71 courses with the subject ERM, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
Students will examine Federal, State, and classroom accountability systems. Enrollment is limited to students who are pursuing initial teaching licensure.
Students will examine standardized testing, key statistical terms, and the application of testing data to instructional planning. Enrollment is limited to students who are pursuing initial teaching licensure.
Development, monitoring, and evaluation of student progress through classroom assessments. Enrollment is limited to students who are pursuing initial teaching licensure in B-K, K-12, and secondary programs.
The fundamentals of measurement and assessment used in teaching middle and secondary grades, including the concepts of assessment types, assessment development, reliability, validity, interpreting test results.
This course provides students with an introductory set of tools for data literacy. The course introduces concepts related to the management, analysis, and presentation (figures and graphs) of data. While not a formal statistics class, fundamental concepts in statistics will be introduced as will relevant data management and statistical software.
As stewards of data and information you are responsible for the origination, storage, description, classification, and retrieval of data. Because of this, there may be times when you must work with programmers and analysts. This course provides the background to do this.
Students in this class will become familiar with the storage and retrieval of data (e.g., different formats and data types in addition to security, assessing public repositories, etc…). In addition, students will become familiar with the various techniques of presenting and summarizing basic and complex data sets.
This course is the second of a two-course series (the first course is ERM 411) that will develop the basic tools that are necessary to understand and communicate with individuals relevant to the data that is managed.
This course provides an overview of the basic concepts in program evaluation. Students will also be introduced to the practice of program evaluation.
Introductory course in applied descriptive statistics, correlational methods, and linear regression that provides a conceptual and theoretical foundation for more advanced work and a thorough grounding in the use of computers for descriptive statistical analysis, and interpretation of results.
Covers emerging technologies, new trends and issues, and other current developments in the field of data analytics.
Guided readings, research, and individual project work under direction of faculty.
Provides a supervised work experience for students in data analytics.
Validity is fundamental to all testing operations. The course covers seminal theories, critical perspectives, as well as prominent validation research of critical importance to professionals working in diverse industry settings.
Techniques and uses of research in education. Designed to provide the student with the ability to read, understand, and critically evaluate published empirical research.
For teachers, counselors, and administrators. Principles of measurement and evaluation, methods of scoring and interpreting tests. Construction and use of teacher-made tests. Statistical concepts basic to understanding and interpreting test data.
Theoretical and practical issues related to second language testing with special attention paid to the assessment of English as a second language, world Englishes, and foreign languages.
Language testing for English and foreign language learners, as well as the World Englishes domain. It investigates policies, theories, research, and tools employed to measure different language modalities.
Existing and emerging formulations of educational evaluation. Developing operational guidelines for conducting evaluations in educational settings.
An application course that uses modern evaluation models, data collection, statistical analyses, and interpretation of findings to establish the effectiveness and utility of an educational program.
This course provides a comprehensive overview of collaborative and participatory approaches to evaluation and research in educational settings and how these approaches are differentiated from other methodological approaches.
This course provides a comprehensive overview of culturally responsive approaches to evaluation and research in educational settings and includes both theoretical and practical applications.
This course will provide graduate students with a theoretical understanding of qualitative research and practical, hands-on experience conducting a small-scale study.
Statistical foundations, classical test theory, reliability, validity, item analysis, and norms; selected topics in modern test theory. Designed for those who will develop, evaluate, and select measurement instruments in their professional roles.
Theory, methods, and procedures of survey research as this methodology is applied to problems in education. Sampling from finite populations.
Conceptual and mathematical foundations, parameter estimation, tests of model assumptions and goodness of fit, and practical applications of IRT.
Modern techniques for summarizing and visualizing univariate and multivariate data using various statistical and graphical software packages. Covers theories and research on graphics and the perception of visual data.
Applied descriptive and inferential statistics. Topics include applied probability, power analysis, chi-square distributions, hypothesis testing for a variety of applications, and correlation and regression. Concept learning, applications, and computer analyses are stressed.
Advanced inferential statistics including factorial ANOVA, repeated measures design, multiple regression, ANCOVA, log-linear analysis. Applications in education and the social sciences.
Multivariate normal distribution. Cluster analysis, discriminant analysis, canonical correlation, principal component analysis, factor analysis, multivariate analysis of variance. Use and interpretation of relevant statistical software.
Using R to learn about programming fundamentals, data management, automating large numbers of analyses, conducting simulation studies, conducting specialized analyses, and producing high quality graphics.
Specific course title identified each semester by subscript, e.g., Contemporary Problems Seminar: Issues in Professional Negotiations.
Guided readings, research, and individual project work under direction of a staff member.
