OpenEd20

This video will breifly review a case-study on integration of quantifiable student perception data into a continuous improvement process driven by learning analytics. The work builds upon the RISE (Resource Inspection, Selection, and Enhancement) Framework to evaluate and improve open content within an introductory physics course, including a web-based textbook, hand-on labs, in-person practice exercises, and interactive self-paced practice questions. The alignment of content identified for improvement by statistical analysis and students’ perceptions are previewed. The methods, tools, and workflow used to integrate student perceptions into the identification, prioritization, inspection and improvement of content is described and example improvement actions from the case-study are proposed. Contact the speaker for information on how to download and implement the open-source software tools used to support his work will be provided or for further discussion the methods and results.

Learning Outcomes: 1) Explain how student perceptions of content can integrated into the quadrant analysis and then used to inform content improvement
2) Be aware of a new software package (in python) that allows users to perform the quadrant analysis with optional student perception features
3) Consider the preparation required to apply the quadrant analysis in your own course and identify possible barriers to completing those steps