Paper Info
Title
Improving Student Modeling Through Partial Credit and Problem Difficulty
Abstract
Student modeling within intelligent tutoring systems is a task largely driven by binary models that predict student knowledge or next problem correctness (i.e., Knowledge Tracing (KT)). However, using a binary construct for student assessment often causes researchers to overlook the feedback innate to these platforms. The present study considers a novel method of tabling an algorithmically determined partial credit score and problem difficulty bin for each student's current problem to predict both binary and partial next problem correctness. This study was conducted using log files from ASSISTments, an adaptive mathematics tutor, from the 2012-2013 school year. The dataset consisted of 338,297 problem logs linked to 15,253 unique student identification numbers. Findings suggest that an efficiently tabled model considering partial credit and problem difficulty performs about as well as KT on binary predictions of next problem correctness. This method provides the groundwork for modifying KT in an attempt to optimize student modeling.
Year
DOI
Venue
2015
10.1145/2724660.2724667
L@S
Keywords
Field
DocType
problem difficulty,model development,next problem correctness,partial credit,student modeling,knowledge tracing,tabling method
TUTOR,Student assessment,Computer science,Correctness,Credit score,Artificial intelligence,Machine learning,Tracing,Binary number
Conference
Citations 
PageRank 
References 
5
0.49
6
Authors
4
Name
Order
Citations
PageRank
Korinn Ostrow1206.47
Christopher Donnelly250.83
Seth Adjei3196.02
Neil T. Heffernan41087135.49