Paper Info
Title
The Opportunity Count Model: A Flexible Approach to Modeling Student Performance.
Abstract
Detailed performance data can be exploited to achieve stronger student models when predicting next problem correctness (NPC) within intelligent tutoring systems. However, the availability and importance of these details may differ significantly when considering opportunity count (OC), or the compounded sequence of problems a student experiences within a skill. Inspired by this intuition, the present study introduces the Opportunity Count Model (OCM), a unique approach to student modeling in which separate models are built for differing OCs rather than creating a blanket model that encompasses all OCs. We use Random Forest (RF), which can be used to indicate feature importance, to construct the OCM by considering detailed performance data within tutor log files. Results suggest that OC is significant when modeling student performance and that detailed performance data varies across OCs.
Year
DOI
Venue
2016
10.1145/2876034.2893382
L@S
Keywords
Field
DocType
Opportunity Count, Random Forest, Student Modeling, Next Problem Correctness, Intelligent Tutoring System
TUTOR,Intelligent tutoring system,Computer science,Correctness,Intuition,Artificial intelligence,Random forest,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
4
Authors
4
Name
Order
Citations
PageRank
Yan Wang172.57
Korinn Ostrow2206.47
Seth Adjei3196.02
Neil T. Heffernan41087135.49