Archive: 2020 Learning at Scale Paper on Codeitz

Archive for Codeitz study published to 2020 Learning at Scale

Contacts: Benjamin Xie (bxie@uw.edu), Prof. Amy J. Ko (ajko@uw.edu) University of Washington, Seattle The Information School, DUB Group

Full citation: Xie, Benjamin, Greg L. Nelson, Harshitha Akkaraju, William Kwok, and Amy J. Ko. “The Effect of Informing Agency in Self-Directed Online Learning Environments.” In Proceedings of the Seventh (2020) ACM Conference on Learning @ Scale. L@S 2020. ACM, 2020.

Code and Deployment Instructions

Information on deployment can be found on the Koconut* Wiki. Some development has occurred since this study was conducted, so the Master branch of the code bases may be different.

• Koconut
• Koconut API

*Codeitz was known internally as Koconut

Demo videos

• Youtube: Onboarding for Informed High-Agency (IH)
• Youtube: Onboarding for Uninformed High-Agency (IH)
• Youtube: Onboarding for Informed Low-Agency (IL)

Contents

• analysis/: Code for analysis done. Primary analysis file is analysis.Rmd, and anonymized (columns with potentially identifiable information blanked out) data on participants found as data_anon.csv.
• assessment/: Description of items used in diagnostic/post-test/assessment and their solutions. Please avoid sharing this widely, as these items may be reused in the future.
• concept_exercise_data/: Information on concept and exercise parameters (as CSV), as well as data on exercises and concepts as JSON.
• surveys/: Survey given before (preSurvey.pdf) and after (postSurvey.pdf) use of Codeitz.