TY - GEN
T1 - Seventh Annual Workshop on A/B Testing and Platform-Enabled Learning Engineering
AU - Murphy, April
AU - Fancsali, Stephen E.
AU - Ritter, Steve
AU - Heffernan, Neil
AU - Basu Mallick, Debshila
AU - Roschelle, Jeremy
AU - McNamara, Danielle
AU - Williams, Joseph Jay
AU - Stamper, John
AU - Bier, Norman
AU - Carver, Jeff
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - Learning engineering applies data and learning science principles to better understand outcomes and support improvement research. One important approach is A/B testing–common in large software companies and also represented academically at conferences like the Annual Conference on Digital Experimentation (CODE), and the International Consortium for Innovation and Collaboration in Learning Engineering (IEEE ICICLE). Several systems supporting A/B testing in educational applications are in active use, including UpGrade, E TRIALS, and Terracotta. Although A/B testing can help improve educational platforms and inform learning science research, there are challenging issues unique to conducting such work in these contexts. In response, a number of digital learning platforms have opened their systems to learning-improvement research by instructors and/or third-party researchers, with specific supports necessary for education-specific research designs. This workshop will explore how A/B testing is conducted in educational contexts, how digital learning platforms are accelerating education research, and how empirical approaches can be used to drive powerful gains in student learning. It will also discuss opportunities for funding to conduct platform-enabled learning engineering.
AB - Learning engineering applies data and learning science principles to better understand outcomes and support improvement research. One important approach is A/B testing–common in large software companies and also represented academically at conferences like the Annual Conference on Digital Experimentation (CODE), and the International Consortium for Innovation and Collaboration in Learning Engineering (IEEE ICICLE). Several systems supporting A/B testing in educational applications are in active use, including UpGrade, E TRIALS, and Terracotta. Although A/B testing can help improve educational platforms and inform learning science research, there are challenging issues unique to conducting such work in these contexts. In response, a number of digital learning platforms have opened their systems to learning-improvement research by instructors and/or third-party researchers, with specific supports necessary for education-specific research designs. This workshop will explore how A/B testing is conducted in educational contexts, how digital learning platforms are accelerating education research, and how empirical approaches can be used to drive powerful gains in student learning. It will also discuss opportunities for funding to conduct platform-enabled learning engineering.
KW - A/B Testing
KW - Digital Experimentation
KW - Educational Technology
KW - Learning Engineering
UR - https://www.scopus.com/pages/publications/105043913562
UR - https://www.scopus.com/pages/publications/105043913562#tab=citedBy
U2 - 10.1007/978-3-032-29794-5_14
DO - 10.1007/978-3-032-29794-5_14
M3 - Conference contribution
SN - 9783032297938
T3 - Communications in Computer and Information Science
SP - 80
EP - 83
BT - Artificial Intelligence in Education. Late Breaking Results, WideAIED, Practitioners, Industry and Policies, Blue Sky, Doctoral Consortium, FoL Workshops and Tutorials, FoL Invited Papers - 27th International Conference, AIED 2026, Proceedings
A2 - Blanchard, Emmanuel G.
A2 - Chen, Guanliang
A2 - Chi, Min
A2 - Isotani, Seiji
PB - Springer Science and Business Media Deutschland GmbH
T2 - 27th International Conference on Artificial Intelligence in Education, AIED 2026
Y2 - 27 June 2026 through 3 July 2026
ER -