Authors: David Wiley, Robert Bodily
Institution: Lumen Learning
Country: United States
Topic: Technologies for Open Education
Sector: Higher Education
UNESCO Area of Focus: Building capacity
Session Format: Presentation
Abstract
Open educational resources provide their users with permission to revise and remix them by means of open licenses. However, neither OER themselves nor their open licenses provide any guidance about how users should invest their limited time and resources in revise and remix activities. Learning analytics can provide the information necessary to help users decide where revising and remixing efforts can have the greatest impact on student learning.As described in Bodily, Nyland, and Wiley (2017), when OER are used by learners online, activity engagement data and assessment performance data can be analyzed in an integrated manner in order to identify open educational resources that are not sufficiently supporting student mastery. The purpose of Resource Inspection, Selection, and Enhancement (RISE) analysis is to identify learning outcomes where students were highly engaged with aligned OER, but simultaneously performed poorly on aligned assessments. When students are highly engaged with learning resources but still fail to learn sufficiently, we can conclude that the learning resources are ineffective and need to be revised, remixed, and improved.
By grounding decisions about what to revise and remix in empirical data about where OER are failing to support student learning sufficiently, rather than basing these decisions on hunches or intuitions about what we think might need to be changed, we can increase the likelihood that the finite time and effort available to revise and remix OER will result in improved student learning.
OER updated by means of this process can be re-integrated into the existing course materials and used by faculty and students again in the next term. After the term ends the RISE analysis can be run again. This new analysis will either confirm that the changes made to the OER have improved student learning, in which case other less effective OER can be selected for continuous improvement, or it will confirm that there is still work to do to better support student learning of the original topic. This ongoing cycle of continuous improvement, in which OER can be more effective at supporting student learning each term, is one of the most powerful possibilities offered by open educational resources.
Keywords
open educational resources, learning analytics, continuous improvement