Paper Info
Title
Enhancing the efficiency and reliability of group differentiation through partial credit.
Abstract
The focus of the learning analytics community bridges the gap between controlled educational research and data mining. Online learning platforms can be used to conduct randomized controlled trials to assist in the development of interventions that increase learning gains; datasets from such research can act as a treasure trove for inquisitive data miners. The present work employs a data mining approach on randomized controlled trial data from ASSISTments, a popular online learning platform, to assess the benefits of incorporating additional student performance data when attempting to differentiate between two user groups. Through a resampling technique, we show that partial credit, defined as an algorithmic combination of binary correctness, hint usage, and attempt count, can benefit assessment and group differentiation. Partial credit reduces sample sizes required to reliably differentiate between groups that are known to differ by 58%, and reduces sample sizes required to reliably differentiate between less distinct groups by 9%.
Year
DOI
Venue
2016
10.1145/2883851.2883910
LAK '16 CONFERENCE PROCEEDINGS: THE SIXTH INTERNATIONAL LEARNING ANALYTICS & KNOWLEDGE CONFERENCE,
Keywords
Field
DocType
Measurement,Experimentation,Reliability,Partial Credit,Group Differentiation,Resampling,Randomized Controlled Trial,Data Mining
Data science,Online learning,Psychological intervention,Learning analytics,Computer science,Correctness,Randomized controlled trial,Educational research,Resampling,Sample size determination
Conference
Citations 
PageRank 
References 
0
0.34
7
Authors
4
Name
Order
Citations
PageRank
Yan Wang172.57
Korinn Ostrow2206.47
Joseph Beck3303.94
Neil T. Heffernan41087135.49