Paper Info
Title
An analysis of the impact of action order on future performance: the fine-grain action model
Abstract
To better model students' learning, user modelling should be able to use the detailed sequence of student actions to model student knowledge, not just their right/wrong scores. Our goal is to analyze the question: \"Does it matter when a hint is used?\". We look at students who use identical attempt counts to get the right answer and look for the impact of help use and action order on future performance. We conclude that students who use hints too early do worse than students who use hints later. However, students who use hints, at times, may perform as well as students who do not use hints. This paper makes a novel contribution showing for the first time that paying attention to the precise sequence of hints and attempts allows better prediction of students' performance, as well as to definitively show that, when we control for the number of attempts and hints, students that attempt problems before asking for hints show higher performance on the next question. This analysis shows that the pattern of hints and attempts, not just their numbers, is important.
Year
DOI
Venue
2015
10.1145/2723576.2723616
LAK
Keywords
Field
DocType
algorithms,prediction of future success,measurement,tabling,reliability,data mining,action order,hint use,performance,binning
Data science,Computer science
Conference
Citations 
PageRank 
References 
3
0.37
7
Authors
4
Name
Order
Citations
PageRank
Eric Van Inwegen1153.07
Seth Adjei2196.02
Yan Wang372.57
Neil T. Heffernan41087135.49