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Nirmaljpatel (talk | contribs) →A/B Testing: Added details of 3 open platforms for A/B testing in education. |
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[[Neil Heffernan]]’s work with TeacherASSIST includes hint messages from teachers that guide students toward correct answers. Heffernan’s lab runs A/B tests between teachers to determine which type of hints result in the best learning for future questions.<ref>{{Cite web|last1=Thanaporn|first1=Patikorn|last2=Heffernan|first2=Neil|date=|title=Effectiveness of Crowd-Sourcing On-Demand Tutoring from Teachers in Online Learning Platforms|url=https://drive.google.com/file/d/1ZRjjie6mMAUBcR1c2JFWZIJ-mKKvTF4C/view?usp=embed_facebook|url-status=live|archive-url=|archive-date=|access-date=2020-07-21|website=Google Docs}}</ref><ref>{{Cite web|last=|first=|date=|title=Programme|url=https://learningatscale.acm.org/las2020/programme/|url-status=live|archive-url=|archive-date=|access-date=2020-07-21|website=Learning @ Scale 2020|language=en-US}}</ref>
[https://www.upgrade-platform.org/ UpGrade] is an open-source platform for A/B testing in education. It allows EdTech companies to run experiments within their own software. [https://www.etrialstestbed.org/ ETRIALS] leverages ASSISTments and give scientists freedom to run experiments in authentic learning environments. [https://terracotta.education/ Terracotta] is a research platform that supports teachers' and researchers' abilities to easily run experiments in live classes.
=== [[Educational data mining|Educational Data Mining]] ===
Educational Data Mining involves analyzing data from student use of educational software to understand how software can improve learning for all students. Researchers in the field, such as [[Ryan S. Baker|Ryan Baker]] at the University of Pennsylvania, have developed models of student learning, engagement, and affect to relate them to learning outcomes.<ref>{{Cite journal|last1=Fischer|first1=Christian|last2=Pardos|first2=Zachary A.|last3=Baker|first3=Ryan Shaun|last4=Williams|first4=Joseph Jay|last5=Smyth|first5=Padhraic|last6=Yu|first6=Renzhe|last7=Slater|first7=Stefan|last8=Baker|first8=Rachel|last9=Warschauer|first9=Mark|s2cid=219091098|date=2020-03-01|title=Mining Big Data in Education: Affordances and Challenges|journal=Review of Research in Education|language=en|volume=44|issue=1|pages=130–160|doi=10.3102/0091732X20903304|issn=0091-732X}}</ref>
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