ajayiea / laser-orientation

This learning lab orientation focuses on reproducible research and the data-intensive research workflow and is designed to orient learners to the instructional activities that make up each LASER learning lab.

LASER Learning Lab Orientation

The LASER learning lab orientation focuses on reproducible research and the data-intensive research workflow and is designed to orient you to the following instructional activities that make up each LASER learning lab:

1. Overview Presentation: Each lab consists of a two-part interactive presentation 60-minutes in length and includes an overview of key concepts and an interactive code-along. Part I of this presentation provides an introduction to reproducible research, including best practices and tools of reproducible research we'll be using throughout the LASER Institute. Part II provides a short but highly structured code-along activity that demonstrates the data-intensive research workflow and highlights key R packages and functions for wrangling, exploring, and modeling data.

2. Case Study: Case studies are interactive coding experiences that expand upon the code-along introduced in the overview presentation and can be completed independently or in small groups. These activities demonstrate how key data-intensive research workflow processes (i.e., wrangling, visualizing, summarizing, modeling, and communicating data) featured in exemplary STEM education research studies are implemented in R. Our first case study will also introduce you to this workflow as well as R Markdown, which is heavily integrated into each LASER Learning Labs.

3. Essential Readings: Each lab includes curated readings to help scholars dive deeper into LA concepts, techniques, and applications introduced in presentation and case studies. Each module includes an exemplary research article that illustrates how LA applications and/or techniques highlighted in each lab (e.g., data visualization, topic modeling) have been used in STEM education contexts. For our orientation reading lab, we focus on select chapters from Reproducible Research with R and RStudio (Gandrud 2013) and Learning Analytics Goes to School (Krumm, Means, and Bienkowski 2018).

4. Badge. The culminating activity for each is designed to help learners reflect on how the concepts and techniques introduced might apply to their own STEM education research. Scholars are also required to demonstrate their technical proficiency with the analytical techniques and methods addressed in each lab. For our first lab, you'll learn how to convert an R Markdown file into a webpage and publish to Posit Cloud.