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%.