Teaching & Learning data sets are:
- Learning Analytics Data Architecture (LARC)
- Student Explorer
- Canvas Data/Unizin Data Warehouse (UDW)
- Unizin Data Platform (UDP)
- Online Learning Datawarehouse (OLDW)
- Coursera Spark
- Coursera Phoenix Redshift
Learning Analytics Data Architecture (LARC)
Contents
Student data describing biographic and demographic information, historical information about the student's progress and interests during each enrollment term in which they were registered, and academic information about the classes they took while enrolled. LARC is designed for research use and aggregates information from numerous content areas. LARC data is available as a traditional data set in a database, and also is available for download as a set of flat files.
Availability
Refer to the Refresh Schedule for specific refresh times.
Resources
- LARC Data Dictionary
- LARC Dataset Discussion Forum (for approved LARC users only)
- LARC Mock Dataset (simulated dataset containing fictional data)
Student Explorer
Contents
Student data, collected from Learning Management Systems (e.g. Canvas), that enables mentors to see grades in all classes for students who are in the mentor's cohort. Grades and related categorizations allow early warning systems to identify at-risk students who may benefit from intervention. Grades and categorizations are used in the Student Explorer application.
Availability
Refer to the Refresh Schedule for specific refresh times.
Resources
Student Explorer Data Dictionary
Canvas Data/Unizin Data Warehouse (UDW)
Contents
Canvas learning management system data, including term, course, enrollment, and activity data. UDW data is designed for optimized access to Canvas data for reporting and queries.
Availability
Refer to the Refresh Schedule for specific refresh times.
Resources
Unizin Data Platform (UDP)
Contents
The UDP includes learning analytics data from the university's Student Information System, Canvas data, and other learning tools.
Availability
Refer to the Refresh Schedule for specific refresh times.
Resources
UDP Teaching & Learning Dataset
UDP Training(U-M VPN is required)
Online Learning Datawarehouse (OLDW)
Contents
The Online Learning Data Warehouse is a single source for engaging with cross-platform MOOC enrollment and completion. Data has been pulled from the other standalone datasets and transformed into a common structure. The Online Learning Data Warehouse currently houses courses, course enrollment, and course completion data on the U-M open learning experiences on Coursera and edX platforms, while in-course engagement data can be accessed through the platform-specific datasets.
Availability
The data set is generally available noon Sunday–11 p.m. Saturday, except when the data is being refreshed. Refer to the Refresh Schedule for specific refresh times.
Resources
Coursera Spark
Contents
Coursera and U-M first launched a partnership to provide Massive Open Online Courses (MOOCs) globally in 2012. Learning experiences launched between 2012 and 2016 are recorded in the Coursera Spark dataset. The widely publicized launch of MOOCs yielded millions of enrollments in these first courses and the data continues to provide a remarkable lens on the advent of the global MOOC experiment.
Availability
The data set is generally available noon Sunday–11 p.m. Saturday, except when the data is being refreshed. Refer to the Refresh Schedule for specific refresh times.
Coursera Phoenix Redshift
Contents
The higher education community experimented with and iterated MOOCs over their first four years of life. In 2016, changes in design practices were matched by Coursera transitioning from their Spark technology to a new Phoenix platform, complete with mobile-friendly features and a new data structure. The Coursera Phoenix dataset includes all U-M MOOCs launched since 2016, their structure and design, and learner engagement and completion.
Availability
The data set is generally available noon Sunday–11 p.m. Saturday, except when the data is being refreshed. Refer to the Refresh Schedule for specific refresh times.