The Learning Analytics Graduate Academic Certificate

As increasing amounts of data are collected in learning environments, analysts are needed who can collect, interpret, and analyze data to improve learning outcomes and processes. Individuals who qualify for these positions must be able to apply not only technical and analytical skills, but also an understanding of learning theories and instructional design. This unique combination of skills and knowledge is needed for developing and implementing effective learning solutions.
Building on the Master of Science degree in Learning Technologies (Learning Analytics concentration), the 4-course graduate academic certificate (GAC) in Learning Analytics prepares professionals to apply data-driven techniques to understand and improve learning processes both in education and in business. The Learning Analytics certificate provides students with scaffolded skill development as they apply learning analytics algorithms and techniques to real-world educational and training datasets and case studies. Learning is deepened through hands-on activities that support learners in building skills while analyzing real-world, contextualized datasets and summarizing important variables and model predictions. 

Individuals who complete the LA GAC will:

  • Develop foundational programming skills in R/Python.
  • Build analytical skills for predictive modeling and advanced text analysis.
  • Collect and analyze unstructured data.
  • Design and apply AI-driven solutions for educational needs/assessment.

A total of 12 hours, or 4 courses, is needed to complete the certificate:

Required Courses:
LTEC 5601 - Introduction to Learning Analytics

Provides an introduction to learning analytics with a focus on Python programming and Exploratory Data Analysis (EDA) in educational contexts. Tailored for students interested in applying data-driven techniques to understand and improve learning processes. Learn the fundamentals of Python programming, including data manipulation and visualization, while working with real-world education datasets. Through hands-on projects, explore patterns in student performance, engagement, and learning behaviors, gaining practical skills relevant to educational research and decision-making. 

LTEC 5602 - Predictive Modeling in Learning Analytics

Explores predictive modeling techniques in the context of learning analytics, focusing on how machine learning can be applied to educational data to inform decision-making and improve student learning outcomes. Emphasizes the unique challenges and opportunities in analyzing student performance, engagement, and learning behaviors. Learn and apply various machine learning algorithms and techniques, including multiple regression, logistic regression, decision trees, and random forests, to real-world education datasets. Through hands-on exercises and case studies, develop the skills to build, interpret, and evaluate predictive models tailored to educational research and policy. 

Prerequisite(s): LTEC 5601.

LTEC 5603 - Text Mining and Natural Language Processing in Learning Analytics
Focuses on text mining and Natural Language Processing (NLP) techniques specifically applied to learning analytics. Emphasizes analyzing educational text data, such as student feedback, discussion forums, and learning materials, to extract insights that inform teaching and learning. Explores sentiment analysis, topic modeling, and advanced text analysis using Large Language Models (LLM) and Generative AI (GAI). Through hands-on projects with education-related datasets, develop practical skills in processing and interpreting unstructured text data to enhance student learning experiences and educational decision-making

Prerequisite(s): LTEC 5601.
LTEC 5604 - Dashboard Design in Learning Analytics

Capstone course focuses on the design, development, and deployment of interactive dashboards for learning analytics. Integrate concepts from previous courses to create data-driven visualizations that address real-world educational challenges. Using various Python frameworks, build dynamic dashboards that communicate insights from Exploratory Data Analysis (EDA), predictive models, and text analysis in meaningful ways for educators, administrators, and policymakers. Equips students with the technical and design skills necessary to translate complex learning analytics into actionable insights through interactive applications. 

Prerequisite(s): LTEC 5602 or LTEC 5603.