ERIC Number: ED624039
Record Type: Non-Journal
Publication Date: 2022
Pages: 5
Abstractor: As Provided
ISBN: N/A
ISSN: N/A
EISSN: N/A
Available Date: N/A
"Closing the Loop" in Educational Data Science with an Open Source Architecture for Large-Scale Field Trials
Fancsali, Stephen E.; Murphy, April; Ritter, Steve
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (15th, Durham, United Kingdom, Jul 24-27, 2022)
Ten years after the announcement of the "rise of the super experiment" at Educational Data Mining 2012, challenges to implementing "internet scale" educational experiments often persist for educational technology providers, especially when they seek to test substantive instructional interventions. Studies that deploy and test interventions, when informed by data-driven modeling, are often described as "close the loop" studies. Studies that close the loop attempt to link improvements in statistical and machine learning models of learning to real-world learning outcomes. After first considering challenges to internet scale experiments, we review several educational data science/mining studies that close the loop between data-driven modeling and learning outcomes. Next, we describe UpGrade, an open source architecture that, when integrated with educational technologies, helps overcome challenges to large-scale field trials (or internet scale experiments) that close the loop between data-driven work and real-world learning outcomes. In addition to describing preliminary randomized experiments that have been conducted and will soon be conducted using the architecture in two educational technology platforms, we end with a "call for contributors and integrators." UpGrade contributors and integrators will be researchers and developers who seek to drive continuous, data-driven improvements in real-world settings where learning with technology occurs. [For the full proceedings, see ED623995.]
Descriptors: Learning Analytics, Educational Technology, Barriers, Data Analysis, Teaching Methods, Intervention, Outcomes of Education, Open Source Technology, Internet, Learning Processes, Correlation, Computer Software, Integrated Learning Systems
International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Publication Type: Speeches/Meeting Papers; Reports - Descriptive
Education Level: N/A
Audience: N/A
Language: English
Sponsor: Institute of Education Sciences (ED); National Science Foundation (NSF)
Authoring Institution: N/A
IES Funded: Yes
Grant or Contract Numbers: R305N210045; 1934745
Author Affiliations: N/A