Foundation knowledge is the base knowledge of which new knowledge is built. Learning Analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs (Siemens, 2011).
These foundation labs allow for the gentle introduction of conceptual understanding of foundation of learning analytics along with R programming basics for STEM Education Research.
Required Pre-Reading:
Foundation Presentation - One and code-along: This presentation is a review of the types of data typically used to perform learning analytics in education. The focus of the essential readings are to introduce LASER Scholars to the most common data structures used in Learning Analytics. We will look closely at Digital Learning Environments, Administrative Data and Sensors / Multimodal.
TYPES:
- Digital Learning Environments
- Games and Simulations
- MOOCs
- Intelligent Tutoring Systems
- Learning Management Systems
CHARACTERISTIC of DATA
- Structured
- Unstructured
- Semi-Structured
- Meta-Data
The code-along includes introduction of a script file and commonly used packages that read in different data types. Scholars learn to use the readr package from tidyverse to organize data into data frames and tibbles. Additionally, scholars will learn how to read in files using the Haven package.
- Reading in Data
- Packages
- Common Functions
Required Work:
- Make sure to complete the R Programming primers: Work with Data)
Badge Requirement
- Complete the Badge requirement documentFoundations badge - Data Sources.
Written by Jeanne McClure, Catherine Noonan, and Shaun Kellogg. Presented by Jeanne McClure and Jenn Houchins at the Learning Analytics in STEM Education Research (LASER) workshop, July 11, 2022, through July 15, 2022, at the Friday Institute, North Carolina State University.
Required Pre-Reading:
Foundation Presentation - two and code-along: Learning Analytics. The focus of the essential reading dives deep into the Learning Analytics workflow.
LEARNING ANALYTICS WORKFLOW
- Prepare
- Wrangle
- Explore
- Model
- Communication
The accompanying code-along introduces R Markdown and Markdown syntax, as well as the YAML header. Participants will practice preparing and wrangling data, including reading in and tidying data. PHASE oF WORKFLOW
-
Prepare
- How to read in Packages
- Tidyverse Package
- How to read in Packages
-
Wrangle
- Read in Data
- Import
- Tidy
- Join
Required Work:
- Make sure to complete the R Programming primers: Tidy your Data
Badge Requirement
- Complete the badge requirement document from your lab 2 folder foudationlab2_badge - Data Sources.
Written by Jeanne McClure, Catherine Noonan and Shaun Kellogg. Presented by Jeanne McClure and Jenn Houchins at the Learning Analytics in STEM Education Research (LASER) workshop, July 11, 2022, through July 15, 2022, at the Friday Institute, North Carolina State University.
Required Pre-Reading:
-
Data Visualization: A practical Introduction (CH. 1 & 3) by Kieren Healy(Feel free to skim)
-
R for Data Science. (CH. 3) by Hadley Wickham & Garrett Grolemund
Foundations Presentation - Threeand code-along: The overview introduces and reviews some of the basic principles of data visualization as it relates to data graphics, including data visualization perception and color.
DATA VISUALIZATION
- Purpose of Visualizations
- Principles
- Perception
- Color
- Cognitive Processing
The accompanying code-along takes a deep dive into the ggplot2 grammar in a simple-to-understand layering approach. We will look at a representation of numeric variables using some of the most popular geoms, histogram and scatter plot, and put it all together to answer a research question. At the end of this code-along participants will understand the "hows" of ggplots aesthetics.
PHASE OF WORKFLOW
- EXPLORE
ggplot2grammar- Scatter plot
- Histogram
Required Work:
- Make sure to complete the R Programming primer: Introduction to data visualization)
Badge Requirement
- Complete the badge requirement document from your lab 3 folder foundationlab3_badge- Data Visualization.
Written by Jeanne McClure, and Shaun Kellogg. Presented by Jeanne McClure and Jenn Houchins at the Learning Analytics in STEM Education Research (LASER) workshop, July 11, 2022, through July 15, 2022, at the Friday Institute, North Carolina State University.
Required Pre-Reading:
1 & 2. R for Data Science. (CH. 22 & 23) by Hadley Wickham & Garrett Grolemund
Foundation Presentation - four and code-along:
This presentation will cover the essentials of crafting a data product for different stakeholders.
- Data storytelling
- narrative elements,
- methods for improving stakeholder understanding and facilitating resolution or call-to-action.
The code-along will focus on using R Markdown to create reports in a variety of formats and will introduce formatting for bibliographies and in-text citations for scholarly publications.
PHASE OF WORKFLOW
-
Model
- Correlation Matrix
- APA Formatted Table
- Linear Regression
- APA Formatted Table
- Summarize
- Correlation Matrix
-
Communicate
- Select
- Polish
- Narrate
Required Work:
- Make sure to complete the R Programming primer: R Markdown
Badge Requirement
- Complete the badge requirement document from your lab 4 folder foudationlab4_badge - Data Products.
Written by Catherine Noonan, Jeanne McClure, and Shaun Kellogg. Presented by Jeanne McClure and Jenn Houchins at the Learning Analytics in STEM Education Research (LASER) workshop, July 11, 2022, through July 15, 2022, at the Friday Institute, North Carolina State University.
THANK YOU!!