Survival Analysis on Duration Data in Intelligent Tutors
نویسندگان
چکیده
Effects such as student dropout and the non-normal distribution of duration data confound the exploration of tutor efficiency, time-intutor vs. tutor performance, in intelligent tutors. We use an accelerated failure time (AFT) model to analyze the effects of using automatically generated hints in Deep Thought, a propositional logic tutor. AFT is a branch of survival analysis, a statistical technique designed for measuring time-to-event data and account for participant attrition. We found that students provided with automatically generated hints were able to complete the tutor in about half the time taken by students who were not provided hints. We compare the results of survival analysis with a standard between-groups mean comparison and show how failing to take student dropout into account could lead to incorrect conclusions. We demonstrate that survival analysis is applicable to duration data collected from intelligent tutors and is particularly useful when a study experiences participant attrition.
منابع مشابه
Opening the Door to Non-programmers: Authoring Intelligent Tutor Behavior by Demonstration
Intelligent tutoring systems are quite difficult and time intensive to develop. In this paper, we describe a method and set of software tools that ease the process of cognitive task analysis and tutor development by allowing the author to demonstrate, instead of programming, the behavior of an intelligent tutor. We focus on the subset of our tools that allow authors to create “Pseudo Tutors” th...
متن کاملData-Driven Feedback Beyond Next-Step Hints
Intelligent tutors have been shown to be as e↵ective as human tutors in supporting learning in many domains. Although they can be very e↵ective, the construction of intelligent tutors can be costly. One way to address this problem is to use previously collected data to generate domain models to provide intelligent feedback to otherwise non-personalized tutors. These data-driven methods for prov...
متن کاملModeling Factors Affecting Unemployment Duration Using Survival Analysis
Abstract. In many statistical studies, we are faced with variables such as duration of unemployment, duration of illness to death, the lifespan of a piece, duration of approval of a bill in the parliament, and other similar cases. In such studies, the aim is to find and model the sources that affect the duration of the study period to control the mentioned period by identifying an...
متن کاملExamining Effective Factors on Duration Time of Recommitment Using Cox's Proportional Hazard Model
Abstract. Recently, in most of scientific studies, the use of survival analysis is performed for examining duration time models. One of the important applications of survival analysis is the study of recommitment crime in criminology which has not yet been considered in Iran. So, with attention to the necessity and importance of predicting recommitment time and the analysis of duration model...
متن کاملA Data Driven Approach to the Discovery of Better Cognitive Models
Cognitive models composed of knowledge components are an integral part of intelligent tutors and drive many of the instructional decisions that these systems make. Most of these models are designed by educators and subject experts. Today vast amounts of data, collected from many intelligent tutors, allow us to analyze and improve the current cognitive models through educational data mining. In ...
متن کامل