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Aarnes Gudmestad; Thomas A. Metzger – Language Learning, 2025
In this Methods Showcase Article, we illustrate mixed-effects modeling with a multinomial dependent variable as a means of explaining complexities in language. We model data on future-time reference in second language Spanish, which consists of a nominal dependent variable that has three levels, measured over 73 participants. We offer step-by-step…
Descriptors: Second Language Learning, Spanish, Applied Linguistics, Predictor Variables
Jacquelynne S. Eccles; Allan Wigfield – Educational Psychology Review, 2024
To address the seven guiding questions posed for authors of articles in this special issue, we begin by discussing the development (in the late 1970s-early 1980s) of Eccles' expectancy-value theory of achievement choice (EEVT), a theory developed to explain the cultural phenomenon of why girls were less likely to participate in STEM courses and…
Descriptors: Educational Theories, Academic Achievement, Females, Student Participation
Nancy Montes; Fernanda Luna – UNESCO International Institute for Educational Planning, 2024
This article characterizes and reflects on the possible uses of early warning systems (hereafter, EWS) in the region as effective tools to support educational pathways, whenever they identify risks of dropout, difficulties for the achievement of substantive learning, and the possibility of organizing specific actions. This article was developed in…
Descriptors: Data Collection, Data Use, At Risk Students, Foreign Countries
Quille, Keith; Bergin, Susan – Computer Science Education, 2019
Background and Context: Computer Science attrition rates (in the western world) are very concerning, with a large number of students failing to progress each year. It is well acknowledged that a significant factor of this attrition, is the students' difficulty to master the introductory programming module, often referred to as CS1. Objective: The…
Descriptors: Computer Science Education, Introductory Courses, Programming, Student Attrition
Rubin, Marc – New England Journal of Higher Education, 2017
The "2018 Guide to New England Colleges & Universities" data published by "Boston" magazine in association with the New England Board of Higher Education provided the author the opportunity to examine the schools' "prices," defined as "tuition plus fees," as a function of several independent factors. By…
Descriptors: Fees, Institutional Characteristics, Paying for College, Student Costs
Balch, Ryan; Koedel, Cory – Education Policy Analysis Archives, 2014
State and local education agencies across the United States are increasingly adopting rigorous teacher evaluation systems. Most systems formally incorporate teacher performance as measured by student test-score growth, sometimes by state mandate. An important consideration that will influence the long-term persistence and efficacy of these systems…
Descriptors: Teacher Evaluation, Stakeholders, Teacher Attitudes, Observation
Sternberg, Robert J. – Creativity Research Journal, 2015
Universities, like students, differ in their ability to learn and to recreate themselves. In this article, I present a 3-part model of institutional creative change for assessing universities as learning organizations that can move creatively into the future. The first part, prerequisites, deals with actual ability to change creatively and belief…
Descriptors: Universities, Higher Education, Creativity, Models
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
Wise, Alyssa Friend; Shaffer, David Williamson – Journal of Learning Analytics, 2015
It is an exhilarating and important time for conducting research on learning, with unprecedented quantities of data available. There is a danger, however, in thinking that with enough data, the numbers speak for themselves. In fact, with larger amounts of data, theory plays an ever-more critical role in analysis. In this introduction to the…
Descriptors: Learning Theories, Predictor Variables, Data, Data Analysis
Harris, Frank, III; Luke Wood, J. – New Directions for Community Colleges, 2016
The authors of this chapter present the Socio-Ecological Outcomes Model as a means of predicting student success for community college men of color.
Descriptors: Social Influences, Ecological Factors, Outcomes of Education, Models
Scaling Student Success with Predictive Analytics: Reflections after Four Years in the Data Trenches
Wagner, Ellen; Longanecker, David – Change: The Magazine of Higher Learning, 2016
The metrics used in the US to track students do not include adults and part-time students. This has led to the development of a massive data initiative--the Predictive Analytics Reporting (PAR) framework--that uses predictive analytics to trace the progress of all types of students in the system. This development has allowed actionable,…
Descriptors: Predictor Variables, Reflection, Academic Achievement, Models
Ren, Zhiyun; Rangwala, Huzefa; Johri, Aditya – International Educational Data Mining Society, 2016
The past few years has seen the rapid growth of data mining approaches for the analysis of data obtained from Massive Open Online Courses (MOOCs). The objectives of this study are to develop approaches to predict the scores a student may achieve on a given grade-related assessment based on information, considered as prior performance or prior…
Descriptors: Large Group Instruction, Online Courses, Educational Technology, Technology Uses in Education
Moss, Tony – Kansas State Department of Education, 2017
The purpose of this paper is to open the methods, construction and assumptions of a new performance benchmark to examination, critique, and improvement by technical experts. The paper begins with known technical questions about this new benchmark. In includes suggestions made by technical advisors. It then details the construction of the…
Descriptors: Educational Assessment, Performance Based Assessment, Benchmarking, State Departments of Education
Chatterjee, Samprit; Hadi, Ali S. – John Wiley & Sons, Inc, 2012
Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…
Descriptors: Regression (Statistics), Data Analysis, Statistical Analysis, Models
McCoach, D. Betsy; Black, Anne C. – New Directions for Institutional Research, 2012
This article is designed to give the reader a conceptual, nontechnical overview of estimation and model fit issues in multilevel modeling (MLM). The process of MLM generally involves fitting a series of multilevel models that increase in complexity. When conducting multilevel analyses, it is important to balance the need for complexity and the…
Descriptors: Institutional Research, Statistical Analysis, Models, Computation