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Bezek Güre, Özlem; Sevgin, Hikmet; Kayri, Murat – International Journal of Contemporary Educational Research, 2023
The research aims to determine the factors affecting PISA 2018 reading skills using the Random Forest and MARS methods and to compare their prediction abilities. This study used the information from 5713 students, 2838 (49.7%) male and 2875 (50.3%) female, in the PISA 2018 Turkey. The analysis shows the MARS method performed better than the Random…
Descriptors: Achievement Tests, International Assessment, Secondary School Students, Foreign Countries
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MD, Soumya; Krishnamoorthy, Shivsubramani – Education and Information Technologies, 2022
In recent times, Educational Data Mining and Learning Analytics have been abundantly used to model decision-making to improve teaching/learning ecosystems. However, the adaptation of student models in different domains/courses needs a balance between the generalization and context specificity to reduce the redundancy in creating domain-specific…
Descriptors: Predictor Variables, Academic Achievement, Higher Education, Learning Analytics
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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
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Kemper, Lorenz; Vorhoff, Gerrit; Wigger, Berthold U. – European Journal of Higher Education, 2020
We perform two approaches of machine learning, logistic regressions and decision trees, to predict student dropout at the Karlsruhe Institute of Technology (KIT). The models are computed on the basis of examination data, i.e. data available at all universities without the need of specific collection. Therefore, we propose a methodical approach…
Descriptors: Foreign Countries, Predictor Variables, Potential Dropouts, School Holding Power
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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
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Cipani, Ennio – Research on Social Work Practice, 2014
In this article, I address the issue of comorbidity and its prevalence in the prior "Diagnostic and Statistical Manual of Mental Disorders" ("DSM") classification systems. The focus on the topography or form of presenting problems as the venue for determining mental disorders is scrutinized as the possible cause. Addressing the…
Descriptors: Mental Disorders, Classification, Comorbidity, Behavior Problems
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Hammonds, Frank; Mariano, Gina – International Journal of Teaching and Learning in Higher Education, 2015
Research on variables related to test performance has produced mixed results. Typically, research of this type involves only a few variables. In an attempt to obtain a more complete picture, we investigated how test grades might be related to variables such as classification, student seating location, test completion time, predicted grade, time…
Descriptors: Grades (Scholastic), Predictor Variables, Student Attitudes, Questionnaires
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Crossley, Scott A.; Kim, YouJin – Language Assessment Quarterly, 2019
The current study examined the effects of text-based relational (i.e., cohesion), propositional-specific (i.e., lexical), and syntactic features in a source text on subsequent integration of the source text in spoken responses. It further investigated the effects of word integration on human ratings of speaking performance while taking into…
Descriptors: Individual Differences, Syntax, Oral Language, Speech Communication
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Truckenmiller, Adrea J.; Petscher, Yaacov; Gaughan, Linda; Dwyer, Ted – Regional Educational Laboratory Southeast, 2016
District and state education leaders frequently use screening assessments to identify students who are at risk of performing poorly on end-of-year achievement tests. This study examines the use of a universal screening assessment of reading skills for early identification of students at risk of low achievement on nationally normed tests of reading…
Descriptors: Prediction, Predictive Validity, Predictor Variables, Mathematics Achievement
Lopez, M. I.; Luna, J. M.; Romero, C.; Ventura, S. – International Educational Data Mining Society, 2012
This paper proposes a classification via clustering approach to predict the final marks in a university course on the basis of forum data. The objective is twofold: to determine if student participation in the course forum can be a good predictor of the final marks for the course and to examine whether the proposed classification via clustering…
Descriptors: Classification, Prediction, Grades (Scholastic), College Freshmen
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Lam, Elizabeth A.; McMaster, Kristen L. – Learning Disability Quarterly, 2014
The purpose of this review was to update previous reviews on factors related to students' responsiveness to early literacy intervention. The 14 studies in this synthesis used experimental designs, provided small-group or one-on-one reading interventions, and analyzed factors related to responsiveness to those interventions. Participants were…
Descriptors: Predictor Variables, Early Intervention, Emergent Literacy, Longitudinal Studies
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Cooper, Kristy S. – American Educational Research Journal, 2014
This case study analyzes how and why student engagement differs across 581 classes in one diverse high school. Factor analyses of surveys with 1,132 students suggest three types of engaging teaching practices--connective instruction, academic rigor, and lively teaching. Multilevel regression analyses reveal that connective instruction predicts…
Descriptors: Teaching Methods, High School Students, Learner Engagement, Regression (Statistics)
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Edison, Shannon C.; Evans, Mary Ann; McHolm, Angela E.; Cunningham, Charles E.; Nowakowski, Matilda E.; Boyle, Michael; Schmidt, Louis A. – Child Psychiatry and Human Development, 2011
The authors examined parent-child interactions among three groups: selectively mute, anxious, and non-anxious children in different contexts. The relation between parental control (granting autonomy and high power remarks), child factors (i.e., age, anxiety, verbal participation), and parent anxiety was investigated. Parental control varied by…
Descriptors: Parent Child Relationship, Anxiety, Parent Attitudes, Classification
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Schumacher, Phyllis; Olinsky, Alan; Quinn, John; Smith, Richard – Journal of Education for Business, 2010
The authors extended previous research by 2 of the authors who conducted a study designed to predict the successful completion of students enrolled in an actuarial program. They used logistic regression to determine the probability of an actuarial student graduating in the major or dropping out. They compared the results of this study with those…
Descriptors: Regression (Statistics), Classification, Probability, Comparative Analysis
Castellano, Katherine E.; Ho, Andrew D. – Council of Chief State School Officers, 2013
This "Practitioner's Guide to Growth Models," commissioned by the Technical Issues in Large-Scale Assessment (TILSA) and Accountability Systems & Reporting (ASR), collaboratives of the "Council of Chief State School Officers," describes different ways to calculate student academic growth and to make judgments about the…
Descriptors: Guides, Models, Academic Achievement, Achievement Gains
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