The Department of Human Development is devoted to promoting an understanding of human development in families, schools, and institutions across the lifespan. The department provides social scientists and educators with theories, empirical methods, and analytical tools for understanding and conducting research in human development and cognition and for helping solve educational and psychological problems.
For up to date information about course offerings including faculty information, please visit the online course schedule.
Preparation for doctoral study. Presentations and discussions of research by faculty, visiting scholars, and students.
The course explores the social dimensions of online learning. The course begins by reviewing the uniquely social dimensions of learning in general and then turns to an examination of the transition to the information age that has made online or networked learning possible. The course next covers how traditional social forms such as classrooms, schools, professions, and libraries have been represented in online learning venues followed by consideration of new and emerging social forms such as digital publishing, social networks and social media, adaptive learning technologies, and immersive and interactive environments. The course concludes by examining macro level factors that shape the opportunities for online learning.
Examination of mobile phone technologies, designing learning activities for mobile phones, and pedagogical and theoretical frameworks.
The development of informal and formal mathematical thinking from infancy through childhood with implications for education.
Prerequisite: HUDM 5122 or equivalent, or approved statistics or computer science data mining course. Methods of educational data mining focused on automated discovery of patterns in large-scale educational data, execution of methods in standard software packages, limitations of existing implementations of methods, and when and why to use these methods. Discussion of how EDM uses some of the same mathematical frameworks as traditional statistics and how their use in EDM differs.
Prerequisite: Course in statistics is recommended. Framework for understanding the emerging field of learning analytics. Examines primary perspectives on what the field should be, including educational data mining, learning analytics and big data perspectives, and relationships to related and existing fields. Includes perspectives on philosophy and theory of design and inquiry, validity of a learning analytics analysis or model, and challenges to its effectiveness and appropriate use.
Introduction to multiple perspectives on activities connected to progress in our capacity to examine learning and learners, represented by the rise of learning analytics. Students develop strategies for framing and responding to the ranges of values-laden opportunities and dilemmas presented to research, policy, and practice communities as a result of the increasing capacity to monitor learning and learners.
Attaining, compiling, analyzing, and reporting data for academic research. Includes data definitions, forms, and descriptions; data and the research lifecycle; data and public policies; and data preservation practices, policies, and costs.
Seminar focusing on the case study method for understanding the principles and concepts underlying creative individuals and their products.
Investigation of the major theoretical and empirical approaches to the study of how morality develops with particular emphasis on the behaviorist, cognitive, psychoanalytic, and sociopolitical approaches.
Explores television-related media designed for pre-schoolers and young children from cognitive and developmental perspectives. Focuses on the psychological roles of television in regard to family and peer relationships, education, and social issues. Topics include hidden/visible curriculum development and cognitive research techniques relative to production.
Using contemporary research as the basis, the focus is on the interface between classical developmental psychology theories and patterns of development identified in atypical contexts (e.g., poverty) and among atypical populations (e.g., resilient youth). Implications for interventions and policy are also discussed.
Overview of student motivation in an academic context from a cognitive psychology perspective. Examines theories of academic motivation, their constructs, and linkages between constructs and student engagement and academic achievement.
Prerequisite: HUDK 4050. Design studio-style course covers the process of feature engineering and distillation, including brainstorming features, deciding what features to create, and criteria for selecting features for data mining. Students learn how to create features in Excel, Java, Google Refine, EDM Workbench, and other relevant tools.
Examination of all aspects of cognitive functioning over the major portion of the life cycle that occurs beyond childhood, addressing both common patterns and individual and cultural variations. A particular focus will be critical examination of the research methods by which such knowledge is gained.
Contemporary theory and research on children adaptation to developmental tasks of childhood. Comparison of typical and atypical pathways in social-personality development. Analysis of the logic and method of empirical studies of development.
Survey of psychological studies of development in different cultures, with emphasis on perceptual and cognitive issues and methodological problems specific to cross-cultural research.
The overall goal of the course is for students to gain understanding of how income and child development intersect. Students will learn about poverty and child development from psychological, economic, sociological, demographic, and biological perspectives
Design of e-learning in workplace environments, from a perspective that looks to put academic research into practice. Real-world cases, including numerous demonstrations of real-life courses and systems, will be used to explore uses of e-learning in the workplace for both training and "just-in-time" performance support purposes.
Two-semester course taught by faculty from Human Development and Education Policy and Social Analysis. Course links research and policy perspectives on early childhood with a focus on contemporary challenges in the field.
Provides a multi-disciplinary perspective on child and family policy. Also provides a foundation of knowledge concerning the role of child and family perspectives in informing policy.
Individual advisement on doctoral dissertation. Fee to equal 3 points at current tuition rate for each term. See the section on Continuous Registration for Ed.D./Ph.D. degrees for details.
Individual advisement on doctoral dissertation. Fee to equal 3 points at current tuition rate for each term. See catalog section on Continuous Registration for Ed.D./Ph.D. degrees.
