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Nayak, Padmalaya; Vaheed, Sk.; Gupta, Surbhi; Mohan, Neeraj – Education and Information Technologies, 2023
Students' academic performance prediction is one of the most important applications of Educational Data Mining (EDM) that helps to improve the quality of the education process. The attainment of student outcomes in an Outcome-based Education (OBE) system adds invaluable rewards to facilitate corrective measures to the learning processes.…
Descriptors: Predictor Variables, Academic Achievement, Data Collection, Information Retrieval
Stanley, Lauren H. K. – ProQuest LLC, 2022
Early childhood traumatic experiences place children at-risk for social, emotional, and behavioral impairments that contribute to poor educational outcomes. There is an increasing awareness that children with early traumatic exposure may need supplemental supports for academic success. These supports may be presented in the form of…
Descriptors: Young Children, Kindergarten, Trauma, Early Experience
Rap, Robyn; Paxton, Pamela – Sociological Methods & Research, 2021
Questions on voluntary association memberships have been used extensively in social scientific research for decades. Researchers generally assume that these respondent self-reports are accurate, but their measurement has never been assessed. Respondent characteristics are known to influence the accuracy of other self-report variables such as…
Descriptors: Accuracy, Measurement Techniques, Error of Measurement, Voluntary Agencies
Al-Sudani, Sahar; Palaniappan, Ramaswamy – Education and Information Technologies, 2019
The students' progression and attainment gap are considered as key performance indicators of many universities worldwide. Therefore, universities invest significantly in resources to reduce the attainment gap between good and poor performing students. In this regard, various mathematical models have been utilised to predict students' performances…
Descriptors: Predictor Variables, College Students, Achievement Gap, Educational Attainment
No, Unkyung; Hong, Sehee – Educational and Psychological Measurement, 2018
The purpose of the present study is to compare performances of mixture modeling approaches (i.e., one-step approach, three-step maximum-likelihood approach, three-step BCH approach, and LTB approach) based on diverse sample size conditions. To carry out this research, two simulation studies were conducted with two different models, a latent class…
Descriptors: Sample Size, Classification, Comparative Analysis, Statistical Analysis
Musso, Mariel F.; Hernández, Carlos Felipe Rodríguez; Cascallar, Eduardo C. – Higher Education: The International Journal of Higher Education Research, 2020
Predicting and understanding different key outcomes in a student's academic trajectory such as grade point average, academic retention, and degree completion would allow targeted intervention programs in higher education. Most of the predictive models developed for those key outcomes have been based on traditional methodological approaches.…
Descriptors: Classification, Prediction, Artificial Intelligence, College Students
Basilakos, Alexandra; Yourganov, Grigori; den Ouden, Dirk-Bart; Fogerty, Daniel; Rorden, Chris; Feenaughty, Lynda; Fridriksson, Julius – Journal of Speech, Language, and Hearing Research, 2017
Purpose: Apraxia of speech (AOS) is a consequence of stroke that frequently co-occurs with aphasia. Its study is limited by difficulties with its perceptual evaluation and dissociation from co-occurring impairments. This study examined the classification accuracy of several acoustic measures for the differential diagnosis of AOS in a sample of…
Descriptors: Multivariate Analysis, Speech Impairments, Clinical Diagnosis, Aphasia
Mahar, Matthew T.; Welk, Gregory J.; Rowe, David A. – Measurement in Physical Education and Exercise Science, 2018
Purpose: To develop models to estimate aerobic fitness (VO[subscript 2]max) from PACER performance in 10- to 18-year-old youth, with and without body mass index (BMI) as a predictor. Method: Youth (N = 280) completed the PACER and a maximal treadmill test to assess VO[subscript 2]max. Validation and cross-validation groups were randomly formed to…
Descriptors: Exercise, Physical Fitness, Preadolescents, Adolescents
Justice, Laura M.; Ahn, Woo-Young; Logan, Jessica A. R. – Journal of Learning Disabilities, 2019
In this study, we identified child- and family-level characteristics most strongly associated with clinical identification of language disorder for preschool-aged children. We used machine learning to identify variables that best classified children receiving therapy for language disorder among a sample of 483 3- to 5-year-old children (54%…
Descriptors: Language Impairments, Disability Identification, Clinical Diagnosis, Preschool Children
Peña, Elizabeth D.; Bedore, Lisa M; Lugo-Neris, Mirza J.; Albudoor, Nahar – Language Assessment Quarterly, 2020
Children with Developmental language disorder (DLD) have particular difficulty learning language despite otherwise general normal development. When school age bilingual children struggle with language, a common question is if the difficulties they present reflect lack of ability or lack of language experience. To address the question of…
Descriptors: Bilingualism, Language Impairments, Accuracy, Spanish
Erbeli, Florina; He, Kai; Cheek, Connor; Rice, Marianne; Qian, Xiaoning – Scientific Studies of Reading, 2023
Purpose: Researchers have developed a constellation model of decodingrelated reading disabilities (RD) to improve the RD risk determination. The model's hallmark is its inclusion of various RD indicators to determine RD risk. Classification methods such as logistic regression (LR) might be one way to determine RD risk within the constellation…
Descriptors: At Risk Students, Reading Difficulties, Classification, Comparative Analysis
Barros, Thiago M.; Souza Neto, Plácido A.; Silva, Ivanovitch; Guedes, Luiz Affonso – Education Sciences, 2019
Predicting school dropout rates is an important issue for the smooth execution of an educational system. This problem is solved by classifying students into two classes using educational activities related statistical datasets. One of the classes must identify the students who have the tendency to persist. The other class must identify the…
Descriptors: Predictor Variables, Models, Dropout Rate, Classification
Lundetrae, Kjersti; Thomson, Jenny M. – Reading and Writing: An Interdisciplinary Journal, 2018
Rhythm plays an organisational role in the prosody and phonology of language, and children with literacy difficulties have been found to demonstrate poor rhythmic perception. This study explored whether students' performance on a simple rhythm task at school entry could serve as a predictor of whether they would face difficulties in word reading…
Descriptors: Foreign Countries, Elementary School Students, Grade 1, Beginning Reading
Hughes, John; Petscher, Yaacov – Regional Educational Laboratory Southeast, 2016
The high rate of students taking developmental education courses suggests that many students graduate from high school unready to meet college expectations. A college readiness screener can help colleges and school districts better identify students who are not ready for college credit courses. The primary audience for this guide is leaders and…
Descriptors: College Readiness, Screening Tests, Test Construction, Predictor Variables
Nugent, William Robert; Moore, Matthew; Story, Erin – Educational and Psychological Measurement, 2015
The standardized mean difference (SMD) is perhaps the most important meta-analytic effect size. It is typically used to represent the difference between treatment and control population means in treatment efficacy research. It is also used to represent differences between populations with different characteristics, such as persons who are…
Descriptors: Error of Measurement, Error Correction, Predictor Variables, Monte Carlo Methods
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