Motivation
1. DataShop can be confusing
The PSLC DataShop is a repository of educational data. It serves as the hub for all data collected from the Open Learning Initiative (OLI) and could serve as a great tool for educational researchers, since it provides a vast variety of analytic tools.
However, for instructional designers, it is too complicated to use, in that it is not easy to draw insights in just a few clicks and discover what can be improved.
2. Courses need to be examined on both micro and macro scale
Learning Curve Analysis and KC Model Analysis are the commonly used and powerful tools for analyzing the course on a relatively micro level. They involve student transaction data and student step data. However, a macro level analysis is also needed for the instructor to see a broader picture of the course and gain insights from it.
It is also extremely difficult to gather insights about the way students progress through a course as DataShop data is not easy to parse without code.
3. Different student approaches to online courses are not well understood
Depending on prior domain knowledge, past experience with online learning environments and other factors, students progress through courses in a number of different ways and analyzing data from students movements can allow us to understand the learner profile in more detail.
Main Question
Can we help instructional designers make data driven decisions by visualizing learning pathways through online courses?
we further divide it into several sub-questions, and tried to answer them accordingly:
- What are the common pathways students take in a course?
- How are learning pathways correlated with students achievements?
- How does student behavior (in terms of completion) affect performance?
Pathway Analysis Using D3 Sankey Diagram
hover over a node to see all sources linked to that node. Click on a node to see all the targets linked from the node.
Following the general pattern, there is a clear group of students who follow the recommended path as shown by the thick link. At the top left of the graph it is also easy to see a number of advanced students who skip the basic lessons on for loops and go to time efficiency. Nodes which are small and pushed to the side like concurrency, common errors and logic are also key areas for intervention as not enough students are attempting these modules.
Another interesting insight from this visualization is the idea that making decisions is linked to different previous modules and being a complicated module results in students going back to other modules to recap as shown by the large number of backwards links. The recommended intervention in this case would be to examine the contents of the making decisions module to decided if it requires more scaffolding or whether recap notes from other modules should be directly inserted into this.
Click here to checkout more details and visualizations using Tableau.