Introduction to Statistics with R for TEL

Introduction to Statistics with R for TEL

📅 Friday 22/05 11:00-12:30h

📍 Workshop Space A

🔎 Needs Analysis

PhD candidates in TEL and EdTech typically generate and collect diverse data across their projects, such as LMS interaction logs, assessment results, collaboration traces, and questionnaire responses. Analyzing this data demands both statistical knowledge and skill with practical tools. This workshop addresses the need for a reliable, free, and research-grade analysis environment by introducing R and RStudio as widely used open-source tools for TEL data analysis. Participants will gain an overview of relevant packages for common TEL data types and learn how to structure analyses in a reproducible way. In addition, the workshop highlights how Quarto can support publishing and sharing results and how Shiny apps can be used to transform analyses into reusable interactive tools. Hands-on experience with common analyses of TEL data helps PhD candidates select suitable analysis workflows in R and RStudio, or consider alternative tools that fit their TEL research contexts better.

📒 Session Description

In this workshop, participants will learn about statistical methods applied in R and RStudio relevant to TEL. After setting up R/RStudio on their computers or laptops, we will explore various analyses interactively. The workshop will showcase example analyses, leaving gaps in the script for participants to practice with different data types, such as LMS, assessment, collaboration, and questionnaire data. Participants will then critically evaluate the suitability of R/RStudio and discuss potential alternatives. This session aims not only to enable participants to operate RStudio but also to develop an understanding of when it is the preferred tool and when alternative methods should be considered.

💡 Learning Objectives

By the end of the workshop, participants will be able to:

1. Set up and operate R and RStudio, ready for TEL data types
2. Explore and apply basic statistical methods to typical TEL data sources, such as LMS, assessment, collaboration, and questionnaire data
3. Select and use suitable R packages for cleaning, analyzing, and visualizing their own TEL datasets
4. Understand how to structure analyses for reproducibility and reusability
5. Consider when to use Quarto for reporting, presenting, or publishing results
6. Assess when R/RStudio might not be suitable due to practical or methodological limitations
7. Critically evaluate alternative tool options and make informed decisions about analysis workflows for their PhD projects