Learning Analytics

HUDK4011 Networked and Online Learning

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.

HUDK4031 Data, Testing, and Meritocracy

Individuals in modern societies live in a world in which evaluation is ubiquitous. More and more aspects of our performances are subject to informal and/or formal assessment. Everything from our health as infants, to our performance in schools as youngsters, our potential to benefit from higher education, and our capacity to contribute in the workplace is evaluated. This course examines the social dimensions of the development and operation of different kinds of evaluation systems in modern societies. Major topics include the social, political, and intellectual contexts for evaluation, the institutional bases of evaluation activities, the social settings in which evaluation takes place, and the effects of evaluations on individuals and groups

HUDK4050 Core methods in Educational Data Mining

The Internet and mobile computing are changing our relationship to data. Data can be collected from more people, across longer periods of time, and a greater number of variables, at a lower cost and with less effort than ever before. This has brought opportunities and challenges to many domains, but the full impact on education is only beginning to be felt. Core Methods in Educational Data Mining provides an overview of the use of new data sources in education with the aim of developing students’ ability to perform analyses and critically evaluate their application in this emerging field. It covers methods and technologies associated with Data Science, Educational Data Mining and Learning Analytics, as well as discusses the opportunities for education that these methods present and the problems that they may create. The overarching goal of this course is for students to acquire the knowledge and skills to be intelligent producers and consumers of data mining in education. By the end of the course students should be able to systematically develop a line of inquiry utilizing data to make an argument about learning and be able to evaluate the implications of data science for educational research, policy, and practice.

HUDK4051 Learning Analytics: Process and theory

Learning Analytics, Theory & Practice provides advanced techniques in the use of new data sources in education with the aim of developing students’ ability to perform analyses and critically evaluate their application in this emerging field. It covers methods and technologies associated with data science, machine learning and learning analytics, as well as discusses the opportunities for education that these methods present and the problems that they may create.

HUDK4052 Data, Learning, and Society

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.

HUDK4054 Managing educational data

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.

HUDK5053 Learning Analytics Practicum

Learning Analytics Practicum is a core course of the M.S. in Learning Analytics Program and a gateway for students to transition from their education to a professional career. The course introduces principles and procedures in real-world educational data problems, provides support for students’ capstone projects with external organizations, and helps students access resources and develop skills necessary for a career in education and data science.

HUDK5100 Supervised Research & Practice

HUDK5324 Research Work Practicum

Students learn research skills by participating actively in an ongoing faculty research project.

HUDM4122 Probability and statistical inference

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.

HUDM4125 Statistical inference

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.

HUDM5122 Applied Regression Analysis

Prerequisite: HUDM 4122, HUDM 4125, HUDM 4120 or equivalent. This course is an introduction to regression with emphasis on the practical aspects. Topics include: simple linear regression, multiple linear regression, regression with categorical independent variables and/or interactions, role of assumptions, model diagnostics, and generalized linear models. Class time includes lab time devoted to applications with IBM SPSS.