The Assessment of Learning Infrastructure (ALI): The Theory, Practice, and Scalability of Automated Assessment

Type: Evidence | Proposition: C: Uptake | Polarity: | Sector: | Country:

Researchers invested in K-12 education struggle not just to enhance pedagogy, curriculum, and student engagement, but also to harness the power of technology in ways that will optimize learning. Online learning platforms offer a powerful environment for educational research at scale. The present work details the creation of an automated system designed to provide researchers with insights regarding data logged from randomized controlled experiments conducted within the ASSISTments TestBed. The Assessment of Learning Infrastructure (ALI) builds upon existing technologies to foster a symbiotic relationship beneficial to students, researchers, the platform and its content, and the learning analytics community. ALI is a sophisticated automated reporting system that provides an overview of sample distributions and basic analyses for researchers to consider when assessing their data. ALI's benefits can also be felt at scale through analyses that crosscut multiple studies to drive iterative platform improvements while promoting personalized learning.

Citation: Korinn Ostrow, Doug Selent, Yan Wang, Eric Van Inwegen, Neil Heffernan and Joseph Jay Williams (2016). "The Assessment of Learning Infrastructure (ALI): The Theory, Practice, and Scalability of Automated Assessment". In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (LAK '16). ACM, New York.