Location: Life Sciences Building, Room 313,
501 S. Nedderman Dr., Arlington, TX 76019
Mailing address: P.O. Box 19528
Course of Study
This course will provide students who receive probationary admission due to an inadequate mathematical background with the core principles of statistical analysis necessary to be successful in the program.
Examination of probability, distributions, estimation, and hypothesis testing in learning contexts.
A comprehensive review of different regression models that emphasizes modeling, inference, diagnostics, and application to educational datasets.
In-depth exploration of univariate and multivariate linear models to derive inferential procedures depending on appropriate learning contexts.
In-depth study of the investigation of observed similarities and dissimilarities between different objects and then grouping the objects based on those similarities.
Using learning analytics to determine the impact of intervention outcomes and critically evaluate quantitative research pertaining to cause and effect in a learning context. This will include potential pitfalls and key factors, as well as application of both practitioner and research lenses.
Sophisticated and emerging techniques for analyzing learning data, including advanced graphing and visualization techniques, multimodal data (such as psychophysiological data), modeling, process mining, measurement of psychological attributes involved in knowledge creation, and learner profile development.
Survey of foundational learning design theories related to human behavior in formal and informal learning settings. Focus on models and strategies to design and evaluate technology that supports and helps improve learning.
Student and instructor agree upon topic of study and requirements for deadlines and products.
The role of learning, sensemaking, human development, and cognition theories in relation to the use of digital technology in knowledge processes.
Application of methods in natural language processing (NLP) and natural language understanding (NLU) to text and language data in the educational setting.
Introduction to social network analysis in educational settings. The course focuses on how to analyze and interpret relationships between people, artifacts, and text in digital learning settings. The students will learn to prepare data, map and analyze these relationships. Foundational graph analysis concepts and their application in learning analytics will be discussed. Students will be trained to use R programming for analysis, but the use of other software is possible.