors: Blended Learning, Learning Motivation, Cluster Grouping, Courseware
Riofrio-Luzcando, Diego; Ramirez, Jaime; Berrocal-Lobo, Marta – IEEE Transactions on Learning Technologies, 2017
Data mining is known to have a potential for predicting user performance. However, there are few studies that explore its potential for predicting student behavior in a procedural training environment. This paper presents a collective student model, which is built from past student logs. These logs are first grouped into clusters. Then, an…
Descriptors: Student Behavior, Predictive Validity, Predictor Variables, Predictive Measurement
Livingston, Samuel A. – ETS Research Report Series, 2014
In this study, I investigated 2 procedures intended to create test-taker groups of equal ability by poststratifying on a composite variable created from demographic information. In one procedure, the stratifying variable was the composite variable that best predicted the test score. In the other procedure, the stratifying variable was the…
Descriptors: Demography, Equated Scores, Cluster Grouping, Ability Grouping
Bishop-Fitzpatrick, Lauren; Hong, Jinkuk; Smith, Leann E.; Makuch, Renee A.; Greenberg, Jan S.; Mailick, Marsha R. – Journal of Autism and Developmental Disorders, 2016
This study aims to extend the definition of quality of life (QoL) for adults with autism spectrum disorder (ASD, n = 180, ages 23-60) by: (1) characterizing the heterogeneity of normative outcomes (employment, independent living, social engagement) and objective QoL (physical health, neighborhood quality, family contact, mental health issues); and…
Descriptors: Quality of Life, Pervasive Developmental Disorders, Normalization (Disabilities), Adults
Jabnoun, Naceur – Quality Assurance in Education: An International Perspective, 2015
Purpose: This paper aims to explore the influence of wealth, transparency and democracy on the number of universities per million people ranked among the top 300 and 500. The highly ranked universities in the world tend to be concentrated in a few countries. Design/Methodology/Approach: ANOVA was used to test the differences between the two groups…
Descriptors: Universities, Classification, Influences, Fiscal Capacity
Kettler, Todd; Puryear, Jeb S.; Mullet, Dianna R. – Journal of Advanced Academics, 2016
Definitions of rurality in education research are inconsistent, making generalization across studies difficult at best. We review published research in rural education between 2005 and 2015 (n = 17) and characterize the way each defined rural. A common technique for classifying rural schools is the National Center for Educational Statistics (NCES)…
Descriptors: Rural Education, Gifted Disadvantaged, Gifted, Definitions
Buri, Olga Elizabeth Minchala; Stefos, Efstathios – International Education Studies, 2017
The objective of this study is to examine the social profile of students who are enrolled in Basic General Education in Ecuador. Both a descriptive and multidimensional statistical analysis was carried out based on the data provided by the National Survey of Employment, Unemployment and Underemployment in 2015. The descriptive analysis shows the…
Descriptors: Foreign Countries, Profiles, Data Analysis, General Education
Foy, Pierre, Ed.; Arora, Alka, Ed.; Stanco, Gabrielle M., Ed. – International Association for the Evaluation of Educational Achievement, 2013
This supplement contains documentation on the explicit and implicit stratification variables included in the TIMSS 2011 data files. The explicit strata are smaller sampling frames, created from the national sampling frames, from which national samples of schools were drawn. The implicit strata are nested within the explicit strata, and were used…
Descriptors: Predictor Variables, Sampling, Comparative Education, International Education
Foy, Pierre, Ed.; Drucker, Kathleen T., Ed. – International Association for the Evaluation of Educational Achievement, 2013
This supplement contains documentation on the explicit and implicit stratification variables included in the PIRLS 2011 data files. The explicit strata are smaller sampling frames, created from the national sampling frames, from which national samples of schools were drawn. The implicit strata are nested within the explicit strata, and were used…
Descriptors: Guides, Information Sources, Geographic Distribution, Databases
Lai, Mark H. C.; Kwok, Oi-man – Journal of Experimental Education, 2015
Educational researchers commonly use the rule of thumb of "design effect smaller than 2" as the justification of not accounting for the multilevel or clustered structure in their data. The rule, however, has not yet been systematically studied in previous research. In the present study, we generated data from three different models…
Descriptors: Educational Research, Research Design, Cluster Grouping, Statistical Data