Validity is fundamental to all testing operations. The course covers seminal theories, critical perspectives, as well as prominent validation research of critical importance to professionals working in diverse industry settings.
Techniques and uses of research in education. Designed to provide the student with the ability to read, understand, and critically evaluate published empirical research.
This course will provide students with a critical understanding of the multiple connections (and disconnections) between public policy and evaluation.
Technical developments and applications in classical test theory, item response theory, generalizability theory, models of selection bias, differential item functioning, and test score equating.
Computer-based testing applications including automated test assembly, item banking, computer-adaptive and multistage testing, web-based testing, large scale assessment development and support systems, and computer-based performance assessments. Covers state-of-the-art research and developments.
Field-based and mentored practicum.
Formulation of structural models, estimation of structural coefficients using LISREL, estimation of model fit, confirmatory factor analysis models, practical applications.
Structure of hierarchical data, random intercepts, individual change/growth models, applications in meta-analysis, assessing hierarchical models, hierarchical generalized linear models, hierarchical models for latent variables, cross-classified random effects, estimation.
Theoretical and practical issues related to second language testing with special attention paid to the assessment of English as a second language, world Englishes, and foreign languages.
Equating designs, equating and scaling assumptions, design of anchor sets, observed score equating methods, true-score equating methods, standard error of equating, use and interpretation of relevant statistical software.
Language testing for English and foreign language learners, as well as the World Englishes domain. It investigates policies, theories, research, and tools employed to measure different language modalities.
Philosophy of diagnostics models, DINA/DINO model, RUM, Log-Linear Cognitive Diagnosis Model, Test Construction, Estimation of Diagnostic Models, Application of Diagnostic Models.
Existing and emerging formulations of educational evaluation. Developing operational guidelines for conducting evaluations in educational settings.
Theoretical understanding of evaluation design and strengthening of practical program evaluation skills.
Advanced seminar of complex issues in program evaluation. Students will develop a deep synthesis of the major approaches and theories of evaluation. The purpose of evaluation in society will be examined.
This course provides a comprehensive overview of collaborative and participatory approaches to evaluation and research in educational settings and how these approaches are differentiated from other methodological approaches.
This course provides a comprehensive overview of culturally responsive approaches to evaluation and research in educational settings and includes both theoretical and practical applications.
This course will provide graduate students with a theoretical understanding of qualitative research and practical, hands-on experience conducting a small-scale study.
Overview of the methodology of case study research; enhancement of students' skills in using case study methods.
An application course that uses modern evaluation models, data collection, statistical analyses, and interpretation of findings to establish the effectiveness and utility of an educational program.
Statistical foundations, classical test theory, reliability, validity, item analysis, and norms; selected topics in modern test theory. Designed for those who will develop, evaluate, and select measurement instruments in their professional roles.
Theory, methods, and procedures of survey research as this methodology is applied to problems in education. Sampling from finite populations.
Conceptual and mathematical foundations, parameter estimation, tests of model assumptions and goodness of fit, and practical applications of IRT.
Estimation techniques for various unidimensional dichotomous and polytomous IRT models using various software packages. IRT applications such as computerized testing, equating, test construction, and differential item/test functioning are also covered.
Multidimensional item response theory models including their estimation, representation, and application. Use of relevant estimation and graphing software discussed.
Modern techniques for summarizing and visualizing univariate and multivariate data using various statistical and graphical software packages. Covers theories and research on graphics and the perception of visual data.
Individual work on dissertation research problems: collection and analysis of data; critical review, integration, and interpretation of research literature on a topic pertinent to the student's dissertation.
Applied descriptive and inferential statistics. Topics include applied probability, power analysis, chi-square distributions, hypothesis testing for a variety of applications, and correlation and regression. Concept learning, applications, and computer analyses are stressed.
Advanced inferential statistics including factorial ANOVA, repeated measures design, multiple regression, ANCOVA, log-linear analysis. Applications in education and the social sciences.
Multivariate normal distribution. Cluster analysis, discriminant analysis, canonical correlation, principal component analysis, factor analysis, multivariate analysis of variance. Use and interpretation of relevant statistical software.
Using R to learn about programming fundamentals, data management, automating large numbers of analyses, conducting simulation studies, conducting specialized analyses, and producing high quality graphics.
Specific course title identified each semester by subscript, e.g., Contemporary Problems Seminar: Issues in Professional Negotiations.
Guided readings, research, and individual project work under direction of a staff member.
Individual direction in the development and execution of a doctoral dissertation.
Thesis Extension.
Dissertation Extension.
Research Extension.
Source: University of North Carolina at Greensboro's catalog, linked per course · table learning_unit · CourseShelf publish 61
