Paper Info
Title
What's Most Broken? A Tool to Assist Data-Driven Iterative Improvement of an Intelligent Tutoring System
Abstract
Intelligent Tutoring Systems (ITS) have great potential to change the educational landscape by bringing scientifically tested one-to-one tutoring to remote and under-served areas. However, effective ITSs are too complex to perfect. Instead, a practical guiding principle for ITS development and improvement is to fix what's most broken. In this paper we present SPOT (Statistical Probe of Tutoring): a tool that mines data logged by an Intelligent Tutoring System to identify the 'hot spots' most detrimental to its efficiency and effectiveness in terms of its software reliability, usability, task difficulty, student engagement, and other criteria. SPOT uses heuristics and machine learning to discover, characterize, and prioritize such hot spots in order to focus ITS refinement on what matters most. We applied SPOT to data logged by RoboTutor, an ITS that teaches children basic reading, writing and arithmetic.
Year
DOI
Venue
2019
10.1609/aaai.v33i01.33019941
AAAI
Field
DocType
Volume
Data-driven,Intelligent tutoring system,Software engineering,Computer science,Artificial intelligence,Machine learning
Conference
33
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Mononito Goswami112.99
Shiven Mian200.34
Jack Mostow31133263.51