Paper Info
Title
The assessment of learning infrastructure (ALI): the theory, practice, and scalability of automated assessment.
Abstract
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.
Year
DOI
Venue
2016
10.1145/2883851.2883872
LAK
Keywords
Field
DocType
Assessment of Learning Infrastructure,Automated Analysis,Randomized Controlled Experiments at Scale,The ASSISTments TestBed,Universal Data Reporting,Tools for Learning Analytics
Data science,Online learning,Learning analytics,Computer science,Knowledge management,Testbed,Curriculum,Student engagement,Personalized learning,Educational research,Scalability
Conference
Citations 
PageRank 
References 
1
0.36
6
Authors
6
Name
Order
Citations
PageRank
Korinn S. Ostrow151.69
Douglas Selent241.83
Yan Wang372.57
Eric Van Inwegen4153.07
Neil T. Heffernan51087135.49
Joseph Jay Williams613221.73