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.
Cognitive and information-processing approaches to attention, learning, language, memory, and reasoning.
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.
Learning Analytics, Theory & Practice builds on HUDK 4050 Core Methods in Educational Data Mining to provide 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.
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.
Surveys production and comprehension of visualizations ranging from ancient cave paintings and petroglyphs to diagrams, charts, graphs, comics, picture books, photographs, gesture, and film to extract and apply techniques for conveying objects, actions, forces relations, and emotions, meanings that are both inherently visible and non-visible. Implications for education, art, media, and HCI are drawn.
Feature Engineering Studio is a core course of the Learning Analytics Program and preference is given to students within this course of study. FES is a design studio style course that tackles real world data problems associated with technology use in education. Students will work in groups with outside organizations on data projects pertinent to educational problems. They will be required to respond to briefs supplied by the organizations and perform all parts of the workflow to generate data solutions for those organizations including, data cleaning and access, feature engineering and distillation, visualization, and final deliverables.