Designed as a one‑semester introduction to statistical concepts and methods. An overview of data analysis techniques, including organizing, graphing, analyzing, reporting, and interpreting data. Both descriptive and inferential techniques will be introduced. Use of statistical software is discussed.
An introduction to statistical theory, including elementary probability theory; random variables and probability distributions; sampling distributions; estimation theory and hypothesis testing using binomial, normal, T, chi square, and F distributions. Calculus not required.
Prerequisite: Course in Calculus. Calculus-based introduction to mathematical statistics. Topics include an introduction to calculus-based probability; continuous and discrete distributions; point estimation; method of moments and maximum likelihood estimation; properties of estimators including bias and mean squared error; large sample properties of estimators; hypothesis testing including the likelihood ratio test; and interval estimation.
Students in this lab must also be enrolled in HUDM 5122 or HUDM 5123.
Prerequisite: HUDM 4122.This course provides an introduction to the R language and environment for statistical computing with an emphasis on the application of fundamental graphical and statistical techniques. While some theory will be presented (for example, when discussing regression models), the focus will be on implementation and interpretation as opposed to study of the statistical properties of the methods.
Prerequisite: HUDM 4122 or equivalent. Surveys quantitative models of individual decision making, from the introduction of the notion of "utility" by Daniel Bernoulli through current models such as Tversky and Kahneman's "Prospect Theory." The focus is on psychological or descriptive models of how people make decisions, although methods of rational decision analysis are briefly discussed.
A previous course in statistics or measurement is recommended. An in-depth examination of measurement and associated techniques, norms, classical test theory, reliability, validity, item response theory, issues, and applications.
This course provides an introduction to the R language and environment for statistical computing with an emphasis on the application of fundamental graphical and statistical techniques. While some theory will be presented (for example, when discussing regression models), the focus will be on implementation and interpretation as opposed to study of the statistical properties of the methods.
Least squares estimation theory. Traditional simple and multiple regression models and polynomial regression models, including use of categorical predictors. Logistic regression for dichotomous outcome variables is also covered. Lab meetings devoted to applications of SPSS regression program.
Analysis of variance models including single and multiple factor experiments, between-subject and within-subject designs, trend analysis, factorial and nested designs, random effects, analysis of covariance, and blocking. Lab devoted to computer applications.
Prerequisites: HUDM 4122 and HUDM 5122 or equivalent. Methods of analyzing proximity data (similarities, correlations, etc.), including multidimensional scaling, which represents similarities among items by plotting the items into a geometric space, and cluster analysis for grouping items.
Introduction to the theory and application of linear regression using calculus and matrix algebra. Focus on multiple regression models including dummy variables and polynomial models, regression diagnostics, and advanced methods such as weighted least squares, multilevel models, and an introduction to the generalized linear model.
Introduces practical and methodological issues for meta-analysis including program formation, literature search, data eveluation, effect size coding, data analysis, reporting results, summarizing effects, and combing results.
Prerequisite: HUDM 5122. The Neyman-Rubin potential outcomes notation and Pearl’s theory of directed graphs will be used to examine issues relevant to the design and statistical analysis of randomized experiments and quasi-experiments. For quasi-experimental designs, we will focus on non-equivalent control group designs, regression-discontinuity designs and instrumental variables designs.
Prerequisite: 24 points completed towards MS Applied Statistics degree. This is a capstone course to the M.S. in Applied Statistics degree. In it students will discuss best practices in statistical analyses, including the role of a consultant and ethical issues encountered in analyses. Students will also study best practices for effective communication of statistics, including verbal, written, and graphical. Students will produce a capstone paper integrating the methods and skills they have learned across the M.S. degree.
Prerequisite: HUDM 4125 and either HUDM 5122 or HUDM 5126. Provides an introduction to computationally intense methods in applied statistics, taught in R. Topics include methods of evaluating statistical estimators; design, implementation, and reporting of Monte Carlo simulation studies; resampling and reordering methods; and nonparametric and data mining approaches to regression.
Permission required. Prerequisites: HUDM 5059, HUDM 5122, or equivalents. Classical test theory and applications and test/instrument development and validation.
Permission required. Prerequisites: HUDM 6052 or equivalent. Item response theory and applications and cognitive diagnostic models.
Prerequisite: HUDM 5122. Recommended: HUDM 6122. Study of latent structure analysis, including measurement models for latent traits and latent classes, path analysis, factor analysis, structural equations, and categorical data analysis.
Prerequisite: HUDM 5122 or HUDM 5126; HUDM 5123 is recommended. An introduction to multivariate statistical analysis, including matrix algebra, general linear hypothesis and application, profile analysis, principal components analysis, discriminant analysis, and classification methods.
Permission required. Development of doctoral dissertations and presentation of plans for approval. Registration limited to two terms. Ph.D & Ed.D students must complete 3 points over 2 semesters prior to proposing their dissertation.
Individual advisement on doctoral dissertation. Fee to equal 3 points at current tuition rate for each term. See section in catalog on Continuous Registration for Ed.D./ Ph.D. degrees. Ed.D & Ph.D students must register for this every semester while completing their dissertation.