Fundamentals of Learning Analytics

3 credits · Peabody College - Professional

This course will provide students with the foundational skills and knowledge needed to computationally analyze learner data. These skills will include sourcing educational datasets, developing appropriate analysis pipelines, communicating findings, and experimenting with data wrangling and cleaning, data visualization, machine learning, and text analytics. Specifically, students will be introduced to the R programming language and appropriate R packages (e.g., tidyverse, CARET, and tidytext), relational data, plotting and graphing data, linear and categorical modeling, data splitting and resampling, feature selection and overfitting, data reduction, reproducibility, sentiment analysis, and topic modeling. The course will be practice-based, and the final project will ask students to analyze data of interest to their field of research, discuss the data from an analytics perspective, and model learning outcomes. Serves as repeat credit for SPED 7715 and PSY-PC 7715. [3]